Experimental design

Comments

Transcription

Experimental design
Experimental design
and modelling
Photo on the front: installation of the EVI beacons on the A12 motorway
Experimental design and modelling
Spitsmijden
Jasper Knockaert (ed.), Michiel Bliemer, Dick Ettema, Dusica Joksimovic,
Albert Mulder, Jan Rouwendal, Dirk van Amelsfort
1 INTRODUCTION
The Dutch Spitsmijden1 project was set up to study the feasibility of a reward
scheme to encourage commuters not to drive during the morning rush-hour. The
project comprises two stages.
Stage 1 – the reward trial reported on here – comprised a behavioural analysis, technical
and organizational implementation, welfare optimization and traffic simulation. The
trial was carried out by a public-private partnership comprising universities, private
companies and public institutions. The geographical focus was on the heavily congested
Dutch A12 motorway link from Zoetermeer towards The Hague. An experiment involving
340 regular rush-hour commuters was conducted in order to obtain revealed preference
observations for a behavioural analysis. This was complemented by several surveys
(including a stated preference survey), which extended the scope of the dataset.
The behavioural analysis led to the establishment of a number of parameters.
These were fed into simulation models that had been developed as part of the
project. One model is based on economic welfare theory and was used to determine the optimal reward level. The second model is a dynamic traffic model that
allowed the simulation of different reward levels and an assessment of the global
impact of the corresponding reward schemes.
Three reports were published after the conclusion of stage 1:
• Effects of reward. This report (which is in Dutch) describes the most relevant
results of the trial. A summary of this report is also available in English.
• Lessons learned. This report contains the lessons learned by the various individuals and
organizations directly involved in the execution of the trial. (Available only in Dutch).
• Experimental design and modelling. This report provides an extended background description of the different technical and scientific aspects of the first
phase of the project. (Available only in English).
Stage 1 focused on preliminary behavioural analyses and the development of simulation tools. The results are presented in this report. Section 3 details the set-up
of the reward trial and the corresponding behavioural analysis. Section 4 briefly
describes the different surveys that were carried out. Sections 5 and 6 discuss
traffic data and the design of the dynamic traffic model. The simulation results
are presented for two scenarios in order to illustrate the model dynamics. Section
7 describes the design and calibration of the economic queuing model. Section 8
concludes this report. The research questions from the various surveys are listed in
the appendices.
A plan of approach has been drawn up for stage 2 of the project. In stage 1, the
main focus was on conducting the trial and designing the simulation modelling
framework, while in stage 2 the bulk of the behavioural analysis and the refinement of the simulation tools will be carried out. Further, a new trial may be
performed in stage 2.
We have also performed a transition study to find out how such projects as
Spitsmijden might contribute towards a transition to sustainable mobility.
1
‘Spitsmijden’ can be roughly translated as ‘avoiding rush hour’.
Spitsmijden | Experimental design and modelling
3
2 CONTENTS
1
2
3
3.1
3.2
3.3
3.4
3.5
3.6
4
5
5.1
5.2
5.3
6
6.1
6.2
6.3
6.4
7
7.1
7.2
7.3
7.4
7.5
8
9
introduction ........................................................................................................................................................................................................................................... 3
contents ........................................................................................................................................................................................................................................................... 5
the spitsmijden reward trial .................................................................................................................................................................................... 6
Overview ............................................................................................................................................................................................................................................................ 6
Reward management ................................................................................................................................................................................................................ 10
Technique used in the trial ................................................................................................................................................................................................ 19
Quality control of data and processes ........................................................................................................................................................... 31
Special traffic circumstances ......................................................................................................................................................................................... 31
Analysis .............................................................................................................................................................................................................................................................33
surveys .............................................................................................................................................................................................................................................................47
network, travel and traffic data ................................................................................................................................................................49
Network infrastructure description ...................................................................................................................................................................49
Travel demand ...................................................................................................................................................................................................................................... 50
Traffic data................................................................................................................................................................................................................................................... 51
analyses with the indy traffic model .................................................................................................................................................. 54
Model description ........................................................................................................................................................................................................................... 54
Model estimation and calibration ........................................................................................................................................................................58
Case studies ............................................................................................................................................................................................................................................... 62
Model results ...........................................................................................................................................................................................................................................63
economic queuing model ..............................................................................................................................................................................................68
Introduction...............................................................................................................................................................................................................................................68
The bottleneck model ...............................................................................................................................................................................................................68
The data and the model .........................................................................................................................................................................................................70
Application of the model ..................................................................................................................................................................................................... 74
Concluding remarks ...................................................................................................................................................................................................................... 76
conclusions ............................................................................................................................................................................................................................................78
bibliography ..........................................................................................................................................................................................................................................79
appendices ..................................................................................................................................................................................................................................................82
Spitsmijden | Experimental design and modelling
5
3 THE SPITSMIJDEN REWARD TRIAL
3.1 Overview
This section discusses the organization of the Spitsmijden reward trial, including
its location and timing, the rules of the trial, and the recruitment and characteristics of the participants.
Location and timing
The trial was launched on 2 October 2006. The test area was the Dutch A12
motorway corridor from Zoetermeer towards The Hague. On weekday mornings,
this stretch of motorway is heavily congested with vehicles heading towards The
Hague. There are few alternative routes or on- or off-ramps on this stretch of
motorway, which made the trial relatively easy to control.
The morning rush-hour was defined as lasting from 07.30 to 09.30h, since this period
has the highest traffic densities. The participants in the trial could earn a reward for
not travelling by car from Zoetermeer to The Hague during the morning rush hour.
Upon registration, the participants chose one of two types of reward. The first type of reward was an amount of money for each morning rush hour that the participant avoided.
The second type comprised savings towards keeping the Yeti smartphone at the end of
the trial. An extended specification of both reward types is provided in section 3.2.
Rules
The following were the main rules of the trial:
• The participants were to commute at least three times per week from Zoetermeer towards The Hague.
• They were to have access to e-mail and the Internet.
• They were to complete questionnaires and travel logs completely and timely.
• Their participation had to be voluntary (although they were required to sign a
contract listing the rights and duties of both parties).
• They would receive a reward only for the number of times they avoided the morning rush-hour by travelling outside the rush hour period, using another mode
of transport or working at home. The frequency of rush hour avoidance was
determined relative to each participant’s usual commuting behaviour (see § 3.2;
Rewards classes).
• The participants who were participating in the Yeti variant had to switch on the
Yeti smartphone during each car trip.
• The participants would use the car in which an On Board Unit (OBU) had been installed.
Recruitment
of participants
The objective was to recruit 500 participants. To this end, we organized three
recruitment waves:
Recruitment in April 2006
Licence plate recognition cameras were used to select 2,300 vehicles that travelled from
Zoetermeer to The Hague at least three times per week. The Department of Road Transport (RDW) provided the names and addresses of the car owners. These people were
approached by mail on 15 April 2006 with an invitation to participate in the trial. The
sample of 2,300 cars comprised private vehicles as well as leased and company cars.
Recruitment in June 2006
Using the same method, a second group of car owners were approached on 16
June 2006. All these cars were private vehicles.
6
Experimental design and modelling | Spitsmijden
Recruitment in July and August 2006
The first two waves of invitations resulted in 283 participants. To increase this
number, two further actions were launched, namely:
• Recruitment according to a ‘member get member’ approach. Participants could
register new participants via the Internet.
• Vehicle owners from the April and June waves who had not responded were approached again.
In the end, 340 commuters participated in the trial.
Participants
Upon recruitment, the participants completed the first questionnaire about their
daily commute (see D1 in Chapter 4), as well as a second one concerning their sociodemographic characteristics and organization of work and household (D2). After
the trial, they filled out a third questionnaire regarding why they had participated
and the experiences they had had with participation (D5). The following subsections provide a description of the participants based on these data collections.
Of the participants, 64.7% were male. About half of all participants were aged between 35
and 49 (see Figure 3.1). About 25% were younger than 35, while 25% were older than 49.
Figure 3.1: Participants’ ages
leeftijdsverdeling deelnemers
<25 years
>49 years
leeftijdsverdeling deelnemers
25-34 years
<25 jaar
25-34 jaar
35-49 jaar
>49 jaar
<25 jaar
25-34 jaar
35-49 jaar
35-49 years
>49 jaar
The majority of the participants held a higher professional education certificate or
a university
degree. deelnemers
opleidingsniveau
Figure 3.2: opleidingsniveau
Participants’ deelnemers
education levels
VMBO / HAVO
LBO
VMBO/HAVO
VMBO/HAVO
Pre-vocational secondary
LBO
education / senior general
MBO
HBO /
WO
MBO
HBO/WO
VMBO/HAVO LBO
LBO
MBO
HBO/WO
secondary education
Lower vocational education
Higher professional education /
university education
HBO/WO
MBO
Upper secondary vocational
education
Deelnemers naar huishoudenssamenstelling
Alleenstaand
Deelnemers naar huishoudenssamenstelling
Getrouwd/samenwonenn
zonder kinderen
Getrouwd/samenwonend
Alleenstaand
met kinderen
Spitsmijden | Experimental design and modelling
Alleenstaande
ouder
Getrouwd/samenwonenn
zonder kinderen
Overig
Getrouwd/samenwonend
met kinderen
Alleenstaande ouder
7
Most of the participants were married or cohabiting; most had children (see Figure 3.3).
Deelnemers naar huishoudenssamenstelling
Figure 3.3: Participants’ household composition
5% 2%
13%
56%
Alleenstaand
Getrouwd/samenwonenn
zonder kinderen
24%
13%
Single
56%
Living with partner with children
Getrouwd/samenwonend
24% Living with partner without children
met kinderen
Alleenstaande ouder
5%
Single parent
2% Other
Overig
Of the participants, 98% lived in Zoetermeer; the rest lived in the surrounding municipalities (e.g. Benthuizen, Berkel en Rodenrijs, Bleiswijk; see Table 3.1). Most of
the participants worked in The Hague, although some worked in Delft, Leidschendam, Rijswijk or Voorburg.
Frequentie woon-werk rit
Table 3.1: Participants’ work location
Work location
The Hague
Delft
Leidschendam
Rijswijk
Voorburg
Other
Total
Motivations
for participation
Number
192
29
10
42
10
57
340
Percentage
56%
9%
3%
12%
3%
17%
100%
3 keer/week
4 keer/week
5 of meer keer/week
The motivations for participation are presented in Table 3.2. The results suggest
that the reward was the most frequent reason for participation, although the
majority of the participants also had another motivation. Both the contribution to
more insight into congestion and experimentation with alternative travel options
were relevant motivations.
Table 3.2: Participants’ motives
Motive
Reward (money or Yeti)
8
Number
138
Percentage
37.9%
Contribute to understanding of road use
during rush-hour
59
16.2%
Contribute to reducing traffic problems
66
18.1%
Experimenting with possibilities of adapting
own behaviour
77
21.2%
Gaining experience with the Yeti smartphone
and the use of traffic information
9
2.5%
Other
15
4.1%
Experimental design and modelling | Spitsmijden
Overig
The daily commute
Of the participants, 62% commuted at least five times per week towards The Hague,
using the A12 motorway; 26% commuted four times per week (Figure 3.4).
Frequentie woon-werk rit
Figure 3.4: Weekly commuting frequency
12%
62%
3 keer/week
26%
4 keer/week
5 of3meer
keer/week
12%
x
26%
4x
62%
5 x or more
84% of the participants commuted only by car, including car trips both during
and outside the rush-hour. The other 16% combined using their car with using a
Park&Ride scheme (6.4% of the participants), motorbike (2.4%), train (4.7%), bus
(1.8%) or bicycle (6.2%).
34% said that they regard public transport as a serious travel option, while 18%
regarded cycling as an option.
The average commute time (including congestion) was 36 minutes. The average reported
free-flow time was 20 minutes, implying an average delay due to congestion of 16 minutes.
The vast majority (90%) of the participants usually arrived at their workplace between
07.30 and 09.30h (the morning rush-hour period as defined in this study) (Table 3.3).
Table 3.3: Start of working day
Start of working day
Before 06.30h
06.30-07.30h
07.30-08.30h
08.30-09.30h
After 09.30h
Unknown
Total
Organization of
work and household
Number
6
15
192
115
11
1
340
Percentage
2%
4%
56%
34%
3%
0%
100%
Fifty-seven per cent of the participants were allowed to start work later than the
usual start time (Table 3.4). Mostly, a delay of up to 60 minutes was allowed.
Table 3.4: Possibilities to start work later
Possibilities to start work later
Number
I cannot start work later
146
I can start work at most 30 minutes later
43
I can start work 30-60 minutes later
86
I can start work 60-120 minutes later
54
I can start work more than 120 minutes later
11
Total
340
Percentage
43%
13%
25%
16%
3%
100%
Spitsmijden | Experimental design and modelling
9
The majority (79%) of the participants could start work right away if they arrived early at
their workplace (Table 3.5); 9% could start preparations. An early departure from home
in order to avoid the rush-hour was therefore an option for many of the participants.
Table 3.5: Situation when arriving at work early
Situation
I can begin work immediately
Number
268
Percentage
79%
I cannot begin work, but I can begin making
preparations for work
32
9%
I must wait to begin work at a certain time (e.g. shift work)
11
3%
I have to wait for colleagues before I can start work
7
2%
I cannot enter the building / office
8
2%
14
4%
340
100%
Other
Total
In addition to work flexibility, the flexibility of household activities played a role in
the participants’ responses. In total, 54% faced constraints stemming from household obligations that prevented them from making an early or a late departure
from home. Table 3.6 suggests that especially child care and dropping off children
at school prohibited an early or a late departure.
Table 3.6: Limitations on departure time to go to work
Limitation
Child care
Breakfast with family
Bring children to school
Drop partner off
Carpool appointments
Other appointments
Other
None
Percentage
29.4%
9.7%
19.4%
5.9%
2.4%
10.9%
8.2%
46.2%
Use of travel
information
41% of the participants said that they use road traffic information at least once a
week. This percentage is the same for participants who chose the financial reward
(40%) and for those who chose the Yeti smartphone (43%). Only 3% used traffic
information regarding public transport once a week or more.
3.2 Reward management
This section describes how the reward management framework was designed and
implemented for the trial. We first discuss the two reward types, namely money and
a Yeti smartphone. We then describe how different variations of each type were
alternated in order to measure the behavioural response to different levels of the
stimulous. Subsequently, we detail the assignment of the participants to reward
classes, which reflected their travelling frequency under undisturbed circumstances.
Finally, we discus the actual implementation of the reward management scheme
and provide a brief note on how feedback to the participants was organized.
10
Experimental design and modelling | Spitsmijden
Reward type
Upon registration the participants were asked which type of reward they would
prefer. There were two options. The first type of reward was an amount of money
for each morning rush hour that the participant avoided. At the moment of registration the premium was indicated to amount to about € 5.
The second type of reward was saving for a Yeti smartphone. These participants
received a Yeti smartphone at the beginning of the trial. The Yeti provided them
with traffic information during the trial. If the number of avoided car trips during
the morning rush hour exceeded a stated number, the participant would be allowed to keep the Yeti at the end of the trial. If the participant failed to meet the
threshold, he / she would have to return the smartphone at the end of the trial.
Thus, it was an all-or-nothing scenario.
The majority of the participants chose a monetary reward. As the trial was set up
to test both reward types, the remainder of the participants (including those who
had said that they did not have a preference for one reward type over the other)
were assigned to the Yeti variant. However, to prevent participants ending up with
an unwanted and hence undervalued reward type, we allowed them to switch to
the other type until the start of the trial.
The final division of participants over reward type is provided in Table 3.7.
Table 3.7: Classification of participants over reward types2
Reward type
Monetary reward
Yeti smartphone
Reward scheme
Number
232
108
In order to increase the amount of information regarding rush hour travel behaviour collected during the trial, the level of the reward proposed to each participant
was varied over time.
Money
After installation of the measurement equipment (an OBU; see § 3.3; EVI system)
in all participants’ cars, a reward-free period of two weeks was scheduled. The
participants were told that these would be ‘test weeks’ in order to check that all
the equipment worked as expected. They were also told that they should complete
their daily logbook (see § 3.3; Website and logbook) on the website, in order to test
the full set-up of the trial.
However, the real purpose of the test weeks (apart from technical testing) was to
collect information on travel behaviour when no reward was provided. This served
as a reference situation both for the behavioural analysis (§ 3.6) and for the design
of the reward management (see§ 3.2; Reward classes ). The former motivation is
the main reason the participants had to complete their logbook during the test
weeks, the latter argument is why we did not tell the participants what we were
actually measuring, in order to avoid any bias in reference (unrewarded) behaviour.
2
It should be noted that the number of participants may vary over different sections of this report,
depending on the selection of participants used in a particular section: for some participants, only partial
information was collected and they may or may not have been included in selected tables or figures.
Spitsmijden | Experimental design and modelling
11
It should be noted that we did observe some change in behaviour in the test weeks.
This was measured by comparing observations for the test weeks with the weeks before for those cars that had been equipped at the beginning of the installation period
(which took two weeks). Feedback from the participants also indicated that some of
them changed their behaviour during the test weeks. The main argument put forward
by these participants was that they perceived the test week message as a call to see
if they could change their behaviour. Also some participants made arrangements
at their workplace before the trial. It is not possible to know how large this share of
participants was, but it is our impression that it was small.
For the reasons mentioned above, we decided to add another reward-free week at
the end of the trial during which the participants would have to provide logbook
information. This time we told the participants that they were expected to behave as
they would normally behave without a reward. It was stressed that their rush-hour
travel behaviour would in no way affect their reward. Nevertheless, we again got
some feedback that some participants continued to avoid the rush-hour, but now
the motivation was that since they had learned how to avoid it (motivated by the
reward), the new situation pleased them more than they had expected and they had
decided to continue their new behaviour even in the absence of a reward.
During the ten-week period between the reward-free weeks, the participants could
obtain a reward by avoiding the rush-hour. In order to maximize the behavioural
information, three reward levels were tested for:
• € 3 reward for avoiding the 07.30 – 09.30h period for three weeks;
• € 7 reward for avoiding the 07.30 – 09.30h period for four weeks;
• € 3 reward for avoiding the 08.00 – 09.00h period, increased to € 7 if the full rushhour (07.30 – 09.30h) was avoided, for three weeks.
The definition of the period 07.30 – 09.30h as the morning rush-hour3 was based on
observations on the stretch of the A12 motorway extending from Zoetermeer to The
Hague (see § 3.1; Location and timing).
Although all the participants dealt with all three reward levels for the same number of
weeks, the order of the three variants was shifted in order to compensate in the analysis for any order-related bias. Table 3.8 illustrates the resulting six reward schemes:
• R1: no reward;
• R2: € 3 / day;
• R3: € 7 / day;
• R4: € 3 – € 7 / day.
Table 3.8: Reward schemes for monetary reward
Scheme
1
2
3
4
5
6
3
12
Week
1
2
3
4
5
6
7
8
9
10
11
12
13
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R3
R3
R2
R2
R4
R4
R3
R3
R2
R2
R4
R4
R3
R3
R2
R2
R4
R4
R3
R3
R3
R4
R3
R2
R2
R4
R3
R4
R3
R2
R2
R4
R3
R4
R3
R2
R2
R4
R3
R3
R3
R3
R4
R2
R4
R3
R2
R3
R4
R2
R4
R3
R2
R3
R4
R2
R4
R3
R2
R3
R1
R1
R1
R1
R1
R1
Local time is used throughout this report.
Experimental design and modelling | Spitsmijden
The participants were divided over the six reward schemes such that all schemes
had approximately the same number of participants. Special attention was paid to
participants living in the same household: they were assigned to the same scheme
in order to avoid behavioural complications, such as the switching of cars within
the household in order to increase the reward.
YETI
The Yeti participants underwent a similar scheme of two reward-free weeks plus
one reward-free week at the beginning and at the end of the trial, respectively.
During the first two weeks, the participants did not have a Yeti, whereas in the last
(reward-free) week of the trial they did have one.
For the Yeti participants, the aim was not only to measure the impact of a reward but also
to test for the impact of traffic information. We therefore decided to increase the number
of reward-free weeks. This led to two reward levels during the ten-week period, namely:
• For a period of five weeks: avoiding enough rush-hours in order to be allowed to
retain the Yeti and traffic information;
• For another period of five weeks: only receiving traffic information.
The resulting schemes are illustrated in Table 3.9:
• R1: no traffic information, no reward;
• R2: traffic information, reward;
• R3: traffic information, no reward.
Table 3.9: Reward schemes for Yeti reward
Scheme
1
2
Week
1
2
3
4
5
6
7
8
9
10
11
12
13
R1
R1
R1
R1
R2
R3
R2
R3
R2
R3
R2
R3
R2
R3
R3
R2
R3
R2
R3
R2
R3
R2
R3
R2
R3
R3
The participants were assigned evenly to both schemes. Again, participants living
in the same household were assigned to the same scheme.
Thus, the impact of the reward could be measured by comparing the behaviour
during the five reward weeks to that during the eight reward-free weeks. As for
the impact of traffic information, the behaviour during the two reward-free weeks
could be compared with the other weeks (traffic information had been provided at
all times to the participants who had a Yeti smartphone), compensating of course
for the impact of the reward.
Communication
The customized reward schemes were communicated to the participants via their
personalized webpages, which were part of the Spitsmijden website (see § 3.3; Website
and logbook). We drew attention to the availability of this personalized information in
an issue of the weekly newsletter that we sent to the participants.
Reward classes
The way the participants were recruited meant that not all participants commuted
from Zoetermeer towards The Hague every day during the morning rush-hour (see
§ 3.1; Rules). It would be unfair to reward part-time travellers for every day that they
Spitsmijden | Experimental design and modelling
13
avoided the rush-hour, considering that even under their unrewarded behaviour they
would not travel during rush-hour on some days.
We therefore designed a procedure to define the reference (unrewarded) travel behaviour of each participant. It should be noted, however, that this customization was not
motivated by analytical arguments: to allow for the behavioural analysis it did not matter that participants received a reward for every day that they avoided the rush-hour.4
Reference travel behaviour
The reference (i.e. unrewarded) travel behaviour was based on the observations carried
out during the two reward-free weeks at the beginning of the trial. Observations on
Monday through Friday were taken into consideration. Days on which the participant
indicated in the logbook that he / she was ill or on holiday were eliminated from the
observations, as observations on these days did not reflect regular travel behaviour.
For each respondent and each observation day we first checked the automated
car observations in the 06.00 – 11.00h period.5 If a movement was registered, we
checked whether there was an observation in the 07.25 – 09.35h period.6 If this
was again the case, we decided that the participant had travelled during the morning rush-hour on that day.
If no car registration was available for the 06.00 – 11.00h period, we checked the
corresponding logbook entry for that participant (if available), because a technical
failure might have resulted in missing observations.7 If the participant indicated
in the logbook that he / she had travelled in the 07.30 – 09.30h period in his / her
own car or in another car, or somebody else had travelled in his / her car, we included the day in the morning rush-hour trip count.
A reference travel behaviour indicator was defined as the ratio of the number of
morning rush-hour trips to the number of observation days, corrected for holidays
and illness.
The procedure took into account only observations of the two reward-free weeks
at the start of the trial. We do, however, have some information concerning the
weeks before for some participants. Based on these automated observations we
recalculated the travel frequency in a similar way, however without compensating
for holidays or illness, considering that no logbook information was available for
this period. Also the fact that most cars were not equipped with registration technology for the full period of these two weeks was not taken into account.
The final travel frequency indicator was defined as the maximum value of the indicators
for both periods. This value was then used to assign the participants to reward classes.
Definition of reward classes
Four reward classes were defined to which the participants were assigned based
on their value on the travel frequency indicator (see Table 3.10). Based on the
reward class, a correction was applied to the reward scheme in order to take into
4
5
6
7
Modest lump sum compensation is not expected to impact travel behaviour significantly.
We only consider observations of OBU-equipped cars.
For the automated observations we defined a tolerance of 5 minutes.
Note that it is in the interest of the participant that we overestimate rather than underestimate the
reference travel frequency.
14
Experimental design and modelling | Spitsmijden
account reference (i.e. unrewarded) travel behaviour during the morning rush-hour.
Table 3.10: Reward classes and their impact on reward level
Reward
class
A
B
C
D
Reference rush-hour
travel frequency
[3.5,5.0]
[2.5 ,3.5[
[1.0,2.5[
[0.0,1.0[
Monetary reward:
max. number
of rewards (per week)
5
4
2
1
Yeti reward:
threshold level
(over five weeks)
15
20
23
25
For the participants who chose the monetary reward, the reward class defined the
maximum number of rewards they could receive each week. The rationale was that
under unrewarded circumstances (reference travel behaviour), some participants
would not travel during the rush-hour five days a week. In the trial we wanted to
reward participants only for the additional days on which they avoided the morning
rush-hour. We therefore limited the number (n) of rewards a participant could receive
per week, based on their reference travel behaviour.
Technically, this meant that independent of the actual motivation for avoiding the mor-
ning rush-hour, the first 5-n days on which the participant was not registered were unrewarded, and that on all subsequent days on which the participant was not observed
during morning rush-hour he / she received a reward. It sometimes happened that the
travel behaviour of a participant did not affect his / her reward. Take, for instance, the
case of a participant in reward class D who travelled during rush-hour on Monday. As
a result, is impossible for this participant to collect any reward during the remainder of
the week concerned. This situation where the marginal stimulus disappears happened
towards the end of every week for a couple of the participants.
For the participants with the Yeti smartphone, the class defined the threshold value
for the number of days on which the participant could avoid the morning rush-hour.
If the participant met or exceeded the threshold value, he / she would be able to
keep the Yeti smartphone at the end of the trial. If the participant failed to meet
the threshold value, he / she would have to return the phone, but not until the end
of the trial. All days on which the participant was not registered in the morning
rush-hour counted towards meeting the threshold; hence, this threshold value was
larger for participants who travelled less in unrewarded circumstances.
Manual corrections
Some manual corrections on the assignment of participants to reward classes had to
be applied. This was for several reasons. Firstly, because for some of the participants
the number of irregular days (holiday or ill) during the two reward-free weeks was too
large. Those participants for whom this number was seven or more were classified
based on stated travel frequency (taken from the survey or the original registration D1
for the trial; D2 see Chapter 4).
Secondly, because a customized message had been entered in the logbook for one
or more days. These participants were classified manually in line with the philosophy of the automatic algorithm.
Spitsmijden | Experimental design and modelling
15
Thirdly, because some participants expressed their opposition to the classification.
These complaints were processed manually by the project office. Different data were
compared (observations as well as stated travel frequency) with the arguments put
forward by the participant. Based on these considerations the reward classification
was re-evaluated. For some participants this resulted in an upgrade (applied retroactively), which was subsequently communicated to the participant; for others, however, we judged that their classification was correct and that it was more likely that
the complaint was related to the participant’s misunderstanding of the reward class
concept, in which case we answered the request by providing a more personalized
explanation of the reward scheme dynamics.
It is not surprising that most complaints were related to a perceived under-classification.
Nevertheless, one participant argued that she should be assigned to a lower reward class.
The dynamics of the trial sometimes resulted in a participant being reluctant to
complete the logbook. In such cases, it could happen that upon a re-evaluation of the
automatic classification a participant was assigned to a higher reward class as a result
of updated logbook information. The upgrade was then assigned retroactively and
communicated to the participant in a brief personal message.
The final classification of the participants is shown in Table 3.11. It can be seen that
the bulk of the participants were assigned to class A or B (reference rush-hour travel
frequency of 2.5 days a week or more). Less than 5% of the participants had a very low
reference travel frequency (class D).
Table 3.11: Classification of participants over reward classes
Class
A
B
C
D
Reward management
Money
134
61
23
14
Yeti
46
31
22
9
Total
180
92
45
23
The reward management was the algorithm used to calculate the reward that each
participant received as a result of his / her participation in the trial. In a first step the
daily observations including the logbook entry were translated into an indicator that
had a value for each day and each participant. In a second step the actual reward was
calculated, taking into account the reward class of the participant. For Yeti participants we compared in a final step the aggregated value of the weekly rewards with
the reward class specific threshold value in order to determine if the participant could
keep the Yeti at the end of the trial.
Observations
The observations were processed in the reward management algorithm following
a procedure that is very similar to the one that was applied to assign participants to
reward classes.
For each respondent and each observation day we first checked the automated car observations in the 06.00 – 11.00h period.8 If a movement was registered, we checked whether
there was an observation in the 07.35 – 09.25h period. If this was again the case, we decided
that the participant had travelled during the morning rush-hour on that day.
8
16
We only consider observations of OBU-equipped cars.
Experimental design and modelling | Spitsmijden
If no car registration was available in the 06.00 – 11.00h period, we checked the
corresponding logbook entry for that participant, because a technical failure may
have resulted in missing observations. If a participant had indicated in his / her
logbook that he / she travelled by car in the 07.30 – 09.30h period with his / her
own car, or that somebody else travelled in the participant’s car, we included the
day in the morning rush-hour trip count.
The output of this procedure is a variable pl,i,t,w which expresses the behaviour of
participant i on day t (t=1...5) in week w (w=1...10) in the 07.30 – 09.30h period.9
The variable has a value of 1 if the participant travelled in the period considered, and
of 0 if he / she did not travel in the period considered. If there had been a manual
evaluation (see further), the variable also has a value of 1 (i.e. the participant received
a manually determined reward rather than the one determined by the algorithm).
A similar approach was followed to determine if the participant travelled in the
08.00 – 09.00h period. The resulting variable is ps,i,t,w .
Note that in all cases the unit of observation was the behaviour of the participant’s
car, rather than of the participant him- / herself. As a result, if somebody else travelled
with the participant’s car during rush-hour, the participant did not obtain a reward. Inversely, if the participant travelled in another car, he / she did receive a reward (even if
he / she indicated this behaviour in the logbook). The rationale behind this rule is that
through the OBU / EVI system (see § 3.3; EVI system) we could fairly reliably register
the use of the car, but not the identity of the driver. By making the reward dependent
on car use only, we eliminated any motivation for fraudulent logbook completion, and
hence improved the quality of information collected through the logbook.
Separate procedures were put in place in order to check for drivers who reported
too frequent use of other cars, or drivers who tampered with the OBU beacon.
These procedures are discussed in section 3.4.
A manually determined reward was necessary if a participant entered a customized message in the logbook. For the corresponding participants and days, the project office evaluated the available observations together with the comments and
decided on the correct reward, which was then entered manually into the project
database. Similarly, a manually evaluated reward was sometimes necessary in the
case of a complaint formulated by a participant.
Daily reward
Based on the values of pl,i,t,w, ps,i,t,w and the manual reward rm,i,t,w , we calculated the
reward on day t in week w as:
ri,t,w = rm,i,t,w + ra,i,t,w - ra,i,t-1,w
with:
• ra,i,0,w is zero by definition;
• ra,i,t,w the cumulative automatically determined reward on day t of week w.
The cumulative automatically determined reward ra,i,t,w for participant i on day t of
week (w) was defined as: 10
9
10
No bank holidays occurred during the trial, so all weeks (w) comprised five observation days.
The variable ra,i,t,w expresses the level at day t of the cumulative reward collected since the start of
week w, without taking into account manual corrections (these are determined by rm,i,t,w ).
Spitsmijden | Experimental design and modelling
17
ra,i,t,w = max[0,(di- pl,i,u,w)rl,i,w +(di- ps,i,u,w)rs,i,w- (5-t)(rl,i,w +rs,i,w)]
u t
u t
with:
• di the maximum number of rewards participant i could receive per week (dependant on reward class of participant i, see Table 3.12);
• rl,i,w the reward participant i obtained by avoiding the 07.30–09.30 rush-hour
(see Table 3.13);
• rs,i,w the reward participant i obtained by avoiding the 08.00–09.00 rush-hour
(see Table 3.13).
Table 3.12: Definition of parameter di
Reward class participant
A
B
C
D
di (monetary reward)
5
4
2
1
di (Yeti reward)
5
5
5
5
Table 3.13: Definition of parameters rs,i,w and rl,i,w
Reward level of participant i in week w
(see reward schemes § 3.2)
R1
R2
R3
R4
Monetary reward
rl,i,w
rs,i,w
0
3
7
4
0
0
0
3
Yeti reward
rl,i,w
rs,i,w
0
1
0
0
0
0
For the participants, who received a monetary reward, the unit of the reward
variable ri,t,w was the euro, whereas for participants who wanted to keep the Yeti
smartphone, the reward variable ri,t,w had a value of 1 on days the participant managed to avoid the morning rush-hour and otherwise of zero.
YETI
As described above, each morning rush-hour that the Yeti participants avoided,
counted. However, the threshold level to keep the Yeti at the end of the trial was
differentiated according to the reward class the participant belonged to. This level
was compared to  ri,t,w to evaluate if participant i had won his / her smartphone.
w,t
Reward feedback
Each participant’s reward level was shown on that person’s personalized webpage.
For those who were reluctant to complete the logbook or fulfil other obligations
(such as completing the surveys), we did not show their reward level. This was
in order to motivate the participant to provide the information requested and
(because the actual logbook entry could impact the reward level) in order to avoid
changing historical reward levels or inducing tuning behaviour.
The reward level was updated once a week. Every Friday, information up to the end
of the previous week was processed in order to re-evaluate the cumulative reward.
This prevented tuning behaviour (e.g. the five-minute tolerance was not formally
communicated to the participants).
18
Experimental design and modelling | Spitsmijden
3.3 Technique used in the trial
In this part of the Spitsmijden project, an important role was given to the technical
side of the trial, because without appropriate technical help the trial could not have
been conducted. To support the trial, different techniques were applied for different
purposes. The main idea was to apply and test existing and new traffic detection
techniques. In addition, without a website it would not have been possible for the
project office to function or for contact with the participants to be maintained. The
Spitsmijden website was used for both the internal and the external communication.
It was also necessary to design the logbooks such that they would provide useful
information for the scientific analyses. All the participants were obliged to fill in these
logbooks and to inform the project office about the travel decisions they made.
Also the feasibility, correctness and practicability of different types of equipment for
traffic detection and registration were tested. The advanced techniques used to detect
traffic and to provide travel information to users comprised the following components:
• OBU devices;
• Camera systems;
• Yeti smartphone.
Details about these traffic detection and recognition techniques are given further
on in this document.
In brief, the technical side of the project consisted of the following:
• Detection of car movements (OBU and cameras);
• Storing, filtering and accessing information about car movements, participants,
bonuses, etc. (data structure and database);
• Providing information to the participants as well as to project groups, and com-
municating within the project and with the participants (website design);
• Collecting information about the participants’ travel decisions (logbooks);
• Providing the participants with traffic information (travel times on the A12 from
Zoetermeer towards The Hague).
All this is described in more detail in the following sections.
EVI system
An EVI (Electronic Vehicle Identification) system was implemented for the first time
in the Netherlands in order to signal and register the participating vehicles. This
section explains the technical design of and the results achieved by the EVI system.
System components
Broadly speaking, the system comprised four components. Together, they formed
the complete chain from vehicle to registration (see Figure 3.5).
Figure 3.5: EVI system components
OBU
EVI beacon
Communication
Back office
OBU
The participating vehicles were fitted with an On Board Unit (OBU) – a small transponder / transmitter that was fixed in a holder attached to the windscreen. Each
Spitsmijden | Experimental design and modelling
19
OBU had a unique identifying code. As soon as a connection was made with an EVI
beacon, the identifying information was transmitted.
EVI beacon
The EVI beacons were placed either on portals over the traffic lanes from which
the readings were to be taken, or on posts situated next to the road (see Figure
3.6). One EVI beacon was used for each traffic lane. Each EVI beacon comprised an
antenna and a registration unit. The antenna picked up the signals from the OBUs
installed in the passing vehicles. Communication took place by means of DSRC (dedicated short range communication), using a radio frequency of 5.8 GHz. As soon
as a vehicle was within range of the antenna, the information exchange between
the OBU in the vehicle and the EVI beacon took place (see Figure 3.7). The information obtained was recorded in the registration unit.
Figure 3.6: Single-lane
EVI beacon
Figure 3.7: Interaction between OBU and EVI beacon
On Board Unit (OBU)
Beacon
The information registered by the unit was then downloaded to the back office for
processing.
Communication unit
The communication unit provided the communication between the EVI beacons
and the back-office system. For the trial, we chose a wireless system that uses GPRS
/ UMTS. It would of course have been possible to use a fixed network connection.
Back-office system
The back-office system comprised two components:
• EVI registration system: this component transferred information from the EVI
beacons to the central system and registered it in the central EVI database;
• EVI management system: this component managed the various data collections
in the registration, registration consultations and the supply of information from
the registration. All the EVI readings were sent to the project office each day (see
Figure 3.8).
Figure 3.8: Management system
20
Experimental design and modelling | Spitsmijden
EVI system data flow
A detailed overview of the complete data stream of information in the EVI chain
(i.e. from OBU to the project office) is presented in Figure 3.17.
Location overview
For the trial, EVI beacons were placed on all main exit roads from Zoetermeer
towards The Hague. The EVI beacons were situated at the following locations (see
Figure 3.9):
• A12 (three lanes)
• Zwaardslootseweg (N206)
• Zoetermeerse rijbaan
• Katwijkerlaan
Figure 3.9: Locations of the EVI beacons
EVI Zwaardslootseweg (N206)
EVI Zoetermeerse rijbaan
EVI A12
EVI Katwijkerlaan
A12
A multi-lane installation was placed over the A12. EVI beacons, linked together,
were centrally located above each of the three lanes of the A12. Each beacon was
connected to a single communication unit. Installation directly above the traffic
lane at a height of five / six metres is the prescribed mounting for the guaranteed
capture rate of 99.9%.
Other main exit roads
A single-lane installation was installed on the other main exit roads. These instal-
lations comprised an EVI beacon and a communication unit. For technical reasons
and reasons related to planning permission, these single-lane beacons were sited
adjacent to the road instead of overhead. Installation adjacent to the road puts
higher demands on the tuning (aiming) of the beacon, which reduces the likelihood of good functioning.
Test location
In order to carry out test work, and for a managed installation of OBUs, a singlelane EVI beacon was installed in the RDW (Department of Road Transport) vehicle
park in Zoetermeer.
Spitsmijden | Experimental design and modelling
21
Guarantee of correct functioning
The data provided by the EVI system formed an important basis for the scientific
study. The demonstrably correct functioning of the system was obviously of great
importance. This was fully taken into account during the EVI set-up and preparation
phase. The following measures were taken to guarantee the correct functioning of
the EVI system.
Proven technology
A conscious choice was made to utilize proven technology. The technology used is
currently being put to successful use in other countries (e.g. Austria and Portugal).
Installation plus test and extension
The fitting of the OBUs was carried out by qualified technical staff in accordance
with the manufacturer’s instructions and took place during special fitting evenings.
Immediately following its installation, each OBU was tested to ensure that it was
the correct OBU and was functioning properly. The beacon installed in the RDW
parking facility was used for this.
Tamper-proof
The OBU holders were attached to windscreens using a special glue. If a holder was re-
moved, the glue became visibly damaged and the holder could no longer be replaced. In
combination with the fitting and removal by qualified staff, this eliminated the possibility
of the temporary removal of the OBU without intervention by the project management.
Tamper indicator
Special tamper-proof OBUs were used. When an OBU was fitted to its holder, a switch
was activated. If the OBU was then removed from its holder, the next time the vehicle
passed an EVI beacon a signal (‘OBU removed from holder’) was sent to the beacon.
This was registered and the switch was reset for subsequent readings. This made it
impossible for participants to remove the OBU from its holder unnoticed.
System management
A procedure was implemented to ensure the correct functioning of the EVI system.
In the case of a breakdown, rapid intervention was possible.
Installation test
An EVI test location was set up at the RDW parking facility in Zoetermeer. This made
it possible to simply and easily carry out advance control tests of the system and
also to check the installation of the OBUs.
Reserve beacon
In order to be able to deal quickly with technical problems, a completely set-up and
functioning reserve beacon was available throughout the trial. Thus, all components
could have been replaced within 24 hours should the need have arisen.
Cameras
A licence plate recognition camera was installed at every EVI location to record the
registration number of every vehicle that passed (see §3.3; Camera system). The
camera compensated for any unsuccessful EVI registrations and thereby reduced
the chance of missed registrations to nearly zero. This same technique is in use in
Austria, where a camera is installed adjacent to the EVI beacon at every toll location.
22
Experimental design and modelling | Spitsmijden
000
Facts and figures
Participants
The participants in the EVI part of the trial can be divided into two groups, namely
those taking part in and those carrying out the trial (see Table 3.14).
Table 3.14: EVI participant classification
Type of participant
Participant
Researcher
Readings
More than 31,000 EVI readings were made during the measurement period. The
readings were divided over the various locations as presented in Table 3.15.
Table 3.15: Readings at the different locations
leeftijdsverdeling deelnemers
<25 jaar
>49 jaar
25-34 jaar
Location
No. of readings
A12
26,772
Zwaardslootseweg / Middelweg
1,392
Zoetermeerse rijbaan
1,463
RDWopleidingsniveau
parking facility
1,266
deelnemers
Deelnemers naar huishoudenssamenstelling
Katwijkerlaan / Pijnacker
677
VMBO/HAVO
2%
5%
13%
RDW test / reserve beacon
15
LBO
Total
31,585
HBO/WO
Spread
HBO
56%
24%
across the morning rush-hour
The most important gauge for both the trial itself and the functioning of the EVI
system was the multi-lane system installed over the A12. To illustrate this, the
graph below (Figure 3.10) shows the distribution of all the EVI readings taken
during the morning rush-hour (i.e. 06.00 – 12.00h) throughout the measurement
period (24 August 2006 – 20 January 2007).
35-49 jaar
Spread of EVI readings across rush hour
Figure 3.10: Spread of EVI readings across the rush-hour
Frequentie woon-werk rit
A12
12%
5,182
5,000
4,000
26%
62%
3,029
3,000
2,578
1,000
26,772
100%
99%
98%
000
000
677
832
309
07.00
07.30
07.30
08.00
552
08.00 08.30 09.00 09.30 10.00 10.30
08.30 09.00 09.30 10.00 10.30 11.00
427
467
11.00
11.30
11.30
12.00
The comparison between the observed and via camera system registered data
OBU did not bleep
For the duration of the trial, the participants kept a logbook (see § 3.3; Website and
500 option ‘No OBU beep’ could be ticked. This option was
logbook)
in which the standard
Functioning
per location
Number of EVI measurements
per location
1,463
1,344
1,102
06.00 06.30
06.30 07.00
1,392
2,160
1,955
2,000
000
000
Number
344
25
1,266
97%
96%
95%
99.94% 99.86% 98.91% 96.16%
100% 99.81%
400
Manual
count
Camera
count
Spitsmijden
| Experimental design and modelling
300
200
23
35-49 jaar
ticked 107 times.
In 47 ofwoon-werk
these cases,
an EVI registration did in fact take place despite
Spread of EVI readings across
Frequentie
rit
the record. Two OBUs were found to be faulty or improperly fitted. This accounted for
A12
12%
14 reports. Forty-four reports were caused by a beacon that was not optimally aimed;
5,182
5,000
for the remaining two reports, no explanation could be found.
4,000
26%
Results62%
3,029
The EVI system performed very well: it made a total of 31,585
EVI readings in the period 24
3,000
August 2006 – 24 January 2007. The readings were taken at four locations from Zoetermeer towards The Hague, and at the test location at the RDW
Zoetermeer parking facility.
2,000
2,578
1,344
1,102
1,000
reported
During the measurement period, the 340 participants
a mere 60 cases of
309
malfunction that, when checked, showed that no EVI registration had taken place.
06.00 06.30
Seen against the total number of registrations, it can be established
that a07.00
score 07.30
of
06.30 07.00 07.30 08.00
11
99.81% was achieved (see Figure 3.11).
2
1,955
08.00 08.30 09.00 0
08.30 09.00 09.30 10
The comparison between the
Figure 3.11: Results of the OBU / EVI system
Number of EVI measurements
per location
25,000
26,772
100%
99.94% 99.86% 98.91% 96.16%
400
100% 99.81%
99%
98%
20,000
15,000
10,000
5,000
0
500
Functioning per location
A
1,392
1,463
677
1,266
B
C
D
E
A A12
B Zwaardslootseweg
300
97%
96%
95%
94%
200
A
B
C
D
E
C Zoetermeerse rijbaan
E RDW parking facility
D Pijnacker
F Total
Detection rates of camera systems used at
different locations in the Spitsmijden project
100
F
0
N470
N206
98.5%
The majority of the malfunctions were caused by a less than optimum setting
45% circumstances
up of the EVI beacons alongside the local exit roads. Local
led to a
A B C D
97%
40%
conscious
choice
for
a
less
optimum
situation.
The
EVI
location
over
the
A12,
which
35%
96.5%
30%
was installed in accordance with the supplier’s instructions,
produced even more
25%
96%
20%
favourable
results:
with
only
2
relevant
malfunctions
out
of
26,772 readings, the
95.5%
15%
detection rate (%)
10%
final
result
was
99.99%.
95%
Besides
94%
5%
0%
By car
By car
By car
By car
before 07.30to07.30-08.00
08.00-09.00 09.00-09.30
the registration through the OBU device, it was necessary
detect the
passing of the participants along alternative routes from Zoetermeer towards
93.5%
The
35% possible routes, it was
93% Hague. Because the EVI registration covered only four
A B C D
30%
necessary
to
identify
other
alternative
routes
from
Zoetermeer
towards The Ha92.5%
25%
N470
N206
N494
Aziëweg
gue. One of the solutions was to place additional license plate recognition camera
20%
systems on these alternative routes and to spot participants who tried to evade
15%
the EVI detection system. One of the tasks of the project
10% team was to choose from
the many possible locations the most appropriate places
5% to install the cameras.
The locations are shown in Figure 3.12.
0%
By car
after 09.30
effect of Yeti reward
Camera system
94.5%
effect of monetary reward
97.5%
By car
By car
By car
By car
By car
before 07.30 07.30-08.00 08.00-09.00 09.00-09.30 after 09.30
11
The figures concerning the functioning of EVI are based on the EVI measurements, the camera
measurements and the `No OBU beep’ reports that were logged by the participants on the Spitsmijden
website during the trial.
24
Experimental design and modelling | Spitsmijden
Figure 3.12: Locations of camera systems in Zoetermeer
registration
equipment
EVI with camera
camera
In order to gain more insight into the real situation and to improve the control of
detections, it was decided to install the camera systems also at locations where
EVI devices were already present. The reasons for this were:
1. The participants were thus registered by both detection systems on the main
roads from Zoetermeer towards The Hague. That provided an additional checking rule for detection of the participants in situations where the reward should
or should not be given.
2. The functioning of both systems was checked simultaneously and any mistakes
or malfunctions were identified in time (for more information, see § 3.4).
3. The cameras detected every vehicle passing along the road. There was a danger
that participants would use the family’s second car and thus collect a reward
while travelling during the rush-hour. Therefore, the license plate numbers of
second (and even third) cars in the participants’ families were also registered
and their passing was detected.
4. The cameras detected all traffic and thus also measured the volumes of the total
traffic, which in additional analyses can be used to calibrate the behavioural
changes of the participants.
It should be noted that the above aspects are crucial for this sort of experiment.
What are ‘traffic cameras’?
Traffic cameras are used to detect car movements based on the recognition of
license plate numbers. These devices are installed alongside roads and are able
to recognize the license plate numbers of different categories of vehicles (pas-
senger cars, motorbikes, buses, trucks). It has been proven that these systems are
efficient in different traffic situations (congestion, bad weather, etc.). It should be
noted that for this technique it is not necessary to have a special device in the car.
Figure 3.13 shows such a camera in use.
Figure 3.13: A camera system
placed at N206 location in
Zoetermeer, the Netherlands
How a camera system for licence plate recognition works
It should be noted that, despite the previously explained OBU systems, the cameras are able to detect and recognize all the traffic passing along a particular road
(and not only the participants with installed OBU device). In accordance with privacy laws, the licence plate numbers of non-participants in the trial were not used
in the analysis or in any other part of the project. Moreover, these licence plate
numbers were deleted from the dataset.
Spitsmijden | Experimental design and modelling
25
EVI
beacon
25-34 jaar
HBO
HBO/WO
>49 jaar
25-34 jaar
56%
24%
LBO
GPRS
router
OVP
48V
DSRC
220V power supply
HBO
HBO/WO
5
OBU
Such a camera (or video) system comprises hardware and software. The hardware
component consists of the camera itself, its support structure, the processor used
35-49 jaar
FTP serve
to process the images of license plates, a central server for collecting data, the
Spitsmijden
project office
electric supplies, batteries, etc. The software component consists of license plate
Spread of EVI readings across rush hour
recognition software, whereby the license plate images are recognized, transforSpread of EVI readings acro
Frequentie woon-werk rit
med and sent to the central server. The data are then processed and analysed.
5,182
5,000
4,000
26%
3,000
2,000
1,000
1,102
309
Figure 3.14: Traffic cameras
06.00 06.30
06.30 07.00
07.00
07.30
Spitsmijden
database
A12
12%
5,182
Efficiency of the camera systems
5,000
The performance of all the camera systems was checked and controlled during
4,000
the trial. This was done by using a video camera to register
the traffic situation for
26%
62%
3,029
at least
one
hour.
The
recordings
were
analysed
by
manually
counting the vehi- 3,029
2,578
2,160
cles (light1,955
blue histograms
in Figure 3.15) and comparing 3,000
the figure with the data
obtained from 1,344
the camera system (dark blue histograms in Figure 3.15). In this way,
2,000
the performance of all the 832
cameras used in the project at the different locations in
1,102
552
467
0.398%
427
Zoetermeer was determined. The detection
rates varied between
94% and
1,000
309
(see Figure 3.16).
07.30 08.00 08.30 09.00 09.30 10.00 10.30 11.00 11.30
08.00
08.30
09.00 09.30
10.00 10.30
11.00
11.30
share morning rush-hour
A12
12.00
0.25
06.00 06.30
06.30 07.00
07.00
07.30
Figure
3.15: The number of cars detected by the camera and the number established
0.2
The comparison between the observed and via camera system registered data
by the manual count
500
Functioning per location
99.94% 99.86% 98.91% 96.16%
100% 99.81%
99%
98%
25,000
300
20,000
97%
96%
95%
94%
Camera
count
26,772
C
D
E
F
1005,000
0
d at
roject
0
A
N470
1,392
1,463
677
1,266
B
C
D
E
N206
N494
94%
Aziëweg
B
C
D
The comparison between th
0.05
E
400
300
200
06.00 06.20 06.40 07.00 07.20
F
100
N470
N20
Detection rates
of camera
at
Figure 3.16: Detection
rates
(in systems
%) ofused
camera
systems used at different locations
97.5%
A Preliminary measurement
45%
B€3
40%
C Variable reward
35%
D€7
97%
96.5%
96%
95.5%
95%
By car
By car
By car
By car
By car
before 07.30 07.30-08.00 08.00-09.00
09.00-09.30 after 09.30
94.5%
Passenger
detection rate (%)
Public
Bicycle
transport
effect of Yeti reward
35%
30%
A B C D
25%
N206
10%
5%
Data structures
0%
and database
By car
By car
before 07.30 07.30-08.00
26
93%
N470
15%
30%
25%
20%
15%
10%
5%
Teleworking
0%
A B C D
N494
35%
A Preliminary measurement
B Reward relevant
30%
C Reward not relevant
DAziëweg
Only traffic information
25%
20%
15%
10%
0.2
0.18
0.16
0.14
0.12
0.1
By car
By car
By car
By car
By car
0.08 08.00-09.00 09.00-09.30 after 09.30
before 07.30 07.30-08.00
93.5%
92.5%
20%
effect of monetary reward
A B C D
effect of Yeti reward
effect of monetary reward
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
98.5%
94%
Aziëweg
08.00 08.30 09.00
08.30 09.00 09.30
0
different locations in the Spitsmijden project
ction rate (%)
100% 99.81%
0
A
1,344
500
0.1
97%
96%
95%
10,000
B
99.94% 99.86% 98.91% 96.16%
99%
98%
15,000
200
A
Functioning per location
100%
1,955
0.15
share morning rush-hour
100%
Manual
count
Number of EVI measurements
per location
400
07.30
08.00
2,578
0.06
A B C D
0.04
0.02
0
06.00 06.20 06.40 07.00 07.20
During the trial, different data from different sources were processed. In order to
5%
store and
data, anPublic
appropriate
structure was needed. Because
By car
By carretrieve
Teleworking
By car collect,
Passenger
Bicycle data
0%
08.00-09.00 09.00-09.30 after 09.30
transport
By car cameras,
By car
By car log-By car
data were available from different locations, different sources (OBU,
before 07.30 07.30-08.00 08.00-09.00 09.00-09.30
books) and in various formats, the way they were collected and stored was crucial.
Experimental design and modelling | Spitsmijden
By car
after 09.30
Data from different sources also needed to be stored, processed and retrieved
from the database.
EVI data stream
The collection of observation data generated by the EVI beacons is illustrated in
Figure 3.17.
Figure 3.17: EVI data
EVI stream
data stream
aar huishoudenssamenstelling
5%
2%
RDW Domain
Domein
13%
cabin
EVI
beacon
24%
DSRC
OVP
GPRS
router
RDW Server
Vodafone
Vodaphone
Remote
control
48V
220V
power supply
Powersupply
220V
Vodaphone
leasedline
line EVI
Vodafone leased
system
OBU
EVI
Download
passages
Spitsmijden
project office
VLS-rpl
internet
FTP server
VLS-beh
intranet
ur
552
427
467
11.00
11.30
11.30
12.00
nd via camera system registered data
d via camera system registered data
Manual
count
share morning rush-hour
Spitsmijden
Data
Base
database
Spitsmijden
0 10.30
0 11.00
0.25
0.2
XML
passages
data
participants
manual
Post-measurement
Nameting
EVI beacon
The EVI beacons communicated with passing OBUs on the basis of DSCR (dedicated
short range communication). Data were transmitted from the OBU to the beacon,
where they were recorded. The data in the beacon could be accessed by a back-office
system. Such a beacon can record 20,000 – 25,000 registrations in its internal buffer.
0.15
0.1
Camera
count
XML
VLS
oracle
OBU
The OBU (On Board Unit) was in the form of a small transponder fitted to the
windscreen of the vehicles, using a holder and glue. The OBU was programmed
with the registration number of the vehicle for identification purposes. Each OBU
had its own unique ID. Both the holder and the OBU were
fraud-resistant: if the
Vóórmeting
Preliminary measurement
holder was removed from the windscreen, the glue was
irreversibly
damaged. As
3 euro
€3
soon as an OBU was removed from its holder,
thereward
factVariabel
was immediately registered
Variable
€7
by the EVI beacon at the next detection point.
7 euro
0.3
0.05
0
Cabinet
07.40 08.00 08.20 08.40 09.00 09.20 09.40 10.00 passagetijd
The cabinet was waterproof and installed at the EVI location
near to the beacon. It
passage time
contained the following elements:
• Power supply: for the 48 V power supply required by the EVI beacon;
• OVP (over-voltage protector): distribution centre for power supply and data com-
06.00 06.20 06.40 07.00 07.20
Aziëweg
Az.weg
rd
A Preliminary measurement
3
B 3€euro
C Variable reward
7
D 7€euro
Public
Bicycle
Teleworking
share morning rush-hour
94
Austria
Oostenrijk
GSM GPRS
0.2
0.18
0.16
0.14
0.12
munication. Also provided protection against overloading of the beacon;
• Router: for the GPRS communication with the back office.
Vóórmeting
Preliminary
measurement
Beloning, relevant
Reward relevant
Reward not
relevant
Beloning,
niet relevant
Only traffic
information
Spitsmijden | Experimental
design
and modelling
Verkeersinformatie
Post-measurement
Nameting
27
GPRS and Vodaphone rented line
Communication between the router and the back-office system was via GPRS
(general packet radio service) over a dedicated Vodaphone rented line.
Structure of the database
Data were different with regard to locations, time dimension, precision, etc. The structure
of the database was such that it was possible to process, retrieve and store all the data.
The database was connected with the central server, where the data were collected.
A dedicated database structure was developed for this project. It was designed
and developed using Relational Rose and MySQL database software. The main
data held in this database concern: participant, vehicle, movements, detection
point, reward scheme, reward type, etc. Figure 3.18 shows the structure of the
database.
Figure 3.18: Spitsmijden database structure
Role of the
Detection
participant
point
Participant
Vehicle
Passage
Reward type
Reward class
in trial
Reward
Reward scheme
The registrations from the different traffic locations were the input for the database.
They were assigned to the participant and vehicle that was detected using the previ-
ously described detection techniques. Based on these detection points, the reward was
calculated for the participant based on the corresponding reward class (see § 3.2). All
registrations of participants were preserved in the evidence in the Spitsmijden database.
Results
The database was used for various purposes:
• The helpdesk used the data to support the participants and to give them information in the case of unclear situations;
• The data were used to check the techniques applied during the trial;
• The data were used to calculate the participants’ rewards based on their detection;
• The data were distributed to various project partners for further scientific analysis.
Website and logbook
28
In order to inform and communicate with both the participants and the
project partners, it was decided to design and develop a dedicated website
(www.spitsmijden.nl). It was used for the following tasks:
1. To register the participants in the trial;
2. To provide information about the trial;
3. To communicate with the participants via a personalized page;
4. To communicate internally within the project.
Experimental design and modelling | Spitsmijden
The participants were obliged to provide information about their travel behaviour
by completing personalized logbooks on the website. Moreover, they were able to
check their movements on the website and see the rewards they had earned.
A screenshot of the website is shown in Figure 3.19. The website provides not only
information about the purpose and the scope of the project (e.g. which geographical area was covered), but also the conditions that commuters needed to fulfil in
order to participate in the trial. It explains the organization and structure of the
trial and where the participants and others (e.g. media representatives) can obtain
information about the project partners involved in the trial.
Figure 3.19: The Spitsmijden website
For this project, in which travel behaviour is the central issue, it was necessary to
obtain from the commuters information about their travel behaviour (e.g. their de-
parture time and other relevant details). This information will be used for further
scientific travel behaviour analysis in the second stage of the project.
Figure 3.20 presents a screenshot of the logbook used in the trial. The participants
were obliged to fill in these forms via the Spitsmijden website every week.
Figure 3.20: The logbook page
Both the website and the logbook proved to be very useful.
Spitsmijden | Experimental design and modelling
29
Yeti smartphone
as a reward
Some of the participants chose to use and (possibly) earn a Yeti smartphone,
rather than a financial reward. The purpose of using Yetis was to investigate the
influence of the actual and historical travel information (e.g. about delays, road
works, etc.) on the travel behaviour of the participants. The question how much
and in what way travel information would influence the travel decision of the
participants needed to be answered.
In addition, the Yetis were used not only to send traffic information to the participants, but also to establish the location of their vehicles. This information was
useful for checking the registrations in the project and will be used for the further
scientific analysis of travel information.
Figure 3.21: Yeti smartphone
The smartphone used in the trial is a form of PDA (personal digital assistant) that
incorporates a mobile telephone, diary, e-mail function, Internet access, camera,
etc. The smartphone can be easily placed in a vehicle using a simple plug-in holder
(Figure 3.21). The use of the smartphone is handsfree; it is thus considered safe to
use while driving. The participants could install the smartphone themselves,
using the installation guide. If they had any questions, they could contact the
Spitsmijden helpdesk.
The most recent information about traffic congestion was sent to the participants
via GPRS (general packet radio service). Congestion information from the Depart-
ment of Waterways & Public Works traffic centre was sent via the Yeti to the participants. Such information is updated every minute. The congestion information
is presented in the form of a traffic map, with the possibility to zoom in or zoom
out (see Figure 3.22). Moreover, using a PDA website that had been custom made
for the trial, the participants were able to see the actual travel times in minutes
between Zoetermeer and Prins Clausplein in The Hague. The idea is that the participants would use this information for their travel decisions. It should be noted
that the participants were obliged to use their Yeti while travelling from Zoetermeer towards The Hague.
The participants were required to remove the smartphone from their car while it
was parked. This allowed them to read the traffic information provided by the Yeti
while, for example, at home or the office. The decision when and how to travel
could thus be made before starting the trip.
Figure 3.22: Yeti smartphone
screen, showing traffic
information
Via this website, the participants also had information about relevant incidents
on the Zoetermeer–The Hague route, as well as recent information about public
transport (delays etc.). The participants could use such information also when they
were not driving (e.g. while they were eating breakfast prior to departure).
The Yeti device is equipped with a GPS (global positioning system) receiver. The
traffic map displayed on the smartphone can show the environment in which the
car is situated. More importantly, the Yeti also uses GPS to register the exact
location of the car. This information was used to analyse which routes the
participants took and to check the detections.
The results show that the travel information influenced the travel behaviour of
the participants. A more detailed analysis will be performed and published during
stage 2 of the project.
30
Experimental design and modelling | Spitsmijden
3.4 Quality control of
data and processes
The control of procedures in the Spitsmijden trial received special attention. Twice
a week:
• The registrations from OBU and cameras were compared;
• The registration from OBU and camera was compared with the content of the
logbooks;
• The functioning of EVI cameras was checked;
• The status of the OBU device in the participants’ vehicles was checked;
• The use of the family’s second / third car was checked.
Apart from these periodic checks, ad hoc data checks were conducted in order to
identify potential issues with the data registration:
• The customized logbook entries were examined at the beginning of the trial in
order to check which circumstances the participants felt they could not classify
under the proposed alternatives. For some recurring issues we provided feedback
in the weekly newsletter on the definition of the proposed alternatives;
• Different database tables were inspected repeatedly in order to identify potential inconsistencies between tables or typos in manual data input.
Detection of the use of alternative routes
The participants could use routes other than the A12 to reach The Hague. There-
fore, not only the movements of the participants along the A12 from Zoetermeer
towards The Hague were checked, but so too was the traffic on the alternative
routes from Zoetermeer towards The Hague. Sophisticated detection techniques
(see § 3.3; Camera system) were used to check whether the participants used the
A12 or other, less congested routes. Thus, the participants were not able to evade
detection during the morning rush-hour.
Use of the family’s second / third car
Because the participants could have used their family’s second or third car to drive
during rush-hour and thus evade detection, additional cameras were placed on all relevant routes. The following rules were applied in order to prevent misuse of the trial:
• Participants signed a contract in which it was clearly stated that misuse of the
trial was not allowed;
• To support the OBUs (whose reliability is guaranteed to be 99%), the alternative
routes were equipped with traffic detection cameras;
• The logbooks and the detected movements were compared with each other.
3.5 Special traffic
circumstances
This section concerns some special circumstances that may have influenced travel
behaviour during the trial. The aim is to identify relevant indicators that can be
included in subsequent, more detailed travel behaviour analyses.
RandstadRail
The historical public transport supply on the Zoetermeer-The Hague corridor
mainly relies on two heavy-rail systems. The first is a local rail loop (Zoetermeer
Stadslijn) that serves all quarters north of the A12 motorway and connects them
to The Hague. The second is the mainline rail connection between Gouda and The
Hague, which serves two stations in Zoetermeer (both are adjacent to the A12
motorway).
The original reason to schedule the trial for the autumn of 2006 was the redesign
of the local rail network during the summer of 2006. The plan was to convert the
local heavy-rail loop into light rail operation and to link it to the existing light rail
Spitsmijden | Experimental design and modelling
31
system in The Hague. As the start of the trial approached, however, it became
clear that construction planning had gone off track and that the trial would have
to start with reduced rail operations (mainline rail only). A more or less scheduled
bus replacement service continued to operate after the summer. However, this
bus service was no match for the traditional local rail service: during rush-hours,
it was slowish (at best). The mainline rail service continued at its normal pace.
Even though there were some additional trains, in general the service was not able
to cater for the increased demand. As a result, rush-hour trains were generally
overcrowded.
After the start up of the renewed local rail service had been postponed a couple of
times, it was decided to start a local light rail service on part of the system on 29
October 2006 (the other part continuing to be unserviced until further notice).
Besides some start-up problems (including two minor derailments), everything
went quite well, so it was decided to cancel the major part of the bus replacement
system by 19 November 2006. But it took only a couple of days until the system
got back off track – this time literally. Two major derailments, which caused many
injuries, happened on 29 November 2006. The whole light rail system was immediately shut down.
However, it proved difficult to restart the replacement bus service: the bus opera-
tor had already shifted vehicles to other locations, and for the first couple of days
the bus service was chaotic. It took till 11 December 2006 to get scheduled bus
operations back on track, facing again the same traffic congestion problems of
the original bus replacement service. Mainline trains continued to be overcrowded
during the rush-hour.
By the end of the trial, the public transport supply was again comparable to that at
the start: no light rail, a substandard bus replacement service and an overcrowded
mainline rail.
It was difficult to collect reliable information on bus and train operations during
the period of the trial. Mainline rail timetables have been saved, but replacement
bus operations are difficult to track down for at least part of the trial period. As
mentioned, part of the replacement bus service ran unscheduled in the first days
of December 2006. As for fares the picture is somewhat clearer, as the evolution of
ticket prices (including season tickets) for the different modes has been archived.
The impact of public transport supply (including fares) on travel behaviour will
receive more attention in subsequent project research.
Traffic situation
The congestion information related to the period of the trial has been stored for
further scientific analysis during stage 2 of the project. For example, it will be used
to analyse the influence of information about traffic delays on the travel decisions
of the participants.
Weather
As the weather can influence modal choice (especially the decision to cycle), we
collected some indicators on weather observations. The Dutch meteorological service provides a dataset with fifteen daily weather indicators based on automated
observations at different locations in the Netherlands. The observation points
closest to the area of the trial are Rotterdam, Utrecht, Schiphol and The Bilt. We
took the Rotterdam observations as being the most representative of the circum-
32
Experimental design and modelling | Spitsmijden
3.6 Analysis
Effects of rewards
on travel demand
stances in the area of the trial, and so added them to the project database. The
actual impact of the weather on travel behaviour will be studied at a subsequent
stage of the project.
The participants could earn a reward by not travelling by car during the morning
rush-hour. Those participating in the monetary reward variant could receive one of
three different rewards (€ 3, € 7, variable reward). Those participating in the Yeti
variant were divided into two groups. In one group, those who avoided the rushhour enough times were allowed to keep their Yeti at the end of the trial. They
also received traffic information. Those in the second group received only traffic
information (see § 3.2; Reward scheme).
This section discusses the behavioural effects of the various rewards. This is done
by analysing the distribution across travel modes and travel times in various
reward variants and comparing these distributions with those in the reference
periods before and after the trial. The analyses are based on the automated registration of vehicle movements (EVI and cameras) combined with information from
the logs filled out by participants (see § 3.3). In the analyses described throughout
section 3.6, we included only observation days on which travel behaviour could
actually impact the reward obtained (see § 3.2; Reward classes). Furthermore we
excluded observation days for which the participant indicated in the logbook to be
on holiday, to be ill or not to have to work (part-time workers). Explanatory variables were derived from the initial questionnaire D2 (see Chapter 4).
The figures presented in section 3.6 'Effects of rewards on travel demand' may
differ slightly from the figures presented to the press in January 2007, due to small
differences in the classification of participants into response categories. More
specifically, the January 2007 figures included the non-working days of part-time
workers.
Financial reward
Table 3.16 indicates the transport methods and travel times at pre-measurement, for
the three reward variants and at post-measurement. It gives the division of all commutes (or telework days) made during the period and / or for the variant.12
In the periods without rewards (pre- and post-measurement), 47-50% of the participants travelled by car during the rush-hour. This indicates that a large proportion of
the participants used to travel outside of rush-hour before the reward scheme was
introduced. This is partly because we used a broader definition of rush-hour during
the recruitment phase. In addition, it seems that a quarter of the participants began
practising adjusting their driving behaviour during the preliminary measurements.
This percentage shrank to 26% with a € 3 reward and to 19-20% with a € 7 or variable reward. The effect of rewards on car travel during rush-hour was very significant.
Avoiding the rush-hour was primarily realized by travelling by car outside of the
rush-hour period. The proportion of car trips before 07.30h increased from 20%
to 33-39%. The proportion of car trips after 09.30h increased from 10% to 15-16%.
Total car use over all time periods decreased from 80% to 73-75%.
12
We assume that all movements during the morning rush hour are made by commuters. This seems
a reasonable assumption in view of the recruitment (§ 3.1; Recruitment of participants), the survey
information (Chapter 4) and the exclusion of non-working days from the observations.
Spitsmijden | Experimental design and modelling
33
After the end of the reward phase, car use during the rush-hour returned to premeasurement levels. This suggests that behaviour stimulated by rewards is not
continued
when the reward is withdrawn. Apparently, the participants do not
opleidingsniveau deelnemers
Deelnemers naar huishoudenssamenstelling
value the alternative behaviour stimulated by the reward enough to continue
VMBO/HAVO
2%
5%as travelling
13%outside of
using the options they had chosen during the trial, such
LBO
the rush-hour or using public transport. Another explanation is that the measures
taken to enable participation in the Spitsmijden trial, such as adjustments in work
times or family obligations, were taken only for the duration of the reward period.
leeftijdsverdeling deelnemers
<25 jaar
25-34 jaar
HBO
HBO/WO
56%
24%
DSRC
The decrease in car use was primarily attained by increased public transport use
(from 4% in the preliminary measurements to 9.5-12% during the reward phase). An
interesting detail is that public transport use was slightly higher during the postmeasurement period than during the preliminary measurements. We can also state
that the bicycle is not a viable alternative to the car for commuting from Zoetermeer towards The Hague. As discussed earlier, inclement weather may have played
Spread of EVI readings across rush hour
a role during the trial. The number of teleworkers increased slightly during the trial.
35-49 jaar
Frequentie woon-werk rit
OBU
A12
12%
A comparison of5,182
the different reward variants shows that a reward of € 3 leads to
the greatest effect (i.e. a reduction in travel during rush-hour from 50% to 26%).
4,000Variable rewards and a € 7 reward lead to an additional reduction of 19%. Signifi5,000
26%
cantly, variable rewards lead to a distribution over alternative transport methods
3,029
times that is almost identical
to what is achieved with a € 7 reward. Apparent2,578
2,160
ly, the participants reacted to the maximum
reward
( € 7) in the variable variant.
1,955
3,000 and
832
Table 3.16: Distribution of1,000
commutes by time and transport method for the various
monetary
rewards
552
467
427
309
N470
34
N206
effect of monetary reward
detection rate (%)
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
35%
By car
By car
By car
By car
By car
before 07.30 07.30-08.00 08.00-09.00 09.00-09.30 after 09.30
A B C D
Aziëweg
25%
20%
15%
A Preliminary measurement
B€3
C Variable reward
D€7
Passenger
0.3
0.25
0.2
0.15
0.1
0.05
0
06.0
Aziëweg
A B C D
Experimental design30%
and modelling | Spitsmijden
N494
N494
Figure 3.23: Distribution of commutes by time and transport method for the various
monetary rewards
ffect of Yeti reward
Detection rates of camera systems used at
different locations in the Spitsmijden project
N206
share morning rush-hour
1,344
1,102
Public
transport
Bicycle
Teleworking
share morning rush-hour
2,000
Preliminary measurement
€3
Variable reward
€7
Post-measurement
06.00 06.30 07.00 07.30 08.00 08.30 09.00 09.30 10.00 10.30 11.00 11.30
08.30 09.00 09.30
10.00 10.30 38.5%
11.00
11.30 12.00
06.30 07.00
By car before 07.30h
20.1%07.30 08.00 33.0%
37.8%
20.7%
By car 07.30-08.00h
17.8%
8.9%
7.4%
6.0%
19.1%
The
comparison between
the observed and10.9%
via camera system registered
data
By car 08.00-09.00h
27.4%
15.1%
9.9%
24.3%
By car 09.00-09.30h
4.8%
2.4%
2.4%
2.2%
3.8%
50016.0%
By car after 09.30h Functioning per location 10.3%
15.9%
15.1%
12.0%
Number of EVI measurements
Manual
per location
Another family car
1.0%
0.7%
0.4%
0.4% count
1.0%
400
99.94% 99.86% 98.91% 96.16% 100% 99.81%
Another, non-family
car
0.1%
0.6%
0.1%
0.2% Camera
0.5%
100%
count
Passenger 99%
in carpool
0.8%
1.9%
1.8%
2.2%
1.4%
300
98%
Public transport
3.9%
9.5%
12.0%
11.4%
6.1%
97%
200
96%
Bicycle
5.2%
4.1%
3.2%
3.5%
1.6%
95%
1,266 means of transport
1,392
1,463
677 Other
2.8%
2.1%
3.2%
2.2%
2.0%
94%
100
B
C
D
E
A
B
C
D
E
Teleworking
2.6%F
3.1%
3.2%
3.9%
2.5%
0 2.7%
Other work location
3.2%
2.8%
3.4%
4.8%
0
EVI
beacon
0.2
0.18
0.16
0.14
0.12
0.1
0.08
0.06
A Preliminary measurement
B Reward relevant
C Reward not relevant
D Only traffic information
0.04
0.02
Yeti as a reward
In the Yeti variant, three situations were presented during the trial:
1. For the duration of five weeks the participants had to avoid enough rush-hours to
be allowed to retain the Yeti. They also received traffic information on the Yeti;
2. For the duration of five weeks the participants only received traffic information;
3. Situation 1, but this was no longer relevant: the participant had already been allowed to keep the Yeti or he / she had not avoided enough rush hours to keep it,
although he / she still received traffic information on the Yeti.
The division of all commutes into different periods and into the different reward
variants is displayed in Table 3.17 and Figure 3.24.
The percentage of car trips during the rush-hour shrank from 43% to 15% due to the
prospect of being rewarded with a Yeti. This decrease is comparable with the reduction due to a monetary reward of € 7 or the variable reward. As in the monetary variant, this reduction was largely realized by an increase in trips before 07.30h (from 21%
to 31%) and after 09.30h (from 17% to 25%). Total car use over all periods declined from
81% to 71%.
As with the monetary variants, this decrease was realized by an increase in the use of
public transport (from 6% to 13%), with the bicycle playing no significant role. Unusually,
in this reward variant more participants chose teleworking than in the monetary
variants. As in the monetary variants, post-measurements indicated that the number of
car trips during the rush-hour had returned to the level of the preliminary measurement.
An interesting phenomenon occurred when participants received traffic information
on the Yeti but could not earn a reward. This was the case when the reward was no
longer relevant as the Yeti had already been earned or could no longer be earned, or
during a period without a reward. In these cases, the number of car trips during the
rush-hour was significantly lower than during the pre- and post-measurements.
One possible explanation is that the traffic information enabled the participants
to avoid traffic due to heavy congestion by delaying or advancing their departure
time or choosing another form of transport. A second possible explanation is that
the participants may not have been completely familiar with the relevance to their
reward of avoiding traffic and therefore may have adjusted their behaviour when
it was not strictly necessary to do so. A third possibility is that the arrangements
made to avoid traffic (e.g. changed work times) were made for the entire period of
the trial and therefore continued during the periods with no reward apart from the
traffic information.
Spitsmijden | Experimental design and modelling
35
07.00
07.30
08.00 08.30 09.00 09.30 10.00 10.30
08.30 09.00 09.30 10.00 10.30 11.00
07.30
08.00
467
11.00
11.30
11.30
12.00
The comparison between the observed and via camera system registered data
Table 3.17: Distribution of commutes by time and transport method for the various Yeti reward types
Number of EVI measurements
per location
Functioning per location
detection rate (%)
effect of monetary reward
Preliminary
99.94% 99.86% 98.91% 96.16% 100%
100%
measurements
99%
By car before
07.30h
21.0%
98%
By car 07.30-08.00h
11.6%
97%
96%
By car 08.00-09.00h
20.8%
95%
1,266
1,392
1,463
677
By
car 09.00-09.30h
10.4%
94%
B
C
D
E
A
B
C
D
E
By car after 09.30h
17.1%
Another family car
1.5%
Another, non-family car
0.4%
Passenger
in carpool
1.1%
Detection rates of camera systems
used at
different locations in the Spitsmijden project
Public transport
5.8%
Bicycle
2.2%
45%
Other transport
A B 1.9%
C D
40%
Teleworking
2.6%
35%
30%
Other work location 25%
3.6%
20%
15%
10%
5%
Figure
0%
500
Reward
Reward
Only traffic
400
99.81%
relevant
not relevant
information
30.9% 300
24.9%
21.9%
4.8%
8.9%
10.0%
6.9% 200
16.3%
16.6%
3.3% 100
4.4%
5.8%
F
25.3%
20.1%
21.3%
0.4% 0
0.2%
1.4%
N470
N206
N494
0.1%
0.6%
0.1%
3.0%
2.4%
2.2%
13.2%
12.9%
9.0%
0.8%
0.8%
1.7%
2.5%
2.0%
2.5%
5.1%
4.0%
3.8%
3.7%
2.4%
3.8%
before 07.30 07.30-08.00 08.00-09.00 09.00-09.30 after 09.30
N206
2%
13%
N494
Aziëweg
56%
24%
effect of Yeti reward
Deelnemers naar huishoudenssamenstelling
35%
30%
A B C D
21.3%
10.4%
24.2%
7.0%
17.2%
0.2%
Aziëweg
0.5%
2.0%
6.6%
0.7%
2.5%
A Preliminary measurement
B€3
2.9%
C Variable reward
4.5%
D€7
transport
EVI
beacon
20%
15%
DSRC
10%
5%
0%
OVP
GPRS
router
A Preliminary measurement
B Reward relevant
C Reward not relevant
information
D Only traffic
Remote
control
48V
Vodaphone leased line EVI
system
By car
By car
By car
By car
By car
before 07.30 07.30-08.00 08.00-09.00 09.00-09.30 after 09.30
Spitsmijden
project office
FTP server
Passenger
Public
transport
Bicycle
Teleworking
Download
passages
VLS-rpl
internet
XML
passages
fore and after the
rush-hour. For the traffic analysisXMLit is important
to view the distribuSpitsmijden
database
data
tion of car trips over the morning in more detail. The table below
shows the distribuparticipants
tion of car trips over ten-minute intervals as a proportion of the totalmanual
number of trips.
1,344
832
552
09.00 09.30 10.00 10.30
09.30 10.00 10.30 11.00
427
467
11.00
11.30
11.30
12.00
between the observed and via camera system registered data
Manual
count
share morning rush-hour
Figure 3.25: Distribution of participants’ car trips during the morning rush-hour
(monetary variant)
2,160
0.3
Preliminary measurement
€3
Variable reward
€7
Post-measurement
0.25
0.2
0.15
0.1
Camera
count
0.05
0
06.00 06.20 06.40 07.00 07.20
N494
36
Aziëweg
08.00 08.20 08.40 09.00 09.20 09.40 10.00
passage time
Experimental design and modelling | Spitsmijden
-hour
N206
07.40
0.05
0
06.0
0.2
0.18
0.16
0.14
0.12
0.1
0.04
0.02
06.0
EVI
Distribution of traffic
VLS-beh
VLS
intranet
In the previous section we mentioned the shift from the rush-hour to theoracle
period be-
ngs across rush hour
0.1
0
220V power supply
OBU
0.15
0.06
Austria
RDW Server
Vodaphone
0.2
0.08
RDW Domain
GSM GPRS
cabin
25%
0.25
Camera
Post-measurements
count
3.24: Distribution of commutes by time and transport method for the various Yeti
By car
By car
By car
By car
By car
Passenger
Public
Bicycle
Teleworking
reward types
EVI data stream
5%
Manual
count
0.3
share morning rush-hour
06.00 06.30
06.30 07.00
427
share morning rush-hour
552
309
0.2
Preliminary measurement
count
0.05
0
06.00 06.20 06.40 07.00 07.20
N206
By car
By car
00-09.30 after 09.30
By car
By car
00-09.30 after 09.30
N494
Aziëweg
A Preliminary measurement
B€3
C Variable reward
D€7
Passenger
Public
transport
08.00 08.20 08.40 09.00 09.20 09.40 10.00
passage time
Figure 3.26: Distribution of participants’ car trips during the morning rush-hour
(Yeti variant)
Bicycle
Teleworking
share morning rush-hour
N470
07.40
0.2
Preliminary measurement
Reward relevant
Reward not relevant
Only traffic information
Post-measurement
0.18
0.16
0.14
0.12
0.1
0.08
0.06
A Preliminary measurement
B Reward relevant
C Reward not relevant
D Only traffic information
0.04
0.02
0
06.00 06.20 06.40 07.00 07.20
Passenger
Public
transport
Bicycle
Teleworking
07.40
08.00 08.20 08.40 09.00 09.20 09.40 10.00
passage time
Figure 3.25 and 3.26 show that the majority of the car trips during the pre- and postmeasurements took place between 07.10 and 07.40h. A significant number of car
trips therefore take place before 07.30h, even without a reward. When a reward is of-
fered, two new peaks are clearly visible. The largest peak, representing 20-25% of the
trips, occurs at 07.20h, just before the critical time period beginning at 07.30h. The
second peak is at 09.40h, just after the time period ending at 09.30h. These peaks
remain the same during all of the reward variants, including the period with only traffic information, although peak sizes vary between the variants as described above. In
the Yeti variant, the second peak is slightly higher than in the monetary variant.
Conclusion
A preliminary conclusion is that rewards, whether monetary or in the form of a
Yeti, lead to substantial decreases in the number of car trips during the rush hour.
Both variants result in a halving of the total number of car trips.
One important observation is that a relatively low reward (€ 3) results in the most
significant effect in avoiding traffic. The additional value of higher rewards is relatively slight. In the Yeti variant, participants appeared to avoid traffic even when
there was no reward. This effect is possibly due to the provision of traffic information, but may also be the result of misunderstanding by the participants about the
reward structure.
The reduction of traffic trips was largely realized by delaying or advancing departure times, as well as by a slight increase in the use of public transport.
Effects at the
individual level
In the previous section we discussed the effects of the reward variants on the total
distribution of transport options. These total effects were the sum of all of the
individual behavioural adjustments. In this section we discuss these individual
adjustments in more detail, paying special attention to the differences between
the preliminary measurements and the various reward types. We also examine
whether the use of certain transport options increased or decreased as a result.
Behaviour change in the monetary variant
Table 3.18 illustrates the behavioural changes in relation to the preliminary measurement by reward variant. The results confirm the description in the previous
Spitsmijden | Experimental design and modelling
37
section. The majority of the participants drove less often during the rush-hour,
choosing instead to drive before or after it. A smaller group chose to take public
transport more often or to work from home.
Significantly, approximately 30% of the participants in each variant chose not to
avoid the rush-hour, sometimes even driving more than during the preliminary
measurement. This shows that the general tendency as described in the previous
section does not apply uniformly to all participants. There are several possible
explanations for this phenomenon:
• First, it may be possible that during either the preliminary measurement or the
reward period there occurred some unexpected circumstance (such as work- or
family-related business) that required the participant to drive during the rushhour, producing a counter-intuitive reaction to the reward at the individual level.
This may have had a considerable effect due to the short reward periods (three to
four weeks).
• It may be possible that structural adjustments relating to work, family or mobility options hindered the adjustment of behaviour, resulting in the participant not
being able to execute his / her planned behavioural adjustments. (We assume
that the participants volunteered for the trial with the intention to do so.) It
is important to consider these factors when analysing the effect of reward on
behaviour, which we do in the following section.
• A more theoretical explanation is that a reduction in rush-hour traffic levels can
make it more attractive for some groups to continue to drive during rush-hour.
The limited number of participants in the trial, however, was insufficient to bring
about a significant increase in traffic speed. This option can therefore be excluded from our analysis.
Table 3.18: Distribution of participants across possible behavioural adjustments (monetary variants)
€3
Variable reward
€7
Increase Same Decrease
Increase Same Decrease
Increase Same Decrease
Trips during rush-hour
17%
16%
67%
11%
18%
71%
13%
15%
71%
Trips before rush-hour
47%
37%
16%
51%
34%
15%
52%
29%
19%
Trips after rush-hour
38%
43%
19%
38%
42%
20%
42%
39%
19%
Public transport
21%
73%
5%
21%
73%
6%
26%
71%
4%
Teleworking
12%
79%
9%
13%
76%
10%
16%
75%
9%
Another interesting question is whether the participants adjusted their behaviour
in only one manner, or whether they combined strategies such as driving before
rush-hour and taking public transport more often. When we look at the number of
behavioural changes, we notice that the majority of people who adjusted their behaviour did so using only one method (such as travelling earlier). Apparently, they
chose to apply the best single option based on their personal circumstances. A
large group applied two behaviour options, and we noticed a trend for participants
to begin to combine options to avoid traffic more often when rewards increase.
38
Experimental design and modelling | Spitsmijden
Table 3.19: Distribution of participants across a number of behavioural adjustments
(monetary variants)
Number of options
0
1
2
3
4
€3
20%
44%
31%
4%
0%
Variable
19%
44%
30%
6%
1%
€7
16%
40%
35%
9%
0%
Behaviour change in the yeti variant
We observed a similar trend in the Yeti variant (Table 3.20). The majority of the
participants drove less often during the rush-hour. This applied both to the variant
with a reward and information, and the information-only variant. In both variants,
most participants travelled before or after the rush-hour. A smaller number chose
to use public transport or to work from home. Some participants drove as often or
even more often than before the reward phase. This confirms the idea that many
specific individual circumstances can influence behavioural adjustment, and that it
is important to gain more understanding of them.
Table 3.20: Distribution of participants across possible behavioural adjustments
(Yeti variants)
Trips during rush-hour
Trips before rush-hour
Trips after rush-hour
Public transport
Teleworking
Reward (relevant)
Increase Same Decrease
11%
17%
72%
45%
40%
14%
43%
33%
24%
21%
71%
8%
20%
73%
7%
Traffic information only
Increase
Same Decrease
15%
15%
70%
28%
53%
18%
30%
48%
23%
20%
76%
3%
10%
80%
10%
The number of behavioural options applied echoes the results of the monetary
variant (Table 3.21). In the reward situation, half of those who adjusted their
behaviour adopted one reaction strategy. A large group adopted two reactions.
When only traffic information is offered, we see a different trend. The strategies
of those who adjusted their behaviour then applied only one reaction.
Table 3.21: Distribution of participants across a number of behavioural adjustments
(Yeti variants)
Number of options
0
1
2
3
4
Reward
16%
42%
30%
12%
0%
Traffic information only
23%
64%
12%
1%
0%
Experiences with spitsmijden
In addition to the scale of behavioural adjustments as sketched in previous sections, it is interesting to note how the participants experienced their behavioural
Spitsmijden | Experimental design and modelling
39
adjustments. Did they have to work hard to achieve them, and what measures did
they take to make their adjustments possible?
As indicated in Table 3.22, 43% of the participants had quite a bit of trouble adjusting their behaviour. Forty-two per cent found it relatively easy to do so. Few
stated that they found it very easy or very difficult to adjust their behaviour.
Table 3.22: Difficulty adjusting behaviour
Very difficult
Reasonably challenging
Relatively easy
Little effort
Unknown
Number
27
130
153
52
2
Percentage
7.4%
35.7%
42.0%
14.3%
0.5%
Some causes of difficulty in adjusting mobility behaviour frequently mentioned
were work- and family-related requirements. Lack of alternative transport means
was mentioned by 5% of the participants, while 7% mentioned the delay in RandstadRail service as the reason for their difficulty in adjusting their behaviour.
Table 3.23: Reasons for difficulty in adjusting behaviour
Reason
Percentage
Work-related requirements (work times, meetings / appointments)
12.6%
Family-related requirements
8.0%
Availability of alternative means of transport
4.9%
Weather
3.3%
RandstadRail delay
6.6%
Other
3.8%
In some cases supporting measures are needed to make a behavioural adjustment possible. The most important measure applied by the participants was to
negotiate with their employer about alternative work times or the possibility of
teleworking (Table 3.23). Many participants also stated the importance of making
arrangements with colleagues and family members. More than a quarter of the
participants stated that they had practised adjusting their behaviour in the weeks
before the trial. This implies that the difference between the preliminary measurements and the reward period is perhaps an underestimate of the true effect of the
trial. Only a small group of participants had looked for information about public
transport or bought a public transport pass.
Table 3.24: Support measures taken by participants
Support measure
Arrangement with employer regarding work times or teleworking
Arrangement with colleagues about work
Arrangement with family members about scheduling
Arrangement with family members about division of tasks
Arrangements for carpooling
40
Experimental design and modelling | Spitsmijden
Percentage
40.1%
24.5%
29.9%
14.8%
7.7%
∆
Continuation of Table 3.24
Purchase of a public transport pass
5.5%
Purchase of bicycle and / or protective clothing
3.3%
Purchase of PC / laptop / broadband connection
0.8%
Gathering information about home-work public transport connections 12.9%
Gathering information about home-work cycle route
3.6%
Practising adjusting behaviour in weeks before trial
26.9%
Other
25.0%
More than a quarter of the participants stated that they had sought traffic information more often than they had before the trial. Almost two thirds stated that participation in the trial had not led to any change in the likelihood of their consulting traffic
information. There is a significant difference here between the Yeti participants and
the money participants: the majority of the Yeti participants used traffic information
more often than they did before the trial, due to the ready availability of the Yeti.
Table 3.25: Consulting traffic information during trial
More often than before
The same as before
Less often than before
Yeti variant
66%
30%
4%
Monetary variant
13%
78%
9%
Total
29%
64%
7%
58% per cent of the participants thought that it would be a good idea to apply
reward measures to stimulate people to avoid driving during road maintenance.
14% per cent stated that this would be a bad idea.
86% per cent of the participants indicated that they would participate in a similar
trial if they were given the chance. Only 5% said that they would not.
Target group analysis
The previous section explained how the behavioural effects of rewards can differ
widely between individuals. In order to implement reward strategies it is important to determine whether different reactions relate to participant characteristics,
such as their family or work situation and their mobility options. To research this
further, we studied how the groups of participants differ in their degree of behaviour adjustment. The groups were identified based on the following criteria:
• Car ownership;
• Gender;
• Single parenthood;
• Availability of public transport;
• Ability to start work earlier;
• Ability to start work later;
• Ability to depart earlier due to family circumstances;
• Regular use of traffic information before the trial;
• Level of education;
• Income;
• Age.
The target group analysis was performed by estimating a logistical regression
model for possible behavioural reactions, such as driving less in traffic, more often
Spitsmijden | Experimental design and modelling
41
before or after the rush-hour, more public transport or working from home. Such
a model represents the probability of a certain behaviour reaction as a function
of the characteristics mentioned above. The logistical regression model can be
expressed as a formula:
Preaction=
1
1+exp [ - ( γ + βjXj )]
j
In this formula, Preaction is the probability of a participant showing a certain behaviour reaction. Xj represents participant characteristics that may influence the
probability of the behaviour reaction, as mentioned above, and βj indicates the
weight of each characteristic. By linking the behaviour reaction to the participant
characteristic we can determine the weight of the βj parameters based on the
observed data. In addition to the participant characteristics, we can also test the
effects of different reward variants with this analysis. In this section, the parameters are represented as exp( βj ). This value indicates how much more likely a
certain reaction will be when characteristic Xj is valid. For example, a value of 1.92
for exp( βj ) for the characteristic ‘availability of public transport’ (see Table 3.26)
means that the availability of a public transport alternative will lead to the odds
rate (P/(1-P)) being 1.92 times as great. When estimating the models, we followed
a sequential procedure where only those coefficients were recorded that were
significant at ( α =0.05).
Target groups in the monetary variant
The groups for which we found statistically significant differences in behaviour
reactions are listed in Table 3.26. This table shows that car ownership has an effect
on the way in which participants react to a monetary reward. A household that
owns two or more cars will be less likely to reduce the number of car trips during
the rush-hour (-45%). One possible explanation is that the presence of two cars
indicates a greater dependency on automobility or a greater preference for car
use, resulting in less motivation for reward. Ownership of two or more cars also
reduces the likelihood of driving before (-41%) or after (-29%) the rush-hour. Teleworking, however, is more likely among this group.
The availability of a public transport alternative has a clear effect on the participants’
reactions. If a public transport alternative is available, the percentage choosing to travel
by public transport is greater and the likelihood of avoiding the rush-hour increases.
Flexible work times also have a clear effect on the reactions observed. Being able to start
work earlier leads to a greater number of individuals avoiding traffic, but not directly to a
larger number of people actually driving before the rush-hour. Being able to leave home
earlier due to a lack of family obligations, however, does lead to a larger number of people avoiding traffic by leaving earlier. Being able to start work later leads to a significant
number of people who leave for work after the rush-hour. Finally, participants who could
leave home later due to their family circumstances are more likely to work from home.
Analysis shows that single parents rarely consider leaving for work earlier as an alternative (odds rate -79%). They are more likely to choose to travel after the rush-hour.
This may be explained by child care obligations, which keep them from leaving home
earlier but leave open the option of departing later. Well-educated participants were
more likely to choose to work from home, and were also more likely to take public
transport. Older participants were less likely to take public transport.
42
Experimental design and modelling | Spitsmijden
We also found that participants who regularly consulted traffic information were
more likely to show a decrease in trips during the rush-hour. The use of traffic
information possibly enabled them to better avoid traffic caused by unexpected
congestion. This group may be more flexible in their behavioural adjustments.
Significantly, our analyses show that the amount of reward has no influence on the
probability of displaying a certain reaction. The amount of reward probably has
more effect on the frequency of behavioural adjustment.
Table 3.26: Effects on behaviour reaction chosen (monetary variant) based on logistical regression model (exp())
Fewer trips
during rush-hour
Two or more cars
Availability of
public transport
Can start work earlier
Can start work later
Can leave home earlier
Can leave home later
Income > € 4,500
Single parent
At least higher
professional education
Age 51+
Uses traffic
information weekly
0.55
Driving before
rush-hour
0.59
1.92
2.83
Driving after
rush-hour
Travelling by
public transport
0.71
Working
at home
1.91
5.27
1.72
0.21
2.3
2.03
0.52
1.68
2.6
1.61
Target groups in the yeti variant
This analysis took into consideration the two reward conditions for the Yeti variant, namely:
• Yeti smartphone: the participants could save up to keep the Yeti at the end of the
trial by avoiding traffic. They also received traffic information on the Yeti;
• Traffic information: the participants received traffic information on the Yeti, but
could not save up to keep it at the end of the trial.
Some of the differences between the various segments of the programme shown by the
Yeti participants were comparable to those seen in the monetary variant. Participants
who had a public transport alternative chose to use it more often, but this did not lead
to a reduction in the number of trips made during the rush-hour. Employees who were
able to begin work later appeared more willing to travel after rush-hour. This emphasizes the importance of the work organization for the reaction to reward arrangements.
These participants travelled less by public transport (-55%) and were more likely to see
teleworking as an option. Participants who were able to begin work earlier were less
likely to choose to work from home (-81%). Participants who were able to leave home
later due to family circumstances were also more likely to choose to work from home.
Furthermore, well-educated participants were more likely to avoid traffic, and more
likely to travel before the rush-hour. This may be due to the greater flexibility in
their work organization. The same applies to participants with higher incomes, who
were significantly less likely to take public transport (-87%).
Spitsmijden | Experimental design and modelling
43
Participants under 25 years of age rarely considered the option of travelling before
the rush-hour, while participants aged between 36 and 50 were more likely to
work from home. Men were more likely than women to avoid traffic.
Participants who used traffic information regularly (and did so even before the
trial) were more likely to travel before the rush-hour. Apparently, participants with
a positive attitude towards traffic information made more effective use of the Yeti
to adjust their trip to avoid heavy congestion.
Finally, it appeared that when the reward of a Yeti was no longer relevant, the
participants were less likely to travel after the rush-hour or to work from home.
When no reward was offered, the number of participants avoiding traffic decreased significantly (-46%).
Table 3.27: Effects on behaviour reaction chosen (Yeti variant) based on logistical regression model (exp())
Availability of
public transport
Can start work earlier
Can start work later
Can leave home later
Income > € 4,500
At least higher
professional education
Age <25
Age 36-50
Male
Uses traffic
information weekly
Reward not relevant
and traffic information
Traffic information only
Elasticities
Fewer trips
during rush-hour
Driving before
rush-hour
Driving after
rush-hour
Travelling by
Working
public transport at home
5.08
2.32
1.93
2.48
1.98
2.07
0.00
0.45
0.26
0.19
3.03
2.17
2.89
3.00
2.72
2.61
0.54
0.56
0.37
In order to perform simulations using the INDY traffic simulation model (see section
6), we created a model for the choice of travel method and time based on a stated
preference survey (D3, see section 4). Based on this model, we then determined valueof-time and value-of-delay data to use in INDY simulations.
In the questionnaire, the participants could choose between different means of transport (car, public transport, bicycle, other) or work at home. The car alternative had three
to five variants per choice set. The alternatives were defined based on the following
points:
• Car: travel time, reward, departure time and estimated transit time at the measurement point;
• Public transport: travel time, reward;
• Bicycle: not further specified;
• Working at home: not further specified;
• Other: not further specified.
44
Experimental design and modelling | Spitsmijden
We used these data to formulate the following choice model. This is a nested logit
model in which the five car alternatives have been grouped into one nest, and the
other alternatives into the other. The utility functions are:
Ucar = Ccar + β1 * cost+ β2 * time + β3 * reward + β4 * SDE+ β5 * SDL+
β6 * dep7.00 + β7 * dep9.00
UPT = CPT+ β1 * cost+ β2 * time + β3 * reward
Ubike = Cbike+ β2 * time + β3 * reward
Uhome = β3 * reward
Uother = Cother + β3 * reward
Ucarnest= 1.0 * log sum
Uothernest= θ
* log sum
Costs for the car and public transport were determined based on travel distance
(measured from departure to arrival postcode). The car costs were estimated at
14ct/km for fuel and maintenance. Public transport costs per kilometre were estimated using the formula ppkm=(0.45-0.011*distance). This formula was determined
based on twenty cases from the data. Travel time for car and public transport fol-
lows from the trial. The average speed of the bicycle was estimated at 15km/h. SDE
(schedule delay early) and SDL (schedule delay late) were based on the preferred arrival time stated by the respondents, taking the moment of transit into consideration.
Example: if the pre-rush-hour period continues to 07.00h, the travel time is r, then you
arrive at work at 07.00+0.5*r (assuming that you wish to arrive at work as late as possible,
the EVI registration was assumed to be in the middle of the trip). With a PAT of 08.00, the
SDE is therefore 08.00-07.00-0.5r. In reverse, if the post-traffic period begins at 09.00h,
you will arrive at 09.00+0.5r. Your SDL is therefore 09.00+0.5r-08.00. Dep<07.00 and
dep>09.00 indicate whether the departure time is before 07.00h or after 09.00h.
Estimates of this model are presented in Table 3.28.
Table 3.28: Estimated results of transport means choice and time model
Cpt
Cbike
Cother
time
reward
cost
SDE
SDL
dep<07.00
dep>09.00
θ
Adjusted rho-square
LL(O)
LL( β )
Parameter
2.734
1.712
0.631
-0.0220
0.249
-0.0833
-0.0147
-0.0137
0.00126
0.00123
0.199
0.49056
-536.9243
-487.4902
t-value
2.653
1.917
1.261
-2.057
5.165
-0.548
-3.540
-3.813
1.686
1.651
1.644
Spitsmijden | Experimental design and modelling
45
The coefficients all point in the same direction. Cost is not significant; apparently
the reward is more important, possibly because the cost differences are not that
great. The SDE and SDL parameters are very low, perhaps suggesting that the
participants can easily switch to other times in order to avoid congestion and earn
the reward. The dummies dep<07.00h and dep>09.00h suggest that early and late
departures are valued positively. These variables may represent a segment that
chooses to leave extra early or much later. Relevant VOTs are listed in the table
below.
Table 3.29: Value-of-time and value-of-delay
Cost-based
Reward-based
46
VOT
13.25
-4.55
Experimental design and modelling | Spitsmijden
VOSDE
8.67
-2.98
VOSDL
8.14
-2.80
4 SURVEYS
We gathered data in various ways for the Spitsmijden project. Besides recordings
made by the EVI system and using cameras, information was collected through
surveys.
The purpose of the trial was to study the way in which rewards given to individuals
who avoid the rush-hour can affect their behaviour. To do so, we required various
types of data:
• Data regarding behaviour before, during and after the trial;
• Data regarding factors that may influence the reaction to rewards, such as:
– socio-demographic characteristics;
– organization of work and family;
– use of telecommunication resources;
– motivation to participate.
We used a number of methods to gather these data. We have provided each dataset with a code that has been referenced in other sections:
• D1: after they had volunteered for the trial, between April and August 2006 the
participants completed a survey with information about their home and work
location, frequency of travel, etc. This survey was completed by 346 people, of
whom 340 eventually took part in the trial.
• D2: the participants also completed an extensive survey containing detailed
questions about their personal characteristics, household composition, and fac-
tors that could influence their reaction to rewards, such as flexible work hours,
family obligations, availability of alternative means of transport, etc. (Appendix
1). This survey could also be completed by non-participants. This survey was
completed by 473 people from Zoetermeer, including the 340 participants. If individuals completed the survey more than once, we used the most recent version
completed before 30 September 2006.
• D3: we also conducted a stated preference survey prior to the trial, in which participants stated how often they would avoid the rush-hour under hypothetical
reward and congestion situations. This survey was used primarily to determine
elasticities for the traffic models (Appendices 2 and 3). This survey was completed by all 340 participants.
• D4: logging. During the preliminary measurements, the trial and the post-measurement, the participants kept a log in which they recorded whether or not they
had made a trip to work (and if not, why not), which means of transport they
used and at what time they made their trip (Appendix 4). We used this information to gain insight into situations in which the participant was not recorded by
the EVI. It was necessary in these cases to know whether they had used some
other means of transport (public transport or bicycle) or whether they had not
made a commute due to vacation, illness, etc.
• D5: evaluation survey. In this survey, we asked questions about the participant’s
experience with the trial. This dealt on the one hand with the experience of be-
Spitsmijden | Experimental design and modelling
47
haviour adjustment (was it easy / difficult to adjust behaviour and which were the
most important factors). On the other hand, we asked about the experience with
the organization of the trial (provision of information, performance of the project
bureau, etc.) (Appendix 5). This survey was completed by all 340 participants.
The information from the surveys listed above and the registrations made by the
monitoring equipment were linked at the level of the individual so that this information would be easily available for each participant.
Aside from the data mentioned above, in February 2007 we held a survey among
residents of Zoetermeer who regularly travel to The Hague during the rush-hour
to determine whether the participants in the trial were representative of the total
population of rush-hour drivers from Zoetermeer to The Hague. We put similar
questions to these respondents regarding personal characteristics, household
composition and factors that may influence reactions to rewards, such as flexible
work hours, family obligations, availability of alternative transport, etc. (Appendix
6). This survey was performed by telephone among 262 residents of Zoetermeer.
48
Experimental design and modelling | Spitsmijden
5 NETWORK, TRAVEL AND TRAFFIC DATA
The traffic and economic models described in sections 6 and 7 rely on specific data
as input for calibration and prediction purposes. This section presents the data
sources used, classified into three main categories:
1. Network supply – the network infrastructure and its characteristics;
2. Travel demand – the number of trips between zones in the network;
3. Traffic data – the combination of travel demand and infrastructure supply yields
traffic with travel speeds, travel times, congestion, etc. as characteristics.
5.1 Network infrastructure
description
The trial was focused on the area around The Hague and Zoetermeer. The area is
bordered by Leiden to the north, Rotterdam to the south, Gouda to the east and
the North Sea to the west (see Figure 5.1).
Figure 5.1: Research area (source: Google Maps)
The area includes three main motorways, namely the A13 between Rotterdam and
The Hague, the A4 between Delft / The Hague and Leiden, and the A12 between
Gouda and The Hague. Because the Spitsmijden reward applied to people travel-
ling from Zoetermeer to The Hague, which is in the centre of the research area, the
impact area (i.e. the area the Spitsmijden reward scheme will affect) may be larger
than the research area. For example, changes on the A12 motorway can impact
trips from Utrecht to The Hague and therefore also traffic outside the research
area. However, the research area is expected to show the main effects of the
Spitsmijden rewards.
In order to be able to make computations related to the transport network (e.g.
about travel times, delays, queues, etc.), the characteristics of each of the infrastructure elements needs to be known. Instead of considering each existing road in
the network, for our purposes it suffices to include only the main roads, that is, all
motorways, all local roads and all major city roads. The road network considered in
the traffic model (see Chapter 6) is depicted in Figure 5.2. For the economic model
in section 7, we focus on only a part of the network, namely the A12 motorway.
The transport network basically consists of links (road segments) and nodes (connections between links, including intersections), as illustrated in Figure 5.3. In total
there are 1,891 links and 1,133 nodes in the network.
Spitsmijden | Experimental design and modelling
49
Figure 5.2: Transport network in traffic model
Figure 5.3: Transport network (close-up) consisting of nodes and links
For each link the following attributes are known:
• length of the road segment (km);
• maximum speed (km/h);
• number of lanes;
• capacity (veh/h);
• speed at capacity (km/h).
The model used is mainly for motorway traffic and does not take delays at intersections
into account, hence the data on intersections, whether or not signalized, are not used.
5.2 Travel demand
In this research, the focus was on the morning rush-hour (i.e. 06.00 –11.00h), in which
the main travel demand is for commuting trips from home to work. This travel demand leads to trips between zones within the research area (‘internal zones’), as well
as to and from zones outside the research area (‘external zones’). External zones are
included in the data by aggregating all travel demand to and from these zones at the
borders of the research area (i.e. an external zone for the northern region at Leiden,
for the southern region at Rotterdam and for the eastern region at Gouda).
In total there are 168 zones in the model, which can be both an origin and a destination (see Figure 5.4). There is a travel demand from each zone to each zone,
provided by the origin-destination (OD) matrix for the whole morning rush-hour.13
13
50
The OD matrix is based on the NRM (Nieuw Regionaal Model) by AVV.
Experimental design and modelling | Spitsmijden
Each cell of the OD matrix represents the total number of trips (= number of vehicles) being made from the origin zone to the destination zone during the morning
rush-hour. The total number of trips in the OD matrix is 473,868, of which 40,722
originate from Zoetermeer. Of these Zoetermeer-based trips, 8,475 have a destination in The Hague.
Figure 5.4: Centroids representing origin and destination zones
5.3 Traffic data
The trips between the zones in the transport network yield network traffic as
observed on the road and measured using loop detectors (see Figure 5.5). These
loop detectors are present on the motorways and on some other major roads. For
this research, we had access to the Regiolab Delft server,14 which stores traffic data
on a number of locations on the A4, A12 and A13 motorways (see Figure 5.6). This
server stores detailed traffic data on these cross-sections, for example, for each
one-minute time period data on:
• the flow (veh/h);
• the average speed (m/s).
The data are retrieved using the Regiolab Delft data viewer15 (see Figure 5.7), which
also automatically filters the data, removing any spurious observations.
Figure 5.5: Loop detectors embedded in the road (source: Google Earth)
14
See www.regiolab-delft.nl
15
Kindly provided by Hans van Lint, Delft University of Technology.
Spitsmijden | Experimental design and modelling
51
Figure 5.6: Location of loop detectors in Regiolab Delft area
Figure 5.7: Regiolab Delft data viewer (4 April 2006)
The Regiolab Delft data viewer not only retrieves and cleans the data, but also
generates trajectory plots and computes travel times. The top figure in Figure 5.7
shows the trajectory plot from Gouda to The Hague along the A12 motorway, while
the bottom figure indicates the travel times from Gouda to The Hague, all over a
period of 24 hours. In this case, it displays the data for Tuesday 4 April 2006. The
2006 data have been analysed and visually inspected in order to find an average
daily traffic pattern. Although the traffic patterns vary from day to day, there are
common patterns to be observed. The selected day (4 April 2006) seems to present a typical daily pattern in terms of flows and travel times. The travel time plot
in the lower figure shows that during the morning rush-hour there is significant
congestion on the section of the A12 from Zoetermeer towards The Hague, where
the free-flow travel time of approximately 17 minutes increases to a travel time
of somewhat less than one hour at around 08.00h. Note that the travel time on
the A12 from Zoetermeer towards The Hague is less (with a free-flow travel time
of approximately 8 minutes), as a large amount of the travel time and congestion
is between Gouda and Zoetermeer, which does not affect those travelling from
Zoetermeer to The Hague (see also the following). The trajectory plot in the top
figure in Figure 5.7 has been enlarged in Figure 5.8. In this trajectory plot, travellers
‘drive up’ (from Gouda to The Hague) and forward in time. Congested locations
and times are indicated by low speeds.
52
Experimental design and modelling | Spitsmijden
From this figure it can be easily seen that queues build up on the A12 at three specific locations, namely at:
1. on-ramps from Zoetermeer;
2. Prins Clausplein (or just after);
3. traffic lights at the end of Utrechtsebaan.
The traffic data are used to calibrate the models in sections 6 and 7.
Figure 5.8: Motorway A12 trajectory plot (4 April 2006)
speed (m/s)
The Hague
end Utrechtsebaan
30
Prins Clausplein
25
20
15
Zoetermeer
A12
10
Gouda
0.00h
5
6.00h
12.00h
18.00h
24.00h
Spitsmijden | Experimental design and modelling
0
53
6 ANALYSES WITH THE INDY TRAFFIC MODEL
A traffic model was used to forecast the traffic conditions that would result
from the introduction of a Spitsmijden reward scheme. The main aim was not to
forecast what would happen in the pilot, but what would happen if (a) a different
Spitsmijden reward scheme were used (e.g. larger or smaller rewards, or more
time-differentiated rewards) and / or (b) a larger number of people were to participate. Clearly, these forecasts extrapolate far beyond the pilot trial.
The traffic model is described in the following section. The model has several
parameters, which have been estimated and calibrated (see § 6.2). The different
case studies that are analysed are described in section 6.3, and their outcomes are
examined and discussed in section 6.4. It is important to note that the results presented here are preliminary results: the model is still being improved and the final
results may deviate from the results shown here, although the tendency should be
more or less the same. Therefore, in this section we only draw careful conclusions,
realizing that the results are outcomes of a model that is still being fine-tuned.
6.1 Model description
The INDY traffic model (see Bliemer et al., 2004; Bliemer, 2004) is used in the
analyses in this section. INDY (INtegrated DYnamic traffic assignment model) is a
macroscopic analytical simulation-based dynamic traffic assignment model that
simulates traffic on medium to large transport networks. It is macroscopic since
it assumes traffic flows instead of considering individual vehicles. Furthermore,
it simulates dynamic route choice behaviour of travellers in such a way that they
adjust their route choices based on their experienced congested travel times. INDY
is used for long-term forecasts as it computes an equilibrium state in which all
drivers choose their best route, assuming that all drivers have access to (perhaps
imperfect) information about the different route alternatives (e.g. from experience). Such an equilibrium state may not occur in real life due to ever changing traffic
conditions. However, such an equilibrium state enables the analyst to compare
different scenarios or variants from a reference scenario / variant, as it is the differences we are interested in. Our reference case consists of the current network
conditions (as observed in the traffic data described in section 5), and our scenarios / variants are different implementations of a Spitsmijden reward scheme with
different percentages of participants (see § 6.3).
INDY runs in the graphical user interface of Omnitrans. The basic INDY model takes
into account only route choice behaviour. However, the model has been extended
within the INDY / Omnitrans framework to include also departure time decision
and trip decision (making a car trip or not). This last decision includes the decision
to travel using a different mode (e.g. public transport, bicycle) and the decision to
work from home.
Introducing a Spitsmijden reward is likely to have an important impact on the departure time decision, as driving before or after the morning rush-hour is rewarded.
Direct routes changes are not to be expected, as there is no way for the participants
to be better off by taking a different route to work. However, due to changes in
departure times, the temporal pattern of travel times of road segments may change,
such that there may be some slight indirect route changes. Besides departure time
adjustments, one may expect the number of car drivers to decrease, as drivers are
also rewarded if they work from home or take a different mode of transport to work.
54
Experimental design and modelling | Spitsmijden
Once the travel choices of the travellers have been modelled, the cars are simulated on the transport network according to their chosen departure time and route.
This traffic simulation determines the flows, travel times and delays on each
road segment in the network. The outcomes of the simulation (in particular the
congested travel times) influence the travel decisions of the travellers (travellers
may reconsider their travel choices), such that the travel behaviour model and the
traffic simulation model are run in an iterative fashion until convergence towards
an equilibrium state has been reached (in which no traveller has the incentive to
change his / her travel decisions). If the Spitsmijden reward leads to less traffic in
the rush-hour period and therefore to decreasing congestion during this period,
some travellers may choose to travel in the rush-hour instead of off-peak. This
return-to-the-peak effect is explicitly taken into account in the model by means of
the iterative process.
The travel behaviour model and the traffic simulation model are outlined in more
detail in the following sections.
Travel behaviour model
The travel behaviour model describes travellers’ decisions on route choice, departure time choice and car trip choice.
Route choice
For a given departure time, car drivers are assumed to consider different route
alternatives as having certain attributes and to choose their subjective optimal
route. The route alternatives available to car drivers are determined by a pre-trip
route generation procedure, in which the most likely route alternatives are genera-
ted for each origin-destination (OD) pair (see also Bliemer & Taale, 2006). Each of
these routes is assumed to have a generalized travel cost. This route travel cost is
composed of two main components, namely the congested route travel time and
a possible additional monetary cost (which is negative in the case of a reward). For
each departure time, the reward / lack of reward will be more or less the same for
each route alternative16, hence the Spitsmijden reward scheme is not likely to have
a large impact on the travellers’ route choice decisions. Therefore, the congested
route travel time will be the main factor in their choice behaviour.
Mathematically formulated, the following generalized route travel cost function is used:
1
rs (k)= 1 τ rs(k)+  2 θ rs (k)+ ε
c mp
(k),
m p
m mp
mp
rs
rs
where c mp (k) is the (congested) route travel time for car drivers taking route p
rs
from origin r to destination s when departing at time k,τ p (k) is the route travel
rs
time for route p from r to s departing at time k, andθ mp (k) is an additional (moners
tary) cost on that route. The termε mp (k) is a random unobserved cost component,
which represents all other cost components. The sub-index m denotes the type of
driver, being either a Spitsmijden participant or not. If a driver is not a participant,
rs
rs
then θ mp (k)=0, else θ mp (k)  0 (a reward is a negative cost). The parameters
m1 and m2 are behavioural parameters to be estimated, which can be specific for
driver class m, being a Spitsmijden participant or not.
16
As the reward is granted whenever a vehicle is not detected crossing a certain screen line (where
detection points are located) during certain time periods, it may be that different routes lead to
different arrival times at such a detection point, leading to minor route preference differences for a
short amount of time around the time at which the reward scheme starts or finishes.
Spitsmijden | Experimental design and modelling
55
rs
2
Making some assumptions ( ε mp (k) ’s are independently extreme value type I distributed over all routes), the percentage of car drivers choosing route p from r to s for departure time k equals (according to the multinomial logit model; see McFadden, 1976):
rs
exp(-1c mp
(k))
rs
rs
rs
,
ψ mp (k)=Pr(c mp (k)  c mp' (k), p')=
rs
p' exp(-1c mp (k))
'
where μ1 is a scaling parameter (which is inversely related to the variance of the
random unobserved component). Multiplying this percentage by the total number
rs
rs
of car drivers from r to s at time k, Dm (k), yields the route flows f mp (k), that is,
3
rs
m
rs
rs (k)D (k) .
f mp
(k)= ψ mp
rs
This dynamic travel demand Dm (k) depends on the departure time choices of the
travellers (see the following section).
Departure time choice
We assume that car drivers first decide what time to depart, and then make the
route choice.17 The departure time choice alternatives are clear, namely different
departure time in the morning (in or outside the reward period). Similar to the
route choice model, each departure time alternative is assumed to have a generalized travel cost. This travel cost consists of three main components, namely the
average travel time to the destination, the average possible additional monetary
cost (which can be negative in the case of a reward) and penalties for deviating
from the driver’s preferred arrival time (and, in the case of non-participants, also
preferred departure time).
4
– rs
2
– rs
3
-
+
4
5
– rs
rs
1 τ (k)+ β θ
cm
( k) = β m
m mp (k)+ β m ( k- ζ ) + β m (k- ζ ) + β m (k+ τ (k) - ξ )
– rs
-
+
6
rs
+β m
(k+ τ (k) -ξ ) + ε m
(k) ,
– rs
– rs
θ
where τ and mp (k) are the average (flow-weighted) travel time and additional costs (or rewards) for type m drivers (participants or not) from r to s departing
at time k (sometimes referred to as the travel time skims and cost skims), respectively, viz.,
5
– rs
rs
rs
rs
τ– rs (k) =p f mp
(k) τ p (k) and θ mp (k)= f mp (k) θ mp (k).
p
rs
-
– max {ζ-k,0} is the time that the car driver departs earlier
Furthermore, (k-ζ ) =
+ –
than his / her preferred departure time, ζ , (k-ζ ) =
max{k-ζ ,0} is the time that the
rs
–
-–
– rs
car driver departs later than preferred, ( k+ τ (k) - ξm ) =
max{ξm- k -τ (k),0} is
the time that the car driver arrives earlier than his / her preferred arrival time, ξm , and
– rs
– rs
+–
( k+ τ (k) - ξm ) =
max{ k+ τ (k) - ξm,0} is the time that the traveller arrives later
rs
than his / her preferred arrival time. The term m (k) is a random unobserved
1 to β 6
component that represents all other cost components. The parameters β m
m
ε
are behavioural parameters to be estimated, and may differ for different classes of
drivers, m (in our case, participants and non-participants).
The preferred departure time of travellers, for both participants and non-participants, is calculated by subtracting the free-flow travel time from the preferred ar17
This assumption is not very restrictive and will most likely hold in many cases. Another approach would be to
consider the combined decision, that is, a driver chooses a route and departure time simultaneously. We opted
for the sequential approach for the sole reason that the INDY model could remain intact, being a pure route
choice model, while adding an extra departure time choice model next to INDY instead of putting it inside INDY.
56
Experimental design and modelling | Spitsmijden
rival time. So even if the arrival time profiles for all zones are equal, the departure
time profiles are different for each zone and destination. For example, long-distance trips depart earlier than short-distance trips.
The preferred arrival time, ξm, is typically not a fixed time instant, but presents
a distribution of preferred arrival times over the population of travellers (where
we have observed different preferred arrival time patterns for different car driver
– Pr(k= ξ ) be a (given estimated) probability density
classes, m). Let h ( ξm ) =
m
m
function over all preferred arrival times ξm . Then the number of car drivers that
will depart at time k from r to s is given by
6
– rs
~ rs
rs
D m(k,ξm )= hm( ξm )D m
, with k+ τ (k)= ξm ,
rs
where D m is the total travel demand of type m car drivers (participants or not).
rs
From this total travel demand, h ( ξm )D m car drivers prefer to arrive at time
m
ξm . In order to arrive at this preferred arrival time, these drivers have to depart at
– rs
time k such that after travel time τ (k) they arrive exactly at the destination at
– rs
rs
time ξm , i.e., k+ τ (k)= ξm . The total travel demand, D m, in terms of car drivers
is input as the OD matrix (described in § 5.2), and may change due to trip choices
of the travellers (see the following section on trip choice). The actual OD matrix is
different from the preferred OD matrix, as car drivers take the generalized cost in
Equation (4) into account. That is, they also take the travel time itself, a possible reward and other components into consideration. Again assuming that the random
rs
components m (k) are independently extreme value type I distributed over all
departure times, the percentage of car drivers (with a preferred arrival time
ξm ) choosing departure time k is given by
ε
rs
7
rs
rs
rs
ϕ mp
(k,ξm )=Pr(cm (k)  cm (k'),
k')=
exp(-2c m(k))
rs
exp(-2c m (k'))
k'
,

where 2 is a scaling parameter (which is inversely related to the variance of the
random unobserved component). Multiplying this percentage by the total number
~ rs
of car drivers preferring to depart at time k, Dm
(k,ξm ) yields the total number of
drivers that will actually depart at time k. Integrating over all preferred arrival
times, the total number of car drivers departing at time k (irrespective of their
preferred arrival time), is given by:
8
rs
Dm
(k)=ϕ
ξ
m
rs
m
~ rs
(k,ξ ) D
m
m
(k,ξ ).
m
Trip choice
rs
The total number of car drivers, D m may be influenced by the Spitsmijden reward
scheme due to travellers taking public transport or other means of transport, or
working from home, in order to receive the reward. Furthermore, the percentage
of participants affects the number of travellers in each driver class m. This participation percentage is given exogenously in the model.
The number of travellers choosing to take the car will be computed using a cost
elasticity of the car travel demand. If the costs for travelling by car go up (or
alternatively, if the reward for not travelling by car goes down), then the car travel
demand will go down.
Spitsmijden | Experimental design and modelling
57
The cost elasticity of car travel demand can be expressed as:
9
e=
~ rs
% change in car travel demand D m
rs
% change in car travel costs c m
.
rs
Therefore, if the costs c m
change (which is a weighted average of the generalized
travel costs, see Equation (4), over all departure time periods), the increase or
~ rs
decrease in the car travel demand D m
(see Equation (6)) can be computed using
elasticity e.
Traffic simulation model
The trip choice model, departure time model and route choice model result in
rs
route flows f mp (k), see equation (3). These route flows are input in the traffic
simulation model, which determines the traffic conditions over time on the road
segments in the transport network. Typical outputs link travel times τ a (t) link
flows uam (t) link volumes Xam (t) and link costs θam (t) for each link a and each time
of link entrance t, for car drivers of type m (participants or not).
The traffic simulation model is sometimes called a dynamic network loading model,
as it loads the route flows onto the network over time. In doing so, it takes congestion
into account, depending on the flows on each of the links. Using the link characteris-
tics – link length, maximum speed, number of lanes, and capacity (see also
§ 5.1) – the link travel times are computed using the number of vehicles on each link
at the time of link entrance, and then propagated to the end of this link when the link
travel time elapses. Mathematically, a dynamic system of equations is solved. For the
interested reader we refer to Bliemer and colleagues (2004) and Bliemer (2004).
Since the output is on a link level while the travel choices in the previous section
are all on a route (or OD) level, we need to compute route level outputs. The route
level travel times and travel costs are dynamic additions of link travel times and
costs of links that constitute the route. That is
10
rs
rs
rs
τ prs (k) = δ ap
(k,t) τa (t), and θ mp (k) = δ ap (k,t) θam (t),
a
t
a
t
rs
where δ ap (k,t) is a dynamic link-route incidence indicator, which equals 1 if link
a on route p from r to s is entered at time t when departing at time k, and equals
zero otherwise. These route travel times and costs are used again in the travel
behaviour model, see equation (1).
6.2 Model estimation and
calibration
Both the travel choice model and the traffic simulation model have parameters
that need to be determined such that the complete model produces as accurate /
realistic as possible outcomes.
Travel behaviour
model estimation
The main parameters to be estimated in the travel behaviour model are the α , β
and γ in the generalized cost functions, the scale parameters in the multinomial
logit models, 1 and 2 the cost elasticity for car travel demand, e, and the distributions of the preferred arrival times, ξ .
m
The parameters of the travel behaviour model have been estimated using outcomes of the stated preference survey (see § 3.6; Elasticities). Transferring the parameter estimates to our travel choice models yields the parameters listed in Table
6.1. The scale parameters are included in these estimates and are set equal to 1.
58
Experimental design and modelling | Spitsmijden
Table 6.1: Parameters used in the generalized cost function
Parameter
βm1 (time)
βm2 (reward)*
βm3 (early departure)
βm4 (late departure)
βm5 (early arrival)
βm6 (late arrival)
Non-participants
-0.0247
Participants
-0.0220
-0.2490
-0.0791
-0.0162
-0.0292
-0.0339
-0.0147
-0.0137
* A reward is added in the generalized cost function as a negative cost.
The distribution of the preferred arrival time (PAT) is derived from the same survey
(see § 4). Figure 6.1 presents the percentages of preferred arrival times as stated
by the participants in the pilot study, and is used to describe function hm (•) in
equation (6). There are two PAT distributions. One for the participants, which is
based on the Spitsmijden survey, and one for non-participants, which has been
calibrated to reproduce correct traffic conditions (see also § 6.2; Traffic simulation
model calibration). For the participants, most travellers prefer to arrive at around
08.30h. Note that there is a small group of travellers who prefer to arrive after the
morning rush-hour, at around 10.00 - 10.30h.
�����������������������������������
������������������������������������������������������������
������������
Figure 6.1: Preferred arrival time (PAT) distribution for Spitsmijden participants
���
���
���
���
���
���
���
���
��
��
��
����� ����� ����� ����� ����� ����� ����� ����� �����
��
�����
������������
�����
�����
�����
���
����������������������������������������������������
������������
����
��
����
���������������������������
�
��
����
��
����
��
Spitsmijden | Experimental design and modelling
����
��
�����
�����
�����
�����
�����
�����
�����
�����
�����
59
�����
�����
��������������
�
�
��������������������������������������������������������������
�������������������������������������������������
��
Figure 6.2: Preferred arrival time (PAT) distribution for non-participants
���
���
���
���
���
���
���
���
���
���
��
����� ����� ����� ����� ����� ����� �����
��
�����
������������
�����
�����
�����
�����
�����
�����
������������
���
���
����������������������������������������������������
������������
The
preferred departure time profile for the matrix totals (see Figure
6.3) resembles the
��
��
preferred arrival time profile. The preferred departure times are computed as the pre-
�������������������������������������������
ferred arrival time minus the minimum (free-flow) travel time. For individual OD pairs
��
��
����� �����
�����
�����
����� �����
�����
�����
�����
�����
in the model
the �����
pattern
can
be�����
quite�����
different,
depending
on the free-flow
travel time
������������
�����
�����
�����
����
����������������������������������������������������
���������������������������
������
Figure 6.3: Preferred departure������������
time (PDT) distribution for non-participants
��
����
��
������
������
������
����
��
�����
�����
�����
�����
�����
��������� �����
��
������������������������������������������������
�����
�����
�����
�����
��������������
����
��
�����
�����
������
�����
�����
������
�������������������������������������������������������������
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
���
�����
��������������
�������
��������
������������������������������������������������
�����������
�����������
The cost elasticity for car travel demand
(e) turned out to be very small, which
�����
�������������
means that travellers are not likely to change to other modes of transport or to
work
from home. Therefore, we set e�����
= 0 in the analyses presented in this report.
�����
����������������������������
Traffic simulation
model calibration
�����
�����
�����
�����
�����������
�����parameters that can be calibrated.
�������������
The traffic simulation model has many
Besides
�����
the parameters for the link characteristics (e.g. capacity), some general parameters need
�����
to be set as well (e.g. the minimum speed and the traffic density per lane). Furthermore,
�����
the
cells in the travel demand OD matrix
that are used as input may have to be altered
�����
(see § 5.2). In order to calibrate these parameters and OD matrix cells, the outcomes of
�����traffic simulation (for the reference scenario in which no Spitsmijden reward scheme
the
�����
�����
�����
����� (see §
�����
exists, i.e. the ‘current situation’) are compared to�����
the traffic�����
data collected
5.3).
��������������
The calibration process was carried out in two stages. In the first stage, we concen�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
Experimental design and modelling | Spitsmijden
��
��������
�����
����������������������������������������������������������
��������������
������������������������������������������������������������������
60
�����
�����������
�����
���������������������������
����
�����
������������������������������������������������������������������
����
��
��������������������������������������������������������������
������������������������������������������������������������
������������
���
��������
�����������������������
���
���
���
���
��
����� ����� ����� �����
��
�����
������������
�����
���������������������������������������������������
�����������
trated on calibrating the link flows in the network, such that the number of cars
�������������������������������������������������
������������������������������������������������������������
on the roads ������������
is more or less accurately replicated in the model. This is done using
��������������������������������������������������������������
a maximum likelihood estimation procedure which changes cells in the initial OD
matrix that was given in section 5.2. In the second stage, the static OD matrix is
split into ten-minute demand periods by changing the preferred arrival time pro��� travel time, densifile���
for non-participants and comparing the model results with
ties and flow measurements. This whole calibration process is a time-consuming
and
emerged from
���
���computationally intensive exercise. In the end, a traffic pattern
the model that represents the ‘current situation’ sufficiently accurately, suitable
for���
analysing case studies. Comparisons between the modelled
��� and measured traffic flows, as well as the modelled and measured traffic densities are made in Figure
6.4���
and Figure 6.5, respectively, which match quite well. For the
preliminary results
���
in this report, the spill-back option in the traffic simulator in INDY was disabled in
order to speed up computation times.18 Ignoring spill-back of congestion compli��
��
cates the calibration process, because the total delay caused by bottleneck needs
to be modelled in a single link. Even though the modelled travel times (see Figure
��
��
6.6) deviate
travel
times,
is the
the travel
�����from
����� the
����� measured
����� ����� �����
�����
����� it
�����
�����differences in
�����
�����
�����
�����
����
�����
�����
�����
�����
�����
������������
times from one scenario to the reference scenario that we are interested in, such
������������
that these deviations should not����������������������������������������������������
bias the results much. In further research we aim
������������
to improve the travel time predictions by including spill-back effects in the model.
����
�������������������������������������������
Figure
�� 6.4: Modelled and measured traffic flow over time
���������������������������
������
��
����
������
��
����
������
��
������
���������������������������
�����������
�������������
����
������
������
����
��
�����
�����
�����
�����
�����
�����
�����
��������������
�����������������������������������������
����� �����
�����
�����
�����
�����
�����
�����
�����
����� ����� ����������
�����
�����
�����
�����
�����
���
�����
�����
��������������
��������������
������������������������������������������������
Figure 6.5: Modelled and measured traffic densities over time
�����������
�����������
�������������
�������������
����� �����
�����
�����
�����������������������������������������������������������������
�����
�����
�����
�����
��������������
�����
�����
18
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
����� ��������������
��������������
Computation times are fairly long, as for each route choice iteration and for each
departure time choice iteration a ������������������������������������������������������������������
whole traffic simulation needs to be run, which in total
����������������������������������������������������������
can take up to a day of computation time.
���
���
���
����������������
�����
����������������������
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����������
�����
�����������
�����������
�������������
����������������������������
�������������������������������������������������������������
���
���
���
���
���
Spitsmijden | Experimental design and modelling
���������������������
������������������������
����������������������
61
����
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
��������������
�����
��������������
����������������������
�����������
�������������
�����������
Figure 6.6: Modelled and measured travel time from Zoetermeer to The Hague
�����������
�������������
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
��������������
6.3 Case studies
��������������������������������������������
�����
�����
�����
�����
�����
�����
��������������
We examined different case
studies in order to investigate the effect of the partici����������������������������������������������������������
pation level and the level of the reward on travel behaviour and traffic conditions.
��������������������
���
In the scenarios
we have changed the participation level of travellers from Zoeter���������������������
���
meer to The
Hague
to levels of 10%, 50% and 100%, respectively.
The reference
������������������������
���
����������������������
cases also���
use these participation levels while no reward for
avoiding the rush���is used. Note that the participation is limited to those travelling from
hour period
���
Zoetermeer
��� towards The Hague, as the Spitsmijden reward scheme is tailor-made
���
for these travellers
by providing a reward on the A12 motorway.
���������������
�������������������
����������������������
���
���
The rewards
���distinguished are € 3, € 5 and € 7, which is consistent with the rewards
used in the���
pilot study. These are rewards to be earned if travellers are not detected tra���
velling from
Zoetermeer towards The Hague between 07.30 and 09.30h. For the model
���
���rewards are assumed to be fixed and do not vary over time, although the
studies, the
model does��allow time-varying rewards. This will be examined in further research.
�
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
Table 6.2 summarizes the different case studies with varying participation
levels
��������������
and reward levels. Case studies 1, 2b and 3 are analysed in order to draw conclusions about the effect of the participation level on travel behaviour and traffic
�����������������������������������������������������������
conditions. Case studies 2a, 2b and 2c are analysed in order to draw conclusions regarding the effect of the reward level. Cases 0a, b and c are the reference scenario,
��
which
serves as the basis for comparison. Note that the reference scenario is not
the ��
same for different participation levels, as the PAT profiles and the travel behaviour parameters are different for participants and non-participants. The model
��
results and these analyses are presented in the following section.
����������������������������������
�������������������������������������
��
��
Table 6.2: Case studies with different levels of participation and rewards
�������������
�������������������
�������������
62
��
Case study
Participant level
Reward
��
0a (reference)
10%
€0
0b (reference)
50%
€0
��
0c (reference)
100%
€0
��
1
10%
€5
2a �
50%
€3
������������������
�������������������
2b
50%
€5
2c
50%
€7
3
100%
€5
Experimental design and modelling | Spitsmijden
�������������������
����������������������
����������������������������������������
����
�������������������������������������������������������������
6.4 Model results
The case study results are presented in the following sections. First, the effect of
the participation level is analysed in section 6.4 ‘Effect of changing levels’. Then
the effect of different reward levels is examined in section 6.4 ‘Effect of changing
reward levels’. Finally, the model outcomes are discussed (section 6.4; Discussion
and further research).
Effect of changing
levels of participation
Figure 6.7 presents the results of different participation levels on the total network
travel time (for both participants and non-participants). A participation level of
50% generates travel time losses, while a participation of 10% and 100% both lead
to travel time savings.19
Case study 1 (10%) causes a small amount of traffic to shift departure times, which
leads to travel time savings for participants because they travel in less busy periods. The reduction of traffic during the morning rush-hour (07.30 – 09.30h) also
causes travel time savings for the non-participants.
In case study 3 (100%), a large group of participants change departure time (see
Figure 6.8). This again leads to travel time savings for participants, although less
per participant because the shift of large groups causes congestion before and
after the rush-hour. Demand during the rush-hour is much lower, which results in
improved travel conditions.
Case study 2b (50%) causes the worst of both worlds. The group that shifts is large
enough to cause some delay for the participants themselves, but mainly the traffic
condition at the start of the rush-hour, combined with a still high level of demand
causes traffic conditions to worsen for the travellers inside the rush-hour. The impact on the travel times is shown in Figure 6.9, which again illustrates the worsening of traffic conditions before the rush-hour while improving traffic conditions
during it. The total travel time savings clearly depend on the travel time increases
and the number of travellers: if the increase in total travel time outside the rushhour period is larger than the decrease in total travel time in the rush-hour period,
then there are no improvements of the total system in terms of travel time.
It should be noted that the results presented are long-term effects in which
non-participants who were previously not driving in the rush-hour, or even took
another route, may now choose to travel in the rush-hour on the A12 motorway,
as traffic conditions have improved during the rush-hour period. The reverse may
happen as well. All these effects together make it a complex task to forecast the
effects of such a reward scheme on the traffic conditions (which is the reason why
this modelling exercise has been performed). We can conclude from Figure 6.7
that the results definitely depend on many different travel behaviour factors. A
closer look at all the factors influencing the traffic conditions will be taken in the
upcoming research.
19
It should be noted that, although 200 hours of total travel time savings seems a large
saving, it is less than 1% of the total network travel time. However, these savings may be
substantial on an individual basis.
Spitsmijden | Experimental design and modelling
63
������������
������
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
��������������
�����
�����
�����
�����
�����
�����
��������������
�����
�����
�����
�����
��������������
Figure 6.7: Effect of participation level on total network travel time savings (with € 5 reward)
�������������������������������
�����������
�����������
�������������
�����
������������������������������������������������������������������
�������������������
��
�����
�����
�����
�����
���
�������������������������������������������������������������
��������������������
�����
���
�����������
�������������
���
���
�����
��
�����
�������������������
�
������������������
�����
�������������������
���
�����
�����
�����
�����
�����
��������������
�����
�����
�����
�����
�����
�����
����������������������
��������������
����
���
���
���
���
���
���
���
���
���
���
���
���
���
���
���
��
�
�����
�
�����������������������������������������������������������
����������������������������������������������������������
�����������������������������������������
��������������������
�������������������������������
Figure 6.8: Departure time adjustments of participants in case study 3
�������������
�������������������
����������������������
���
���
���
���
���
���
���
���
���
���
���
���
���
���
��
���
���
���
�
���
���
���
��
�
����
���������������������
������������������������
����������������������
����������������������������������
��
�����
�������������������
��
��
��
��
��
��
��
�������������������
�������������������
��
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
��������������
�������������
��
�
�����������������������������������
�����
Figure 6.9: Travel times on A12 from Zoetermeer to The Hague for different partici�����������������������������������������������������������
��
��
��
��
��
��
��
��
��
��
�
��
�
��
�
�������������������
�������������
�������������������
��
����������������������������������������
�����������
��
�������������������
����������������������������������
pation levels (with € 5 reward)
��
�
���������������������
��
��
�
��
Experimental design and modelling
| Spitsmijden
��
��
����������������������
��
��
��
��
�������������������
�������������������
���������������������������
�����������������������������
���������������������������
����������������������
������������������������������
Figure 6.10 depicts the percentages of participants that receive a reward. This
�������������������
shows
�� a decreasing percentage of participants receiving a reward. However, the
��
��
��� ���� ��� ���� ��� ���� ��� ���� ��� ���� ��� ���� ��� ���� ��� ���� ���� ����� ���� ����� ����
������������������
��
��
��
����������������������������
�����������������������������
64
��
��������
���
���
���
���
���
��
�
�������������������
�������������������
����������������������
��������������������������������������������������
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
�����
total number of participants increases more rapidly than the percentage
in reward
��������������
decreases.
�����������������������������������������������������������
����������������������������������
Figure 6.10: Effect of participation level on number of rewards received (with € 5 reward)
�������������������
��
��
��
��
��
��
��
�������������������
��
�������������
��
�
������������������
�������������������
�������������������
����������������������
Effect of changing
reward levels
Changing the level of rewards causes a similar effect as changing participation
level (see Figure 6.11). A small number of participants changing their departure
time can alleviate the congestion for many: the € 3 case study 2a. If too many
people change, they cause congestion for themselves and for others: the € 5 case
study 2b.
Finally, some travel time saving can be achieved at even higher levels of participation. In case study 2c, the participants achieve some travel time savings for themselves, but leave much congestion behind for the non-participants who travel later.
Figure 6.12 shows the changes in travel times for different reward levels. All reward
levels suggest an increase in the travel times before the rush-hour and a decrease
in the rush-hour period. However, the combined effect of these increases and decreases on the total travel time savings as in Figure 6.11 depends on this trade-off
and many other factors, as mentioned in the previous section.
Higher reward levels are expected to trigger larger behavioural effects. This is
shown in Figure 6.13. As the reward level increases, the percentage of participants
who change their behaviour to travel outside the rush-hour also increases. Note
that this percentage does not include participants who already travel outside the
rush-hour and who also receive a reward even without changing their behaviour.
The increase in change in behaviour to travel outside the rush-hour is larger between case 2a and case 2b than it is between case 2b and case 2c.
Spitsmijden | Experimental design and modelling
65
�
�����
�����
������������������
�����
�������������������
���
��������������
����������������������
����
���������
�����
Figure 6.11: Effect of reward level
on total network travel time savings (with 50%
�����������������������������������������������������������
participation)
���
�������������������
���
��
����������������������������������
�������������������
�������������������������������
�����������
�������������
��
���
��
��
���
��
����
�
�����
�
�
�����
��������������
�������������������
���������������������������
�����������������������������
���������������������������
������������������������������
���������������������
�������������������
��
��
��
��
��
��
��
��
�
�
�
�
�����
��� ��� ���� ���� ��� ���� ��� ���� ��� ���� ��� ���� ��� ���� ��� ���� ���� ����� ���� ����� ����
����������������������
��������������
���������������������������
����������������������������
�����������
����������������������������
����������������������������
����������������������������������������������������
����������������������������������������
Figure 6.13: Effect of reward level on number of rewards received (with 50% participation)
��
��
��
��
��
��
��
��
�
�������������������
����������������������
66
��
��
��
��
��
����������������������������
�����������������������������
�
�����
��
��
��
�����
��
����������������������
�������������
Figure 6.12: Travel times on A12 from Zoetermeer to The Hague for different reward
levels (with 50% participation)
���������������������
������������������������
����������������������
��
�������������������
�������������������
����
��� ���� ��� ���� ��� ���� ��� ���� ��� ���� ��� ���� ��� ���� ��� ���� ���� ����� ���� ����� ����
����
���������
�����
���
���
���
���
���
��
�
�������������������
�������������������
Experimental design and modelling | Spitsmijden
�������������������
�������������������
�������������
�
�
Discussion and
further research
The model results suggest that there is a trade-off between the number of participants who can earn a Spitsmijden reward and the level of the reward. A high level
of participation with high rewards, which in reality would correlate if participation
is voluntary, will probably lead to delays before the rush-hour. These delays and
queues will negatively impact other travellers as well, resulting in net travel time
losses for the whole network. In practice, this combination of high reward and
high participation would also be very expensive.
The greatest travel time savings can be achieved by shifting a number of travellers
that is low enough that they do not cause congestion for themselves or others,
while decreasing demand in the rush-hour to below capacity, thus solving the
bottleneck. In the cases examined here, the € 3 and 50% participation (case 2a) or
the € 5 and 100% participation (case 3) lead to the largest total travel time savings.
Although there may be an optimal combination that would yield the best traffic
conditions for all car drivers, we cannot conclude this from the few case studies
presented in this report. Additional case studies may provide more insight into this
trade-off. An interesting question would then be what the best combination of
level of participation and level of reward would be, given a certain budget.
These case studies present some preliminary results from a network modelling
exercise, and further research is needed to be able to draw more definite conclu-
sions. Including the trip choice elasticities in the model, running the model in a
spill-back mode and running more case studies would be the next step in order to
investigate the impact of a Spitsmijden reward scheme on travel behaviour and
traffic conditions. The fixed time period for receiving rewards that is currently used
in the different cases is also a topic of research. The welfare optimal rewards as
calculated by the Free University (see Chapter 7) could then be taken into account
as well.
The model can also be further improved by also considering travel time unreliability and junction delays on urban roads. Of further interest is what such a
Spitsmijden reward scheme would be like were it open to all travellers in the whole
area or even the whole country, and what impact this would have on the traffic
conditions. The impacts simulated in this study are local impacts on a small part
of the transport network, therefore affecting only a proportion of all travellers. A
broader implementation would yield effects on a much larger scale.
Spitsmijden | Experimental design and modelling
67
7 ECONOMIC QUEUING MODEL
7.1 Introduction
A model is a very handy tool if one wishes to understand the causes of the traffic
congestion from Zoetermeer towards The Hague during the morning rush-hour.
The model should provide a description of the main features of this phenomenon
and allow one to get an idea of the effects of measures that are intended to
change it, for instance rewarding drivers who avoid the morning rush-hour.
In this section, we argue that a specific economic model – the bottleneck model
– provides a useful description of traffic congestion in this area. We do so by showing that the traffic delays that occur can be attributed to the limited capacity of a
specific part of the A12 between Prins Clausplein and the centre of The Hague. This
observation is of some interest because there is an alternative economic theory of
congestion that does not emphasize the effect of bottlenecks, but concentrates on
the density of traffic on a road. Moreover, even for commuters who use this part
of the A12, it is unclear where the bottleneck is located. However, the data that are
available to us identify its location.
The observation that most of the traffic congestion from Zoetermeer towards The
Hague can be attributed to the limited capacity of a particular section of road
indicates the use of the bottleneck model. This model predicts that the relation-
ship between the total travel time of commuters and the moment they pass the
bottleneck has particular properties. Indeed, the data confirm this prediction to
a reasonable extent. This intimates the use of tolls or rewards as suggested by
this model in simulations with the much more extended INDY traffic engineering
model. However, the complexity of the INDY model – which provides a much more
detailed picture of traffic congestion in the relevant area than does the bottleneck
model – complicates the derivation of optimal tolls. The main function of the
bottleneck model is therefore its use as a complementary tool for traffic analysis.
In the following sections we provide a brief description of the bottleneck model.
Section 7.3 discusses the compatibility of the data with this model. In section 7.4
the parameters of the model are calibrated on the basis of the data and optimal
tolls are derived. Section 7.5 presents some conclusions.
7.2 The bottleneck model
The bottleneck model originates from Vickrey (1969) and was further developed
by Arnott, de Palma and Lindsey (1990, 1993). As the name indicates, this model focuses on the effects of a bottleneck on traffic flows. As such, it is the main alternative to the flow congestion model pioneered by Pigou.
The model provides a highly stylized description of traffic flows through a single
link with a bottleneck at the end of the link. The capacity of the bottleneck (the
number of cars per time unit that can pass though it) is smaller than that of the
road segments leading towards and from it.
A homogeneous population of workers uses the link for commuting. All these workers would like to pass through the bottleneck at the same time (t*), so as to arrive
at work exactly on time.20 However, since the capacity of the bottleneck is limited,
it is inevitable that some workers will be either too late or too early.
20
We assume here the travel destination to be located right behind the bottleneck so that
the time of passage through the bottleneck is equal to the arrival time at destination.
68
Experimental design and modelling | Spitsmijden
Since the workers all want to arrive at the preferred arrival time, a queue will form
in front of the bottleneck. The result is that workers who arrive at work close to
their desired time have to spend some time in the queue. Due to the presence
of the queue, the attractiveness of arriving close to the preferred arrival time (t*)
diminishes, and arriving earlier or late becomes more acceptable. In equilibrium all
drivers reach the same utility: the disutility of having to spend some time in the
queue for those who arrive at work exactly on time is equal to that of those who
arrive early or late and have to spend less time in the queue.
Formally, the utility function is specified as:
u=- α tt-β max{0,(t*-at)}-γ max{0,(at-t*)},
where the Greek letters are parameters, tt denotes travel time and at arrival time.
The utility function contains three terms. The first refers to the disutility associated
with travel time. Since free-flow travel time is, by assumption, equal for all commuters, differences in tt are only due to differences in the amount of time spent in the
queue. Thus, one may think of tt as referring to time spent in the queue only. The
parameter α is the value of time. The second expression indicates the loss of utility
associated with arriving at work early. The scheduling delay early is the maximum of
0 (indicating that this term vanishes when the worker does not arrive early) and the
difference between preferred and actual arrival time. The parameter β indicates the
loss in utility associated with arriving at work one time unit early. The interpretation
of the third term in the utility function is similar for arriving at work late. The parameter γ indicates the loss of utility caused by arriving at work one time unit late.
In equilibrium all commuters experience the same disutility, which means that
the gain in utility associated with having to spend a shorter time in the queue is
exactly compensated by a larger disutility associated with schedule delay. Figure 7.1
illustrates the relationship between arrival time (indicated on the horizontal axis)
and time spent in the queue. The triangle reaches its peak for workers who arrive
at work exactly on time, and therefore have no disutility associated with scheduling delay. The slope of the lines AB and BC reflects the values of the scheduling
delay cost parameters β and γ. In drawing the figure, we assumed that β <γ, which
seems reasonable, and for this reason BC is steeper than AB.
�����������������������������������
Figure 7.1: Equilibrium in the bottleneck model
�
��������������������������������
�������������
�
�
��
������������������������������������
Spitsmijden | Experimental design and modelling
����
69
���
���
���
���
���
���
���
���
���
���
���
���
���
���
���
��
�
7.3 The data and the model
A large number of commuters use the part of the A12 highway that links Zoetermeer
with The Hague for their daily home-to-work trip, which leads to severe congestion
problems. Since it was not clear that this congestion is associated with a particular
bottleneck, we examined our data so as to verify that this is indeed the case.
These data originate from measurements by a number of detectors of the number of
passing vehicles and their speed. These detectors are listed in Table 7.1. The numbers in the
first column indicate the location of the detectors. The numbers correspond roughly to
the distance to the centre of The Hague. The detector with the highest number (see the
first line in the table) is therefore located furthest from The Hague, while the one with the
lowest number (see the last line of the table) is located closest to the centre of that city.
The second column of Table 7.1 gives the number of lanes of the A12 at the location
of the detector. In all cases, traffic flows and speeds were measured on all lanes.
The third column indicates the presence of an on- or off-ramp between the location of the previous detector (corresponding with the previous line) and the one
associated with the line on which the ramp is indicated.
It is clear from the table that the A12 between Zoetermeer and the centre of The
Hague does not correspond with the simple network assumed in the bottleneck mo-
del. There is traffic that originates from places located to the east of Zoetermeer. There
are a number of ramps through which traffic leaves or joins the A12. Perhaps the most
significant of these are the ramps associated with Prins Clausplein, which is a highway
crossing. Here, traffic from Zoetermeer heading towards Amsterdam or Rotterdam
leaves the A12, and traffic from these cities heading towards The Hague joins the A12.
Table 7.1: Observation points from Zoetermeer towards The Hague
Location detector
17645
17045
16505
15765
(15185)
14590
14095
13590
13045
12555
12125
11715
11295
10845
(10345)
9875
9390
(8845)
8385
7897
7375
6855
6340
70
Number of lanes
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
4
4
2
2
Experimental design and modelling | Spitsmijden
On- / off-ramps
Off-ramp to Zoetermeer
On-ramp from Zoetermeer
On-ramp from Zoetermeer Centrum
Off-ramp to petrol station
On-ramp from petrol station
Off-ramp to Nootdorp
On-ramp from Nootdorp
Off-ramp to Prins Clausplein
�����������������������������������
��������������������������������
�
On-ramp from Prins Clausplein
Off-ramp to Voorburg
���
The
made available to us as traffic flows
���data collected by these detectors were �������������������
��������������������
���
and average speeds at five-minute intervals���������������������
from 06.00 to 10.00h for a typical day
���
(4 ���
April 2006; see § 5.3). To see if a bottleneck is present, we show the development
���
of ���
speed in this time interval for the seven detectors that are closest to The Hague.
���
The result is shown in Figure 7.2.
�
��
����
������������������������������������
���
���
���
This
���figure shows that traffic speed is almost constant during the whole time
���
interval
at the three detectors located closest to The Hague (nos. 3645, 4045 and
���
4645).
��� This suggests that on this part of the A12 capacity problems are negligible.
��
�
��� two���detectors
���
���that���
���
��� furthest
���
����
At the
are located
from����The ����
Hague (nos. 5830 and
����������
6340) there is a sudden drop in traffic speed just after 07.00h and an equally sud-
���������������������
den return to free-flow
speed at around 09.45h. This pattern is consistent with the
�����������������������������������������������������������������
���������
emergence of a queue in front of a bottleneck when its capacity is exceeded at the
��
beginning of the morning rush-hour and it disappears
at the end of this period.
�����������
�������������
��
����
�����
�����
�����
�����
����
This suggests that the bottleneck must be located between detector number 4645
and detector number 5835. This is the part of the A12 on which traffic from the
A4 flows ��in from Prins Clausplein and traffic to Voorburg flows out. There are two
detectors on this road segment (5500 and 5220). Figure 7.2 shows that during the
���������������
�� travel speed there is between the free-flow value and the much lower
rush hour,
��������������
value registered at the detectors upstream of this road segment. This is consistent
���������������������������
with the interpretation
of this road segment as a bottleneck: in the bottleneck,
�
traffic speeds up to the free-flow level.
�
�
�
�
�
�
��
��
Figure 7.2: Travel speeds during the morning rush hour
between Prins Clausplein and
����������
�����������������������������������������������������������������������
The Hague
������
��������������
����������������
������
�������
�������
��������
�������������
�
�
∆
��
�
Continuation of Table 7.1
5835
2
5500
4
5220
4
4645
3
4045
3
�����������������������������������
3645
3
��
��
��
����
����
��
����
����
����
�
����
����
�
�����
�����
�����
�����
�����
Spitsmijden | Experimental design and modelling
�����
����
71
�������������
������������������
���
��
���
��
���
Figure 7.3 is a stylized map of the road segment identified as the bottleneck. Travel
flows are from the east (right-hand side of map) to the west (left-hand side of
���
map), as indicated by the arrow. The eastern part of the map shows���������������
that the two
��������������
lanes with traffic originating from the A4 join the traffic from the two
lanes from
���������������������������
the
� A12. Of the four lanes that result, one becomes the off-ramp to Voorburg.
Detectors 5220
and 5500
are both�����
located on�����
the part of
the road where there are
����
�����
�����
four lanes. On this road segment, vehicles from the A12 that are heading towards
����
Voorburg have to cross two or three lanes to reach the off-ramp, while vehicles
from the A4 heading towards The Hague have to leave the right-hand lane to avoid
entering the off-ramp to Voorburg. There is therefore a relatively large amount of
traffic weaving on this road segment, and this is probably a main reason why it
acts as a bottleneck.
��������������
��������
��������
�������
���������
����������������
������
�������
�������
��������
Our conclusion that there is a bottleneck on the A12 between Prins Clausplein
and the Voorburg off-ramp is confirmed by other evidence. The bottleneck model
assumes that the capacity of the bottleneck is given. Figure 7.4 shows the flows
through the bottleneck. The number of vehicles passing through the bottleneck
fluctuates somewhat between subsequent five-minute time intervals, and the
average flow size seems to be somewhat lower in the second half of the rush-hour.
However, it seems adequate to summarize the figure by stating that during the
rush-hour the number of cars passing through the bottleneck per unit of time
remains constant.
�������������
Figure 7.3: The bottleneck
��
��
��
��
�
Moreover, during the rush-hour the ratio between the flow entering the bottle-
neck from the A4 and the flow entering from the A12 is approximately constant,
as should be expected when a stable weaving pattern exists. Such stable weaving
patterns arise more or less naturally when traffic flows from different lanes have
to merge. Figure 7.4 shows that throughout the rush-hour the share of traffic entering the bottleneck from the A4 is larger than that entering it from the A12.
A second piece of evidence that confirms the presence of a bottleneck on this road
segment emerges when we compute the travel time between Zoetermeer (detector no. 14590) and the bottleneck. The bottleneck model suggests that the pattern
of travel times is a triangle, like the one shown in Figure 7.1. To verify this, we approximated the travel times implied by our data. The result is shown in Figure 7.5.
72
Experimental design and modelling | Spitsmijden
�
�����
��
�
�
�
�
�
��
����
������������������������������������
���
���
���
��
���������������������
����������������������������������
Figure 7.4: Traffic flows through the bottleneck
���
���
���
���
���
���
��
�
���
���
��
���
��
���
���������������
��������������
���������������������������
���
�
����
�����
�����
�����
�����
�
����
Figure 7.5 shows our computed travel time and a triangular approximation. The
figure shows that during the majority of the morning rush-hour, the triangular
approximation works well. It is only during the peak of the rush-hour (around
��������������
09.00h) that actual travel times on the A12 are lower than those suggested by the
bottleneck model.
������
��������
��������
����������������
��
�
�
�������
�������������
A probable explanation for this is that around 09.00h the queue in front of the
��
bottleneck extends beyond the location of detector 14590. If this is the case, the
amount of time spent in the queue on the A12 is larger for those who joined this
highway
east of Zoetermeer. For the commuters in Zoetermeer the
�������
�������presence of
��
���������
��������
a queue at the on-ramp means that joining the highway is more difficult. The
capacity of the on-ramp therefore decreases, with the result that a queue forms
on the ramp and, perhaps, on the local road leading towards the ramp. This means
��
that also for these drivers, the amount of time spent in the queue is larger than
suggested by Figure 7.5.
��
With this explanation for the measured travel time around 09.00h, it appears that
the pattern of travel times during the remainder of the rush-hour is extremely well
approximated by the triangular pattern suggested by the bottleneck model. We
�
conclude, therefore, that using the bottleneck model as a first approximation of
traffic congestion on the A12 from Zoetermeer towards The Hague is justified on
the basis of the properties of the data at our disposal.
�
�����
Spitsmijden | Experimental design and modelling
73
���
����
���
���
���
��
�
���
���
���
���
���
���
���
���
����
����
����
����������
Figure 7.5: Travel time from Zoetermeer to the bottleneck during the morning
rush-hour �����������������������������������������������������������������
���������
���������������������
��
�����������
�������������
��
��
���������������
��������������
���������������������������
�����
�
����
�
�
�
�
�
�
��
��
����������
�����������������������������������������������������������������������
������
������ of the model
7.4 Application
�������
Capacity of the bottleneck
Total demand
�������
��������
�������������
�����
��
��
��
��
Parameters of the
utility function
Between 07.00 and 09.30h, an average of 587 vehicles pass through the bottleneck
every five minutes. We noted above that most of these vehicles enter from the A4.
The average number entering from the A12 is 255 every five minutes.21
The total number of vehicles that pass through the bottleneck during the morning
rush-hour having entered from the A12 is equal to the product of the capacity (255)
and the length of the rush-hour (measured as the number of five-minute intervals). This length equals 30. Total demand is therefore equal to 7659.
The utility function is defined in section 7.1 as:
��
�
�
u=- α tt-β max{0,(t*-at)}-γ max{0,(at-t*)},
with α the value of time (vot), β the disutility of schedule delay early and γ the
disutility of schedule delay late. The value of time α in the Netherlands is € 7.50
per hour on average. Given this figure, we can compute the other two parameters
on the basis of the triangular approximation in Figure 7.5.
Table 7.2 provides further information about this triangular approximation. The prearrival time
t* corresponds to �����
the peak of the triangle
�����
����� that occurs at 09.07h.
�����
For the driver who passes through the bottleneck exactly at that time, the disutility
of the commute is equal to the product of the vot and the delay caused by the
presence of the queue. Using the triangular approximation results in a disutility of
€ 2.25. Note that this is based on a delay of 25 minutes, of which a part (approxima-
ferred
�����
21
This means that every five minutes, 255 cars leave the queue during the morning rush
hour. The number of cars that join the queue can be computed on the basis of the values
of α and β , as being equal to 309 before the peak in the travel time and 159 after that
peak. The computation uses equation 6 of Arnott et al. (1990).
74
Experimental design and modelling | Spitsmijden
����
����
����
����
����
����
����
�����
����
tely five minutes) is realized before the driver passes the detector at 14,590.
The drivers who pass through the bottleneck at other times experience the same
disutility, since their schedule delay costs compensate exactly for the lower travel
time. For the drivers who pass the bottleneck at 07.24 or at 09.36h there are only
schedule delay costs. It is easy to compute then that β must be equal to € 1.32 per
hour and γ to € 4.50 per hour. It may be noted that γ is smaller than the vot, which
suggests that for the drivers on the A12 schedule delay late is less of a problem
than spending more time in the queue.
Table 7.2: The approximated triangular travel time curve
Approximation
7 minutes
25 minutes
9.1 hours
Free flow travel time
Maximum travel time
Preferred arrival time
Optimal fine toll
The optimal time varying toll is equal to a-(t*-t) β for vehicles passing through
the bottleneck before the preferred arrival time t* and a-(t*-t) γ for vehicles that
pass through it later (see Arnott, de Palma & Lindsey, 1990, p. 118). The value of
a can be chosen by the policy maker, but has to be at most equal to 2.25 for the
values of the parameters we use. If we give a this maximum value, then the driver
who passes through it the bottleneck at the beginning of the morning rush-hour
receives a reward of € 2.25, as does the driver who passes through it at the end of
the rush-hour. Drivers who pass through the bottleneck between the beginning
and the end of the rush-hour receive a lower reward, and drivers who pass through
exactly at 09.07h (and thus arrive at work at the preferred time) do not receive
anything. It is clear that in this case the toll is in fact a reward: nobody has to pay,
and all except the drivers who arrive at the preferred time receive some money.
It may be observed that the maximum value of the fine toll is relatively low in
comparison to the rewards that were used in the trial (€ 3 or € 7). The same observation can be made for the coarse tolls that are discussed in the sections below.
This suggests that rewards lower than those used in the trial might be optimal.
Optimal coarse toll
Since it is difficult to implement a time varying toll, researchers have considered
the possibility of using a coarse toll, which is a toll (or reward) whose value does
not change over time. Using the results of Arnott, de Palma and Lindsey (1990,
p. 120),22 we find that in the case considered here the optimal coarse toll equals
€ 1.28, and that it should be introduced at 08.05h and withdrawn at 09.24h. This
toll can be easily transformed into an equivalent reward. This reward equals € 0.84
and is given to drivers who pass through the bottleneck between 07.24 and 08.05h,
or between 09.24 and 09.36h. No reward is granted outside this time period.
Optimal rewards in
fixed time intervals
It is also possible to determine optimal tolls or rewards for predetermined time
intervals. This is done by computing the minimum value of time spent in the
queue by drivers who pass through the bottleneck in a particular thirty-minute interval. If the tolls during the time intervals are set equal to these levels, the length
22
The authors only consider the case in which γ exceeds the vot. For this reason it is not
completely clear whether their formulas can also be applied in the present case. This
issue has to be investigated further.
Spitsmijden | Experimental design and modelling
75
of the queue will be reduced, but the capacity of the bottleneck will still be fully
used during the whole rush-hour. The results of our computations are presented
in Table 7.3. It shows that a maximum toll of € 1.46 can be levied between 08.30
and 09.00h. The equivalent optimal rewards can be found by taking the difference
between € 2.25 and the optimal toll.
Table 7.3: Optimal tolls in thirty-minute intervals
Time
07.30-08.00h
08.00-08.30h
08:30-09.00h
09.00-09.30h
Value of minimum time spent in the queue
0.13
0.79
1.46
0.45
Optimal tolls or rewards for different time intervals can be computed in a similar
manner.
7.5 Concluding remarks
Even though it was shown in section 7.3 that some aspects of the traffic congestion
problems on this network correspond closely to the predictions of the bottleneck
model, the actual situation differs markedly from that assumed in the model.
• A large amount of traffic on the A12 leaves this highway at Nootdorp or Prins
Clausplein. This traffic experiences some of the congestion on the A12. It can, however, be argued that it does not contribute to the travel time of commuters who
have to pass through the bottleneck.
• Traffic from Nootdorp and Zoetermeer Centrum joins the A12 between the
Zoetermeer on-ramp and the bottleneck. As this traffic has to pass through the
bottleneck, it contributes to the congestion experienced by drivers who join the
A12 at Zoetermeer or earlier. We did not present a formal analysis of the consequences of this phenomenon for the traffic flows on the network, the resulting
equilibrium or the optimal tolls.
• It is hard to believe that all commuters passing through the bottleneck have the
same preferred arrival time (i.e. close to 09.00h). There are probably many commuters who want to pass through the bottleneck earlier. The implications of the
presence of such commuters for the analysis have not been investigated.
• We have assumed that the values of time and scheduling delay costs are equal
for all commuters, and this is probably also at variance with reality. This suggests
an extension of the model to a heterogeneous population of drivers.
This list could perhaps be extended and suggests that it would be worthwhile to
carry out a number of sensitivity analyses on the application of the bottleneck
model to this particular situation.
Another couple of comments are related to the Spitsmijden trial. The analysis
presented above assumed that all drivers who pass through the bottleneck will
be rewarded or tolled. However, the trial concerned a limited (and selected) group
of commuters. In the previous sections we did not consider the implications of
rewarding or tolling a subset of commuters. To do so, one should take the example
76
Experimental design and modelling | Spitsmijden
of a coarse toll / reward and assume that it is relevant to only a certain group of
commuters. The members of this group will tend to avoid the time interval during
which the toll is in effect or switch to the interval during which a reward will be
given. The result will be a change in travel times during some time intervals. Even
though these changes may tend towards a new equilibrium for the group that is
tolled or rewarded, it will distort the original equilibrium for the other commuters.
These other commuters will therefore tend to switch reversely, which will counteract the move towards a different equilibrium.
Whether or not a different equilibrium will ultimately be realized depends on the
relative size of both groups. If the group that is rewarded is a small minority (as
was the case in the trial), the equilibrium will remain unchanged if the time intervals during which they are rewarded are part of the original rush-hour. The switch
in departure times of the participants in the trial will be exactly compensated for
by reverse shifts of the non-participants. If the time intervals during which the participants are rewarded are outside the original rush-hour, total demand during the
rush-hour will decrease, and there will be a modest decrease of congestion during
the rush-hour.
Even if the group of commuters who are rewarded is large enough to enable a
shift in the traffic equilibrium, one must be aware of the possibility that decreased
congestion during the rush-hour may attract drivers who previously chose to travel
at different time periods. This possibility is neglected by the version of the bottleneck model that we used above, which is characterized by inelastic total demand.
Arnott and colleagues (1993) consider an extension of the model with elastic
demand that may be useful in this respect. It should be noted in this context that
rewarding drivers for avoiding the rush-hour results in a situation that is different
from optimal tolling, since it decreases the disutility of the commute. Latent demand may be substantial. This is an additional topic for further investigation.
Spitsmijden | Experimental design and modelling
77
8 CONCLUSIONS
The first stage of the Spitsmijden project successfully carried out a reward trial
that encouraged car drivers to avoid the morning rush-hour. A close examination
of the various aspects of the implementation resulted in the following insights:
• Technical implementation. The selected technical design allowed for reliable registrations of vehicle movements. The EVI / OBU system had a very high reliability.
• Behavioural analysis. The impact of the reward on rush-hour travel behaviour
was significant, both for the monetary and the Yeti smartphone reward type. A
reduction of rush-hour car trips by about 50% was observed. This reduction was
obtained mainly by rescheduling trips to earlier or later points in time. A shift to
public transport occurred, but to a lesser degree.
• Bottleneck simulation model. The bottleneck model is an insightful tool for the
analysis of motorway queuing. Application to the A12 Zoetermeer-The Hague
morning rush-hour demonstrates how welfare optimal rewards can be identified.
• INDY simulation model. The INDY model was extended to allow for a detailed
modelling of a reward scheme on the A12 motorway. Preliminary simulations of
some scenarios provide an indication of the capabilities of the model, which are
promising. The results indicate that both the level of the reward and the participation rate have a decisive influence on the net queuing time results.
A subsequent stage of the project will focus on refining the various aspects. The
behavioural analysis will be extended by explicitly linking the stated preference
surveys to the revealed preference trial. This will extend the scope of the analysis.
Extending and refining the simulation tools should allow for further exploration
of the dynamics of reward schedules. Possible extensions of the bottleneck model
include a more detailed traffic representation, which at this stage is still a simplified single link set-up.
As for the INDY model, a further extension of the simulation scenarios will contribute to a better understanding of the impact of certain aspects of a reward
scheme, including the impact of flexible time intervals. The link between the INDY
model and the bottleneck model will be further explored at a subsequent stage.
78
Experimental design and modelling | Spitsmijden
9 BIBLIOGRAPHY
Arnott, R., A. de Palma & R. Lindsey (1990) Economics of a Bottleneck. Journal of
Urban Economics, 27, 111-130.
Bliemer, M.C.J. (2004) INDY Model Specifications. Working document, Delft University of Technology, the Netherlands.
Bliemer, M., and H. Taale (2006) Route Generation and Dynamic Traffic Assignment
for Large Network. Proceedings of the 1st Symposium on Dynamic Traffic Assignment, Leeds, UK.
Bliemer, M., E. Versteegt and R. Castenmiller (2004) INDY: A New Analytical Multiclass Dynamic Traffic Assignment Model. Proceedings of the 5th Triennial Symposium on Transportation Analysis, Le Gosier, Guadeloupe.
McFadden, D. (1974) Conditional logit analysis of qualitative choice behaviour. In
Frontiers of Econometrics, Zarembka, P. (ed.), Academic Press, New York, pp. 105-142.
Verhoef, E.T. (2005) Speed-flow Relations and Cost Functions of Congested Traffic:
Theory and Empirical Analysis. Transportation Research A, 39, 792-812.
Verhoef, E.T. (2003) Inside the Queue: Hypercongestion and Road Pricing in a
Continuous Time-Continuous Place Model of Traffic Congestion. Journal of Urban
Economics, 54, 531-565.
Vickrey, W.S. (1969) Congestion Theory and Transport Investment. American Economic Review, 59, 251-261.
Spitsmijden | Experimental design and modelling
79
80
Experimental design and modelling | Spitsmijden
Spitsmijden, Experimental design and modelling
APPENDICES
Spitsmijden | Experimental design and modelling
81
APPENDIX 1: FIRST SURVEY
Enquête “Spitsmijden”
Inleiding
Zoals u misschien al via de media hebt vernomen, wordt er in het najaar van 2006
op de A12 een proef uitgevoerd om de mogelijke effecten te onderzoeken van
beloningen voor automobilisten in de ochtendspits op de A12. De proef bestaat
eruit dat automobilisten, die geregeld voor hun werk in de ochtendspits op de
A12 rijden, beloond worden als ze tijdens de spitsuren niet van de A12 (of alternatieve routes) gebruikmaken. Dit kan door op andere tijden te reizen, of door een
alternatief voor de eigen auto te kiezen (bijvoorbeeld openbaar vervoer, carpoolen,
thuiswerken, of de fiets). Of u in de spits de A12 of alternatieve routes gebruikt,
wordt (via een ontvanger in uw auto) gemeten op de wegen tussen Zoetermeer en
Den Haag.
Ter voorbereiding op de proef voeren de Universiteit Utrecht en de Vrije Universi-
teit een enquêteonderzoek uit. Door middel van deze enquête willen we inzicht
krijgen in de mogelijke effecten van de proef. We zullen u vragen welke mogelijkheden u heeft om uw woon-werkrit aan te passen, en zo ja, of u daartoe zou
overgaan bij bepaalde soorten beloningen. Het kost circa 20 minuten om de lijst in
te vullen.
De navolgende vragen hebben achtereenvolgens betrekking op uw:
– woon-werksituatie;
– gebruik van verkeers- en reisinformatie;
– mening over verschillende beloningsvarianten;
– persoonlijke gegevens.
De door u verstrekte gegevens worden uitsluitend voor dit onderzoek gebruikt
en worden op geen enkele wijze aan derden ter beschikking gesteld. Als u vragen
heeft over de enquête, dan kunt u telefonisch contact met ons opnemen via nummer 070 3621094.
82
Experimental design and modelling | Spitsmijden
Uw woon- en werksituatie
1. Wilt u hieronder uw woonadres invullen:
Straat en huisnummer ____________________________________________________________________
Postcode ______________________________________________________________________________________
Plaats__________________________________________________________________________________________
2. Wilt u hieronder uw werkadres invullen:
Straat en huisnummer ____________________________________________________________________
Postcode (indien bekend)_________________________________________________________________
Plaats__________________________________________________________________________________________
3. Hoeveel dagen per week werkt u (zowel op uw werkplek als thuis):
_____ dagen
4. Hoe vaak reist u gemiddeld per week in de ochtendspits (tussen 06.00 en 10.00
uur) naar uw werk (hiermee bedoelen we naar een vaste werkplek, dus niet
het incidenteel bezoeken van klanten of zakelijke relaties)?
_____keer
5. Maakt u deze reis wel eens anders dan als autobestuurder (eventueel in
combinatie met de auto)?
_____Ja ________________________________________________________________ > ga naar vraag 6
_____Nee _______________________________________________________________> ga naar vraag 7
6. Hoe vaak per week (gemiddeld) gebruikt u de volgende vervoermiddelen om
naar uw werk te gaan?
Auto (bestuurder) ______________________________________________________________ _____ keer
Auto (passagier) ________________________________________________________________ _____ keer
Auto + trein (P&R) _____________________________________________________________ _____ keer
Motor _____________________________________________________________________________ _____ keer
Brommer _________________________________________________________________________ _____ keer
Trein _______________________________________________________________________________ _____ keer
Bus ______________________________________________________ _____ keer
Fiets _______________________________________________________________________________ _____ keer
Anders ___________________________________________________________
namelijk: __________________________________________________________________________ _____keer
7. Hoe lang duurt uw woon-werkrit met de auto gemiddeld?
_____uur en _____minuten
8. Hoe lang zou uw woon-werkrit met de auto duren als er geen enkele vertraging
(door files of anderszins) zou zijn?
_____uur en_____minuten
9. Hoe lang duurt uw woon-werkrit met de auto tijdens een erg drukke
ochtendspits (bijv. de drukste ochtendspits van de afgelopen twee weken)?
_____uur en_____minuten
10. Hoe laat vertrekt u meestal naar uw werk als u met de auto gaat?
_____uur_____minuten
Spitsmijden | Experimental design and modelling
83
11. Hoe laat komt u meestal op uw werkplek aan als u met de auto gaat?
_____uur_____minuten
12. Wat zijn voor u op een gemiddelde werkdag de parkeerkosten als u met de auto
naar uw werk gaat?
_____euro
13. Vindt u het openbaar vervoer (of het combineren van auto en openbaar
vervoer, zoals Park & Ride) een realistisch alternatief voor uw woon-werkrit?
_____Ja ________________________________________________________________> ga naar vraag 15
_____Nee ____________________________________________________________________________________
14. Waarom is het openbaar vervoer (of combineren van auto en openbaar vervoer)
voor u geen realistisch alternatief (u kunt meerdere antwoorden aankruisen)?
�
_____Reistijd
�
_____Onzekere reistijd (kans op vertragingen)
�
_____Looptijd van woning naar OV halte
�
_____Looptijd van OV halte naar werkplek
�
_____Dienstregeling sluit niet aan bij werktijden
�
_____Comfort
�
�
_____In verband met meenemen van bagage
_____Anders, nl. ___________________________________________________________________________
Ga nu naar vraag 20
15. Welke vorm van openbaar vervoer (evt. in combinatie met andere
vervoerwijzen) is voor u het beste alternatief?
�
_____Trein
�
_____Tram
�
_____Bus
�
_____Anders, nl. ___________________________________________________________________________
16. Hoe gaat u van uw huis naar de bushalte, tramhalte of station?
�
_____Lopend
�
_____Fiets
�
_____Auto (bestuurder)
�
_____Auto (passagier)
�
_____Anders, nl. ___________________________________________________________________________
17. Hoe gaat u van de uitstaphalte of - station naar uw werk?
_____Lopend
_____Fiets
_____Auto (bestuurder)
_____Auto (passagier)
_____Anders, nl. ___________________________________________________________________________
18. Hoe lang duurt de rit met het openbaar vervoer naar uw werk gemiddeld,
inclusief voor- en natransport (enkele reis)?
_____uur en_____minuten
84
Experimental design and modelling | Spitsmijden
19. Wat zijn de kosten van uw woon-werkrit per openbaar vervoer (voor de
heen- en terugreis)?
_____euro en_____cent of_____strippen
20. Vindt u de fiets een realistisch alternatief voor uw woon-werkrit?
_____Ja _______________________________________________> ga naar 22
_____Nee
21. Waarom is de fiets voor u geen realistisch alternatief (meerdere antwoorden
mogelijk)?
_____Reistijd
_____Comfort
_____Veiligheid
_____In verband met meenemen van bagage
_____Te ver / afstand te groot
_____Anders, nl. __________________________________________________
Ga nu naar vraag 23
22. Hoe lang duurt de rit met de fiets naar uw werk (enkele reis)?
_____uur en_____minuten
23. Wat is de meest voorkomende begin- en eindtijd van uw werk?
begintijd ___________________________________ _____uur_____minuten
eindtijd ____________________________________ _____uur_____minuten
24. Op welk tijdstip zou u bij voorkeur beginnen en ophouden met werken?
begintijd ___________________________________ _____uur_____minuten
eindtijd ____________________________________ _____uur_____minuten
25. Hoe vaak per week kunt u, als u dat wilt, later dan gebruikelijk beginnen?
_____dagen per week
_____Ik kan niet later beginnen
26. Hoeveel minuten kunt u dan maximaal later beginnen?
_____minuten
27. Als u vroeger dan gebruikelijk op uw werkplaats aankomt, welke situatie is dan
het meest op u van toepassing?
_____Ik kan direct aan mijn werk beginnen
_____Ik kan nog niet echt beginnen, maar wel alvast voorbereidingen
treffen voor mijn werk
_____Ik moet echt wachten op een bepaald tijdstip, voordat ik mijn werk kan
beginnen (bijv. ploegendienst)
_____Ik moet wachten op collega’s voordat ik met mijn werk kan beginnen
_____Ik kan het (kantoor-)gebouw niet in
_____Anders, nl __________________________________________________
Spitsmijden | Experimental design and modelling
85
28. In hoeverre kunt u de eindtijd van uw werk aanpassen als uw aankomsttijd op
het werk verandert?
_____De eindtijd van mijn werk ligt vast
_____Als ik eerder begin met werken, kan ik ook eerder ophouden en daarna
meteen naar huis gaan
_____Als ik later begin te werken moet ik ook langer doorwerken
_____Ik kan mijn werktijden volledig vrij bepalen
_____Anders, nl. __________________________________________________
29. Welke van de volgende (gezins- of persoonlijke) omstandigheden zijn van
invloed op de mogelijke vertrektijden van huis (meerdere antwoorden
mogelijk)?
_____Zorg voor kinderen
_____Samen willen ontbijten
_____Het brengen van kinderen naar school
_____Het afzetten van partner bij zijn / haar werk (of elders)
_____Carpoolafspraken
_____Anders, nl. __________________________________________________
_____Geen
30. In hoeverre kunt u, als u met de auto gaat, eerder van huis vertrekken dan uw
gebruikelijke vertrektijd (uit vraag 10), rekeninghoudend met de
bovengenoemde (gezins)omstandigheden?
_____Ik kan maximaal_____minuten eerder vertrekken
_____Niet van toepassing: ik kan zo vroeg vertrekken als ik maar zou willen
31. In hoeverre kunt u, als u met de auto gaat, later van huis vertrekken dan uw
gebruikelijke vertrektijd (uit vraag 10) rekening houdend met de
bovengenoemde (gezins)omstandigheden?
_____Ik kan maximaal_____minuten later vertrekken
_____Niet van toepassing: ik kan zo laat vertrekken als ik maar zou willen
32. Hoeveel dagen per week kunt u gemiddeld thuiswerken?
_____dagen per week
Gebruik van
verkeersinformatie
33. Hoe vaak per week raadpleegt u informatie over de situatie op het wegennet
(files) voordat u naar uw werk vertrekt?
_____keer per week
34. Uit welke bron is deze informatie afkomstig (meerdere mogelijkheden)?
_____Internet (specificeer website _________________________________ )
_____Teletekst
_____Via mobiele telefoon (WAP/sms)
_____Radio
_____TV
_____Anders, nl __________________________________________________
35. Hoe vaak raadpleegt u informatie over het openbaar vervoer (routeplanner en
informatie over vertragingen) voordat u naar uw werk vertrekt?
_____keer per week
86
Experimental design and modelling | Spitsmijden
36. Uit welke bron is deze informatie afkomstig (meerdere mogelijkheden)?
_____Internet (specificeer website _________________________________ )
_____Teletekst
_____Via mobiele telefoon (WAP/sms)
_____Radio
_____TV
_____Telefoon (0900-9292)
_____Anders, nl. __________________________________________________
37. Hoe vaak leidt verkeersinformatie er toe dat u eerder naar uw werk vertrekt?
_____keer per maand
38. Hoe vaak leidt verkeersinformatie er toe dat u later naar uw werk vertrekt?
_____keer per maand
39. Hoe vaak leidt verkeersinformatie er toe dat u een ander vervoermiddel kiest
dan u oorspronkelijk van plan was?
_____keer per maand
40. Hoe vaak leidt verkeersinformatie er toe dat u besluit thuis te werken?
_____keer per maand
De proef Spitsmijden proef
In dit onderdeel van de enquête willen we u vragen hoe u staat tegenover de
proef Spitsmijden. De proef houdt in dat u een beloning krijgt als u niet in de spits
met de auto naar uw werk gaat. U wordt dus beloond als u buiten de spits met
de auto reist, of met een ander vervoermiddel reist of helemaal niet reist (bijv.
thuiswerken).
Dit wordt gemeten via meetpunten op de wegen tussen Zoetermeer en Den Haag
en een ontvanger die in uw auto wordt geplaatst. U kunt er van uit gaan dat u
ongeveer halverwege uw woon-werkrit geregistreerd wordt. Als u in de spits met
de auto langs een van deze meetpunten rijdt, krijgt u dus geen beloning. U mag
er tevens van uit gaan dat de vertraging door files en het aanbod van openbaar
vervoer hetzelfde blijft als in de huidige situatie.
In dit onderdeel stellen we u verschillende varianten van de Spitsmijden proef
voor, en vragen u om zich voor te stellen dat u aan de proef meedoet. Wilt u voor
iedere situatie aangeven hoe u uw woon-werk-reis zou maken, door uit een aantal
getoonde mogelijkheden te kiezen? Omdat u misschien niet iedere dag dezelfde
keuze zult maken, vragen we u om uw woon-werkritten voor 50 werkdagen (de
duur van de proef voor een voltijdwerker) aan verschillende alternatieven toe te
delen.
Spitsmijden | Experimental design and modelling
87
Uw huidige reisgedrag
41. Allereerst willen we u vragen aan te geven hoe vaak, per 50 werkdagen,
u gemiddeld op dit moment de volgende opties kiest voor uw huidige woonwerkreis:
Huidige situatie
Stel u werkt 50 dagen en u zou niet meedoen aan het experiment. Hoe vaak kiest u ieder van de volgende
mogelijkheden:
Met de auto
vóór 06.30 uur
langs meetpunt
Met de auto
tussen 06.30 en 09.30 uur
langs meetpunt
Met de auto
na 09.30uur
langs meetpunt
Met het
openbaar
vervoer
Met de
fiets
_____keer
_____keer
_____keer
_____keer
_____keer _____keer
Geld als beloning
Thuiswerken
42. Stelt u zich nu voor dat u aan de proef Spitsmijden meedoet, en dat de
beloning als volgt plaatsvindt. Voor iedere dag dat u niet met de auto in de
spits langs een van de meetpunten reist, ontvangt u een geldbedrag, zoals
hieronder is aangegeven. De totale beloning tijdens de proef kan oplopen
tot ongeveer € 300. Wilt u aangeven hoe vaak, per 50 werkdagen, u gemiddeld
de volgende opties kiest voor uw huidige woon-werkreis:
Beloningsvariant Geld
U wordt beloond als u niet met de auto langs een van de meetpunten rijdt tussen 06.30 en 09.30 uur.
De beloning bedraagt € 5,00 per dag.
Tijdens de proef werkt u 50 dagen. Hoe vaak kiest u ieder van de volgende mogelijkheden:
Met de auto
vóór 06.30u
Met de auto
tussen 06.30 en 09.30 uur
Met de auto
na 09.30u
Met het
openbaar
vervoer
Met de
fiets
_____keer
_____keer
_____keer
_____keer
_____keer _____keer
De Yeti als beloning
88
Thuiswerken
43. Stelt u zich nu voor dat u aan de proef Spitsmijden meedoet, en dat de beloning als volgt plaatsvindt. Bij het begin van de proef krijgt u een Yeti in bruikleen. De Yeti is een navigatiesysteem waarmee de automobilist met gesproken instructies naar iedere gewenste bestemming wordt geleid. Bovendien
geeft de Yeti overal de meest actuele gebaseerd op locatie en rijrichting, en
heeft onder andere de standaard functionaliteit van een PDA: agenda,
e-mail, Word, Excel, Notes, Internet en telefoon. De winkelwaarde van de Yeti
bedraagt € 600.
Voor iedere keer dat u niet met de auto in de spits langs een van de meet
punten reist, ontvangt u spaarpunten (credits). U kunt in totaal 50 spaarpunten behalen. Als u aan het einde van de proef voldoende credits heeft verzameld mag u de Yeti gratis behouden en ontvangt u een abonnement op de
verkeersinformatieservice van 1 jaar. Wilt u aangeven hoe vaak, per 50 werkdagen, u gemiddeld de volgende opties kiest voor uw huidige woon-werkreis:
Experimental design and modelling | Spitsmijden
Beloningsvariant Yeti
U ontvangt 1 spaarpunt als u niet met de auto langs een van de meetpunten rijdt tussen 06.30 en 09.30 uur.
Als u 25 van de 50 spaarpunten behaalt mag u de Yeti houden.
Tijdens de proef werkt u 50 dagen. Hoe vaak kiest u ieder van de volgende mogelijkheden:
Met de auto
vóór 06.30u
Met de auto
tussen 06.30 en 09.30 uur
Met de auto
na 09.30u
Met het
openbaar
vervoer
Met de
fiets
_____keer
_____keer
_____keer
_____keer
_____keer _____keer
Achtergrondgegevens
Thuiswerken
44. Wat is uw geboortejaar?
_____
45. Wat is uw geslacht?
_____Man
_____Vrouw
46. Wat is uw opleidingsniveau?
_____Basisschool
_____VMBO / HAVO / VWO (secundair onderwijs)
_____LBO (lager beroepsonderwijs)
_____MBO (middelbaar beroepsonderwijs)
_____HBO / WO (hoger beroepsonderwijs / universitair)
47. Wat is het netto maandelijks inkomen van uw huishouden?
_____< € 2.000
_____€ 2.000 – € 3.500
_____€ 3.500 – € 5.000
_____Meer dan € 5.000
_____Ik wil geen informatie geven over mijn inkomen.
48. Wat is uw gezinssamenstelling?
_____Alleenstaand
_____Getrouwd / samenwonend zonder kinderen
_____Getrouwd / samenwonend met kinderen
_____Alleenstaande ouder
_____Anders _____________________________________________________
49. Indien u thuiswonende kinderen heeft, wilt u dan hun leeftijden aangeven?
1ste kind ________________________________________________ _____ jaar
2e kind __________________________________________________ _____ jaar
3e kind __________________________________________________ _____ jaar
4e kind _________________________________________________ _____ jaar
5e kind __________________________________________________ _____ jaar
6e kind _________________________________________________ _____ jaar
50. Over hoeveel auto’s beschikt uw huishouden?
_____auto’s
Spitsmijden | Experimental design and modelling
89
WIJ DANKEN U HARTELIJK VOOR UW MEDEWERKING!
U kunt de enquête retourneren in de bijgevoegde antwoordenvelop.
90
Experimental design and modelling | Spitsmijden
APPENDIX 2: SP SURVEY (MONETARY REWARDS)
Beste Spitsmijden deelnemer,
Als deelnemer aan de proef Spitsmijden heeft u enige tijd geleden een enquête
ingevuld. Hierin werden vragen gesteld over uw woon- en werksituatie, die nodig
zijn om de effecten van de proef naar waarde te kunnen schatten. Daarnaast heeft
u aangegeven hoe u op de beloning door middel van geld of sparen voor de Yeti
zou reageren.
In de volgende enquête gaan we in meer detail in op de door u gekozen
beloningsvariant (beloning met geld). We stellen u verschillende varianten van de
proef Spitsmijden voor, die verschillen met betrekking tot:
- de beloningshoogte
- de vertraging in de spits
We vragen u om zich voor te stellen dat u aan de proef meedoet. Wilt u voor
iedere situatie aangeven hoe u uw woon-werkreis zou maken, door uit een aantal
getoonde mogelijkheden te kiezen? Omdat u misschien niet iedere dag dezelfde
keuze zult maken, vragen we u om uw woon-werkritten voor 50 werkdagen (de
duur van de proef voor een voltijdwerker) aan verschillende alternatieven toe te
delen.
Stelt u zich nu voor dat u aan de Beloningsproef meedoet, en beantwoordt u de
vragen voor verschillende beloningsvarianten.
Beloningsvariant 2
U wordt beloond als u niet met de auto langs een van de meetpunten rijdt tussen 06.30 en 09.30 uur. De reistijd
met het openbaar vervoer is 20 minuten langer dan met de auto in de spits. De beloning bedraagt: € 3,00 per dag.
De vertraging door files bedraagt: • vóór 06.30 gemiddeld 5 minuten.
• tussen 06.30 en 09.30 uur gemiddeld 35 minuten.
• na 09.30 uur gemiddeld 5 minuten.
Tijdens de proef werkt u 50 dagen. Hoe vaak kiest u ieder van de volgende mogelijkheden:
vóór 06.30 uur
Met de auto
tussen 06.30 en 09.30 uur
Met de auto
Met de auto
na 09.30 uur
Met het
openbaar
vervoer
Met de
fiets
_____keer
_____keer
_____keer
_____keer
_____keer _____keer
Thuiswerken
Spitsmijden | Experimental design and modelling
91
Beloningsvariant 3
U wordt beloond als u niet met de auto langs een van de meetpunten rijdt tussen 06.00 en 10.00 uur. De reistijd
met het openbaar vervoer is 5 minuten langer dan met de auto in de spits. De beloning bedraagt: € 7,00 per dag.
De vertraging door files bedraagt: • vóór 06.00 gemiddeld 5 minuten.
• tussen 06.00 en 10.00 uur gemiddeld 35 minuten.
• na 10.00 uur gemiddeld 5 minuten.
Tijdens de proef werkt u 50 dagen. Hoe vaak kiest u ieder van de volgende mogelijkheden:
Met de auto
vóór 06.00 uur
Met de auto
tussen 06.00 en 10.00 uur
Met de auto
na 10.00 uur
Met het
openbaar
vervoer
Met de
fiets
Thuiswerken
_____keer
_____keer
_____keer
_____keer
_____keer _____keer
Beloningsvariant 7
U wordt beloond als u niet met de auto langs een van de meetpunten rijdt tussen 06.30 en 09.30 uur. De reistijd
met het openbaar vervoer is 5 minuten langer dan met de auto in de spits. De beloning bedraagt: € 5,00 per dag.
De vertraging door files bedraagt: • vóór 06.30 gemiddeld 10 minuten.
• tussen 06.30 en 09.30 uur gemiddeld 20 minuten.
• na 09.30 uur gemiddeld 10 minuten.
Tijdens de proef werkt u 50 dagen. Hoe vaak kiest u ieder van de volgende mogelijkheden:
vóór 06.30 uur
Met de auto
tussen 06.30 en 09.30 uur
Met de auto
Met de auto
na 09.30 uur
Met het
openbaar
vervoer
Met de
fiets
_____keer
_____keer
_____keer
_____keer
_____keer _____keer
Thuiswerken
Beloningsvariant 9
U wordt beloond als u niet met de auto langs een van de meetpunten rijdt tussen 06.00 en 10.00 uur. De reistijd
met het openbaar vervoer is 20 minuten langer dan met de auto in de spits. De beloning bedraagt: € 3,00 per dag.
De vertraging door files bedraagt: • vóór 06.00 gemiddeld 5 minuten.
• tussen 06.00 en 10.00 uur gemiddeld 35 minuten.
• na 10.00 uur gemiddeld 5 minuten.
Tijdens de proef werkt u 50 dagen. Hoe vaak kiest u ieder van de volgende mogelijkheden:
92
Met de auto
vóór 06.00 uur
Met de auto
tussen 06.00 en 10.00 uur
Met de auto
na 10.00 uur
Met het
openbaar
vervoer
Met de
fiets
_____keer
_____keer
_____keer
_____keer
_____keer _____keer
Experimental design and modelling | Spitsmijden
Thuiswerken
Beloningsvariant 12
Als u met de auto langs een van de meetpunten rijdt:
• vóór 06.00 uur ontvangt u € 5,00 (5 minuten vertraging door files);
• tussen 06.00 en 07.00 uur ontvangt u € 2,50 (20 minuten vertraging door files);
• tussen 07.00 en 09.00 uur ontvangt u geen beloning (35 minuten vertraging door files);
• tussen 09.00 en 10.00 uur ontvangt u € 2,50 (20 minuten vertraging door files);
• na 10.00 uur ontvangt u € 5,00 (5 minuten vertraging door files).
De reistijd met het openbaar vervoer is 5 minuten langer dan met de auto in de spits.
Als u reist met openbaar vervoer of fiets of thuis werkt ontvangt u € 5,00. Tijdens de
proef werkt u 50 dagen. Hoe vaak kiest u ieder van de volgende mogelijkheden:
Met de auto
vóór 06.00 uur
tussen 06.00 en 07.00 uur
tussen 07.00 en 09.00 uur
tussen 09.00 en 10.00 uur
na 10.00 uur
_____keer
_____keer
_____keer
_____keer
_____keer
Met het
openbaar
vervoer
Met de fiets
Thuiswerken
_____keer
_____keer
_____keer
Beloningsvariant 14
Als u met de auto langs een van de meetpunten rijdt:
• vóór 07.00 uur ontvangt u € 7,00 (10 minuten vertraging door files);
• tussen 07.00 en 07.30 uur ontvangt u € 3,50 (18 minuten vertraging door files);
• tussen 07.30 en 08.30 uur ontvangt u geen beloning (25 minuten vertraging door files);
• tussen 08.30 en 09.00 uur ontvangt u € 3,50 (18 minuten vertraging door files);
• na 09.00 uurontvangt u € 7,00 (10 minuten vertraging door files).
De reistijd met het openbaar vervoer is 20 minuten langer dan met de auto in de spits.
Als u reist met openbaar vervoer of fiets of thuis werkt ontvangt u € 7,00. Tijdens de
proef werkt u 50 dagen. Hoe vaak kiest u ieder van de volgende mogelijkheden:
Met de auto
vóór 07.00 uur
tussen 07.00 en 07.30 uur
tussen 07.30 en 08.30 uur
tussen 08.30 en 09.00 uur
na 09.00 uur
_____keer
_____keer
_____keer
_____keer
_____keer
Met het
openbaar
vervoer
Met de fiets
Thuiswerken
_____keer
_____keer
_____keer
WIJ DANKEN U HARTELIJK VOOR UW MEDEWERKING!
Spitsmijden | Experimental design and modelling
93
APPENDIX 3: SP SURVEY (YETI REWARDS)
Beste Spitsmijden deelnemer,
Als deelnemer aan de proef Spitsmijden heeft u enige tijd geleden een enquête
ingevuld. Hierin werden vragen gesteld over uw woon- en werksituatie, die nodig
zijn om de effecten van de proef naar waarde te kunnen schatten. Daarnaast heeft
u aangegeven hoe u op de beloning door middel van geld of sparen voor de Yeti
zou reageren.
In de volgende enquête gaan we in meer detail in op de door u gekozen
beloningsvariant (beloning met de Yeti). We stellen u verschillende varianten van
de Spitsmijden proef voor, die verschillen met betrekking tot:
- het aantal spitsmijdingen nodig om de Yeti te verdienen
- de vertraging in de spits
We vragen u om zich voor te stellen dat u aan de proef meedoet. Wilt u voor
iedere situatie aangeven hoe u uw woon-werkreis zou maken, door uit een aantal
getoonde mogelijkheden te kiezen? Omdat u misschien niet iedere dag dezelfde
keuze zult maken, vragen we u om uw woon-werkritten voor 25 werkdagen (de
duur van de proef voor een voltijdwerker) aan verschillende alternatieven toe te
delen.
Stelt u zich nu voor dat u aan de Beloningsproef meedoet, en beantwoordt u de
vragen voor verschillende beloningsvarianten.
Beloningsvariant 21
U ontvangt 1 spaarpunt als u niet met de auto langs een van de meetpunten rijdt tussen 07.00 en 09.00 uur.
De reistijd met het openbaar vervoer is 20 minuten langer dan met de auto in de spits. Als u 20 van de 25
spaarpunten behaalt mag u de Yeti houden.
De vertraging door files bedraagt: • vóór 07.00 gemiddeld 10 minuten.
• tussen 07.00 en 09.00 uur gemiddeld 20 minuten.
• na 09.00 uur gemiddeld 10 minuten.
Tijdens de proef werkt u 25 dagen. Hoe vaak kiest u ieder van de volgende mogelijkheden:
94
Met de auto
vóór 07.00 uur
Met de auto
tussen 07.00 en 09.00 uur
Met de auto
na 09.00 uur
Met het
openbaar
vervoer
Met de
fiets
_____keer
_____keer
_____keer
_____keer
_____keer _____keer
Experimental design and modelling | Spitsmijden
Thuiswerken
Beloningsvariant 24
U ontvangt 1 spaarpunt als u niet met de auto langs een van de meetpunten rijdt tussen 07.00 en 09.00 uur.
De reistijd met het openbaar vervoer is 5 minuten langer dan met de auto in de spits. Als u 10 van de 25
spaarpunten behaalt mag u de Yeti houden.
De vertraging door files bedraagt: • vóór 7.00 uur gemiddeld 10 minuten.
• tussen 07.00 en 09.00 uur gemiddeld 30 minuten.
• na 09.00 uur gemiddeld 10 minuten.
Tijdens de proef werkt u 25 dagen. Hoe vaak kiest u ieder van de volgende mogelijkheden:
Met de auto
vóór 07.00 uur
Met de auto
tussen 07.00 en 09.00 uur
Met de auto
na 09.00 uur
Met het
openbaar
vervoer
Met de
fiets
Thuiswerken
_____keer
_____keer
_____keer
_____keer
_____keer _____keer
Beloningsvariant 25
U ontvangt 1 spaarpunt als u niet met de auto langs een van de meetpunten rijdt tussen 07.00 en 09.00 uur.
De reistijd met het openbaar vervoer is 5 minuten langer dan met de auto in de spits. Als u 15 van de 25
spaarpunten behaalt mag u de Yeti houden.
De vertraging door files bedraagt: • vóór 07.00 uur gemiddeld 5 minuten.
• tussen 07.00 en 09.00 uur gemiddeld 35 minuten.
• na 09.00 uur gemiddeld 5 minuten.
Tijdens de proef werkt u 25 dagen. Hoe vaak kiest u ieder van de volgende mogelijkheden:
Met de auto
vóór 07.00 uur
Met de auto
tussen 07.00 en 09.00 uur
Met de auto
na 09.00 uur
Met het
openbaar
vervoer
Met de
fiets
Thuiswerken
_____keer
_____keer
_____keer
_____keer
_____keer _____keer
Beloningsvariant 28
U ontvangt 1 spaarpunt als u niet met de auto langs een van de meetpunten rijdt tussen 06.00 en 10.00 uur.
De reistijd met het openbaar vervoer is 20 minuten langer dan met de auto in de spits. Als u 15 van de 25
spaarpunten behaalt mag u de Yeti houden.
De vertraging door files bedraagt: • vóór 06.00 gemiddeld 10 minuten.
• tussen 06.00 en 10.00 uur gemiddeld 30 minuten.
• na 10.00 uur gemiddeld 10 minuten.
Tijdens de proef werkt u 25 dagen. Hoe vaak kiest u ieder van de volgende mogelijkheden:
Met de auto
vóór 06.00 uur
Met de auto
tussen 06.00 en 10.00 uur
Met de auto
na 10.00 uur
Met het
openbaar
vervoer
Met de
fiets
Thuiswerken
_____keer
_____keer
_____keer
_____keer
_____keer _____keer
Spitsmijden | Experimental design and modelling
95
Beloningsvariant 30
Als u met de auto langs een van de meetpunten rijdt:
• vóór 07.00 uur ontvangt u 1 spaarpunt (5 minuten vertraging door files);
• tussen 07.00 en 07.30 uur ontvangt u 0,5 spaarpunt (20 minuten vertraging door files);
• tussen 07.30 en 08.30 uur ontvangt u geen spaarpunten (35 minuten vertraging door files);
• tussen 08.30 en 09.00 uur ontvangt u 0,5 spaarpunt (20 minuten vertraging door files);
• na 09.00 uur ontvangt u 1 spaarpunt (5 minuten vertraging door files).
De reistijd met het openbaar vervoer is 5 minuten langer dan met de auto in de spits.
Als u reist met openbaar vervoer of fiets of thuis werkt ontvangt u 1 spaarpunt.
U ontvangt de Yeti als u 20 van de 25 spaarpunten heeft behaald. Tijdens de proef werkt
u 25 dagen. Hoe vaak kiest u ieder van de volgende mogelijkheden:
Met de auto
vóór 07.00 uur
tussen 07.00 en 07.30 uur
tussen 07.30 en 08.30 uur
tussen 08.30 en 09.00 uur
na 09.00 uur
_____keer
_____keer
_____keer
_____keer
_____keer
Met het
openbaar
vervoer
Met de fiets
Thuiswerken
_____keer
_____keer
_____keer
Beloningsvariant 31
Als u met de auto langs een van de meetpunten rijdt:
• vóór 07.00 uur ontvangt u 1 spaarpunt (5 minuten vertraging door files);
• tussen 07.00 en 07.30 uur ontvangt u 0,5 spaarpunt (20 minuten vertraging door files);
• tussen 07.30 en 08.30 uur ontvangt u geen spaarpunten (35 minuten vertraging door files);
• tussen 08.30 en 09.00 uur ontvangt u 0,5 spaarpunt (20 minuten vertraging door files);
• na 09.00 uur ontvangt u 1 spaarpunt (5 minuten vertraging door files).
De reistijd met het openbaar vervoer is 20 minuten langer dan met de auto in de spits.
Als u reist met openbaar vervoer of fiets of thuis werkt ontvangt u 1 spaarpunt.
U ontvangt de Yeti als u 15 van de 25 spaarpunten heeft behaald. Tijdens de proef werkt
u 25 dagen. Hoe vaak kiest u ieder van de volgende mogelijkheden:
Met de auto
vóór 07.00 uur
tussen 07.00 en 07.30 uur
tussen 07.30 en 08.30 uur
tussen 08.30 en 09.00 uur
na 09.00 uur
_____keer
_____keer
_____keer
_____keer
_____keer
Met het
openbaar
vervoer
Met de fiets
Thuiswerken
_____keer
_____keer
_____keer
WIJ DANKEN U HARTELIJK VOOR UW MEDEWERKING!
96
Experimental design and modelling | Spitsmijden
APPENDIX 4: LOGBOOK
Datum invullen
Ik heb gewerkt.
Mijn rit was:
Ik heb niet
gewerkt vanwege:
Eventuele
bijzondere situaties:
___met
de auto* vóór 07.30u**
de auto tussen 07.30 en 08.00u
___met de auto tussen 08.00 en 09.00u
___met de auto tussen 09.00 en 09.30u
___met de auto na 09.30u
___met een andere auto uit gezin
___met een auto van buiten gezin
___carpoolen (als passagier)
___met het openbaar vervoer
___met de fiets
___met een andere vervoerswijze
___niet nodig i.v.m. thuiswerken
___niet naar mijn gebruikelijke werkadres
maar naar een locatie buiten het proef-
___Geen
___Mijn
nl.: _____
_________________
_________________
_________________
_________________
_________________
_________________
_________________
_________________
_________________
auto was niet
beschikbaar:
º met vervangende auto
gereden ***
niet
º met vervangende
auto gereden
___Ik heb in de ochtendspits met
de auto tussen Zoetermeer
en Den Haag gereisd, maar
NIET om naar het werk te
gaan
___Iemand anders heeft in de
ochtendspits met mijn auto
Ik heb gewerkt.
Mijn rit was:
Ik heb niet
gewerkt vanwege:
Eventuele
bijzondere situaties:
___met
___Geen
___Mijn
___met
gebied (bijv. i.v.m. een afspraak)
Datum invullen
de auto* vóór 07.30u**
de auto tussen 07.30 en 08.00u
___met de auto tussen 08.00 en 09.00u
___met de auto tussen 09.00 en 09.30u
___met de auto na 09.30u
___met een andere auto uit gezin
___met een auto van buiten gezin
___carpoolen (als passagier)
___met het openbaar vervoer
___met de fiets
___met een andere vervoerswijze
___niet nodig i.v.m. thuiswerken
___niet naar mijn gebruikelijke werkadres
maar naar een locatie buiten het proefgebied (bijv. i.v.m. een afspraak)
___met
werkdag
___Verlof/vakantie
___Ziekte
___Anders,
_________________
_________________
_________________
_________________
_________________
_________________
werkdag
___Verlof/vakantie
___Ziekte
___Anders,
nl.: _____
_________________
_________________
_________________
_________________
_________________
_________________
_________________
_________________
_________________
_________________
_________________
_________________
_________________
_________________
_________________
tussen Zoetermeer en Den
Haag gereisd
___De Yeti werkte niet ***
___OBU gaf geen piepsignaal ***
___Overige bijzondere situatie,
namelijk: ______________
auto was niet
beschikbaar:
º met vervangende auto
gereden ***
niet
º met vervangende
auto gereden
___Ik heb in de ochtendspits met
de auto tussen Zoetermeer
en Den Haag gereisd, maar
NIET om naar het werk te
gaan
___Iemand anders heeft in de
ochtendspits met mijn auto
tussen Zoetermeer en Den
Haag gereisd
___De Yeti werkte niet ***
___OBU gaf geen piepsignaal ***
___Overige bijzondere situatie,
namelijk: ______________
* Hiermee wordt de auto waarin de OBU is ingebouwd bedoeld.
** Het gaat hierbij om het tijdstip waarop u volgens eigen inschatting langs het detectiepoortje kwam (u heeft een piepsignaal gehoord bij het passeren).
*** Het projectbureau Spitsmijden is hiervan op de hoogte gesteld.
Spitsmijden | Experimental design and modelling
97
APPENDIX 5: EVALUATION SURVEY
Evaluatie-enquête
A. Uw reisgedrag
In de afgelopen maanden heeft u deelgenomen aan de proef Spitsmijden. Door
uw deelname aan de proef heeft u een bijdrage geleverd aan het inzicht in verplaatsingsgedrag van forensen bij positieve prikkels om niet in de spits te rijden.
In deze enquête stellen wij u nog een aantal vragen, noodzakelijk voor de wetenschappelijke analyse van de proef. Deze enquête vormt daarom onderdeel van uw
deelname.
Uw reisgedrag tijdens
de proef Spitsmijden
1. Wat was uw belangrijkste motivatie(s) om aan het project mee te doen?
_____Beloning (geld of Yeti)
_____Een bijdrage leveren aan de kennis over het weggebruik in de spits
_____Een bijdrage leveren aan vermindering van de fileproblematiek
_____Experimenteren met mogelijkheden om het eigen gedrag aan te passen
_____Het opdoen van ervaring met de Yeti-smartphone en het gebruik van
verkeersinformatie
_____Anders, nl. __________________________________________________
_______________________________________________________
2. Om de beloning te krijgen heeft u uw woon-werkrit aan moeten passen. Hoeveel
moeite kostte u dat?
_____Erg veel
_____Redelijk veel
_____Redelijk weinig
_____Nauwelijks
3. Heeft u tijdens de proef Spitsmijden vaker of minder vaak dan u vooraf van plan
was uw gedrag aangepast door de spits te mijden?
_____Minder vaak ______________________________________ > ga naar 4
_____Even vaak
_____Vaker _____________________________________________> ga naar 5
4. Wat waren de belangrijkste factoren die het u moeilijk maakten om uw woonwerkrit aan te passen (meerdere antwoorden mogelijk)?
_____Afspraken op het werk (werktijden, vergaderingen / afspraken)
_____Verplichtingen binnen het gezin
_____Beschikbaarheid van alternatieve vervoermiddelen
_____De weersomstandigheden
_____Het later beschikbaar komen van RandstadRail
_____Anders, nl. __________________________________________________
________________________________________ > Ga naar 6
98
Experimental design and modelling | Spitsmijden
5. Wat waren de belangrijkste factoren die het u gemakkelijker maakten om uw
woon-werkrit aan te passen (meerdere antwoorden mogelijk)?
_____Verandering in werktijden, nl. __________________________________
_____Aanpassing in OV-verbinding, nl. _______________________________
_____Verandering binnen het gezin, nl. _______________________________
_____Steun van de werkgever om flexibel te kunnen werken
_____Anders, nl. __________________________________________________
__________________________________________________
6. Heeft u specifieke maatregelen genomen om het veranderen van uw gedrag
mogelijk te maken (meerdere antwoorden mogelijk)?
_____Afspraken met mijn werkgever over werktijden of thuiswerken
_____Afspraken met collega’s over werktijden
_____Afspraken met gezinsleden over tijdschema
_____Afspraken met gezinsleden over taakverdeling
_____Afspraken met kennissen / collega’s over carpoolen
_____Aanschaf van een OV-abonnement
_____Aanschaf van een fiets en / of regenkleding
_____Aanschaf van PC / laptop / breedband internet
_____Zoeken van informatie over OV-verbindingen tussen woning en werk
_____Zoeken van informatie over fietsroutes tussen woning en werk
_____Oefenen met het aanpassen van gedrag in de weken voor de
beloningsperiode
_____Anders, nl. __________________________________________________
__________________________________________________
7. Hoe vaak heeft u tijdens de proef informatie geraadpleegd over de situatie op
het wegennet (files) voordat u naar uw werk vertrok?
_____keer per week
8. Is dit vaker dan normaal?
_____Vaker
_____Even vaak
_____Minder vaak
9. Uit welke bron was deze informatie afkomstig (meerdere mogelijkheden)?
_____Yeti (of andere smartphone)
_____Haaglanden.mobiel
_____Internet (specificeer website _________________________________ )
_____Teletekst
_____Radio
_____TV
_____Mobiele telefoon
_____Anders, nl. __________________________________________________
__________________________________________________
10. Hoe vaak heeft u tijdens de proef informatie geraadpleegd over het openbaar
vervoer (routeplanner en informatie over vertragingen) voordat u naar uw werk
vertrok?
_____keer per week
Spitsmijden | Experimental design and modelling
99
11. Is dit vaker dan normaal?
_____Vaker
_____Even vaak
_____Minder vaak
12. Uit welke bron was deze informatie afkomstig (meerdere mogelijkheden)?
_____Yeti
_____Internet (specificeer website _________________________________ )
_____Teletekst
_____Radio
_____TV
_____Telefoon (0900-9292)
_____Anders, nl. __________________________________________________
__________________________________________________
13. In welke mate heeft de vertraging in de opening van RandstadRail uw gedrag
beïnvloed?
_____In het geheel niet
_____Als RandstadRail eerder van start was gegaan, had ik vaker met het OV
gereisd en minder vaak met de auto in de spits
_____Als RandstadRail eerder van start was gegaan, had ik vaker met het OV
gereisd maar was het aantal ritten met de auto in de spits hetzelfde
gebleven
_____Anders, nl. __________________________________________________
__________________________________________________
14. Kunt u aangeven welke van de volgende situaties op u van toepassing is met
betrekking tot uw gedrag na afloop van de proef Spitsmijden?
_____Tijdens de proef heb ik mijn gedrag aangepast om de beloning te
verdienen, maar na de proef ga ik weer terug naar mijn gedrag van
voor de proef.
_____Tijdens de proef heb ik een aantrekkelijke OV-verbinding (niet
RandstadRail) gevonden, die ik na de proef blijf gebruiken. Ik zal
daardoor minder autoritten tijdens de spits naar mijn werk maken.
_____Tijdens de proef is RandstadRail geopend. Dit is voor mij een
aantrekkelijke optie, die ik na de proef blijf gebruiken. Ik zal daardoor
minder autoritten tijdens de spits naar mijn werk maken.
_____Tijdens de proef heb ik het tijdstip van mijn autorit naar het werk
aangepast. Dit is mij goed bevallen en ik zal dit in de toekomst
regelmatig blijven doen.
_____Tijdens de proef ben ik vaker dan voorheen met de fiets naar het werk
gegaan. Dit is mij goed bevallen en ik zal dit in de toekomst regelmatig
blijven doen. Ik zal daardoor minder autoritten tijdens de spits naar
mijn werk maken.
_____Het is mij niet gelukt mijn gedrag tijdens de proef zodanig aan te
passen dat ik de spits structureel heb kunnen mijden. Dat zal ook na
de proef zo zijn.
_____Anders, nl. __________________________________________________
__________________________________________________
100
Experimental design and modelling | Spitsmijden
15. Wat zou u er van vinden als de beloning, zoals beschikbaar tijdens deze proef,
gebruikt zou worden als middel om bij wegwerkzaamheden mensen te
stimuleren om de spits te mijden?
_____Een goed idee, want __________________________________________
_____Een slecht idee, want _________________________________________
_____Weet niet / geen mening
(Als u geen Yeti in bruikleen heeft gehad tijdens de proef, ga dan naar vraag 20)
B. Het gebruik van de
Yeti-smartphone
16. Hoe vaak hebt u via de Yeti verkeersinformatie over files gebruikt?
_____per week
17. Hoe vaak hebt u via de Yeti verkeersinformatie over wegwerkzaamheden
gebruikt?
_____per week
18. Hoe nuttig vond u de informatie over files en wegwerkzaamheden?
_____Zeer nuttig
_____Redelijk nuttig
_____Niet erg nuttig
_____Totaal niet nuttig
19. Hoe goed kwam deze informatie overeen vergeleken met de werkelijkheid?
_____Zeer goed
_____Redelijk goed
_____Niet goed en niet slecht
_____Matig
_____Slecht
C. Uw deelname –
evaluatie projectbureau
Graag horen we ook uw mening over uw ervaringen rond uw deelname tijdens
de proef. De uitkomsten uit deze enquête dienen om eventuele vervolgprojecten
beter te kunnen uitvoeren.
20. Wat vond u van de informatie over de proef (timing, beloningsschema’s,
enquêtes etc.), zoals die door het projectbureau werden verstrekt?
_____Zeer goed
_____Redelijk goed
_____Niet goed en niet slecht
_____Matig
_____Slecht
Toelichting _______________________________________________________
_________________________________________________________________
_________________________________________________________________
_________________________________________________________________
_________________________________________________________________
Spitsmijden | Experimental design and modelling
101
21. Wat vindt u van de bereikbaarheid van het projectbureau tijdens de proef?
_____Zeer goed
_____Redelijk goed
_____Niet goed en niet slecht
_____Matig
_____Slecht
Toelichting _______________________________________________________
_________________________________________________________________
_________________________________________________________________
_________________________________________________________________
_________________________________________________________________
22. In hoeverre bent u het eens met de volgende stellingen over de ‘inbouwavond’
bij de RDW, toen uw OBU werd gemonteerd?
_____Ik ben keurig geholpen toen de OBU in mijn auto werd gebouwd
[geheel mee eens, mee eens, neutraal, mee oneens, zeer mee oneens]
_____Ik kon goed terecht met de vragen die ik had [geheel mee eens, mee
eens, neutraal, mee oneens, zeer mee oneens]
_____Ik vond het leuk om andere deelnemers te spreken [geheel mee eens,
mee eens, neutraal, mee oneens, zeer mee oneens]
_____Ik vond het fijn om mensen van het projectbureau te ontmoeten
[geheel mee eens, mee eens, neutraal, mee oneens, zeer mee oneens]
_____De Yeti-demonstratie was erg nuttig [geheel mee eens, mee eens,
neutraal, mee oneens, zeer mee oneens]
23. Indien u voor de Yeti als beloning heeft gekozen, kunt u dan aangeven in
hoeverre u het eens bent met de volgende stellingen?
_____Ik kon de Yeti goed gebruiken. Alles was duidelijk. [geheel mee eens,
mee eens, neutraal, mee oneens, zeer mee oneens, niet van toepassing]
_____Ik had wel een paar vragen, maar die zijn netjes beantwoord door
medewerkers van het projectbureau. [geheel mee eens, mee eens,
neutraal, mee oneens, zeer mee oneens, niet van toepassing]
_____Ik heb het overzicht met veelgestelde vragen over de Yeti (f.a.q. op de
persoonlijke pagina) als zeer nuttig ervaren. [geheel mee eens, mee
eens, neutraal, mee oneens, zeer mee oneens, niet van toepassing]
_____De informatie die ik via de Yeti ontving was duidelijk en nuttig. [geheel
mee eens, mee eens, neutraal, mee oneens, zeer mee oneens, niet van
toepassing]
_____Ik zou een volgende keer weer de Yeti hebben gekozen. [geheel mee
eens, mee eens, neutraal, mee oneens, zeer mee oneens, niet van
toepassing]
24. Tijdens de proef heeft u wekelijks via uw logboek informatie aan ons
doorgegeven over uw dagelijkse woon-werkrit. Hoe heeft u dit ervaren?
_____Veel werk
_____Redelijk veel werk
_____Vrij weinig werk
_____Een kleine moeite
Toelichting _______________________________________________________
102
Experimental design and modelling | Spitsmijden
_________________________________________________________________
_________________________________________________________________
_________________________________________________________________
_________________________________________________________________
25. Hoe vond u uw persoonlijke pagina’s op www.spitsmijden.nl (het logboek niet
meetellend)?
_____Zeer goed
_____Redelijk goed
_____Niet goed en niet slecht
_____Matig
_____Slecht
Toelichting _______________________________________________________
_________________________________________________________________
_________________________________________________________________
_________________________________________________________________
_________________________________________________________________
26. Heeft u de wekelijkse nieuwsbrief als nuttig ervaren, of eerder als vervuiling
van uw mailbox?
_____Nuttig
_____Vervuiling van mijn mailbox
_____Een nieuwsbrief? Nooit gezien?
_____Geen mening
Toelichting (vrijblijvend) ____________________________________________
_________________________________________________________________
_________________________________________________________________
_________________________________________________________________
_________________________________________________________________
27. Zou u een volgende keer weer meedoen?
_____Ja
_____Nee
_____Weet niet / geen mening
Toelichting (vrijblijvend) ____________________________________________
_________________________________________________________________
_________________________________________________________________
_________________________________________________________________
_________________________________________________________________
28. Als u aanvullende opmerkingen, ervaringen, aanbevelingen of ideeën hebt over
de proef, dan kunt u deze hier kwijt:
___________________________________________________________________
___________________________________________________________________
___________________________________________________________________
___________________________________________________________________
WIJ DANKEN U HARTELIJK VOOR UW MEDEWERKING!
Spitsmijden | Experimental design and modelling
103
APPENDIX 6: NON-RESPONSE TO SURVEY
Intro
Goedemorgen/-middag/-avond mevrouw/mijnheer, u spreekt met ... van Stratus
Marktonderzoek. In opdracht van onder meer het Ministerie van Verkeer en Waterstaat voeren wij momenteel een onderzoek uit naar het autoverkeer over de A12.
Vraag 01
Is er iemand in uw huishouden die zelf (als bestuurder) minimaal drie keer per
week tijdens de ochtendspits over de A12 richting Den Haag gaat?
1: ja
2: nee
Als Vraag 01 is 2 dan door naar Afsluiting2
Vraag 02
Hoeveel personen in uw huishouden doen dat?
1: 1 persoon
2: 2 personen
2: 3 of meer personen
Vraag 03INTR1
Ik wil graag het gesprek voortzetten met degene die dagelijks tijdens de spits
gebruik maakt van de A12 richting Den Haag. Is dat mogelijk?
Enq.: Indien respondent “JA” zegt en het gesprek NU gevoerd kan worden TYP “1” Indien respondent niet wil, de huisgenoot is niet thuis of wil ook niet of een andere reden waardoor geen gesprek kan, TYP DAN <CTLR END>.
Vraag 03INTR2
Dit onderzoek wordt uitgevoerd in het kader van het project “Spitsmijden”. Dit is
een experiment onder een willekeurige groep automobilisten uit Zoetermeer. Zij
krijgen een beloning voor elke keer als zij niet in de spits met de auto vanuit Zoetermeer richting Den Haag rijden.
Vraag 03
Heeft u over dit proefproject iets gehoord, gezien of gelezen?
1: ja
2: nee
3: wil niet zeggen
Vraag 04
Als Vraag 03 is 1
Heeft u toevallig zelf aan dit proefproject meegedaan?
1: ja
2: nee
Als Vraag 04 is 1 dan naar Afluiting4
Vraag 05
104
Hoe vaak reist u gemiddeld per week in de ochtendspits, dus tussen 6 uur en 10
uur, naar een VASTE bestemming, uw werk of voor nog andere doeleinden, over de
A12 richting Den Haag?
1: 2 keer of minder (Enq.: Is einde gesprek)
2: 3 keer
3: 4 keer
4: 5 keer of meer
5: wil niet zeggen (Enq.: Is einde gesprek)
Experimental design and modelling | Spitsmijden
Als Vraag 05 is 1 of 5 dan door naar Afsluiting3
Vraag 06
Wat is de reden van uw dagelijkse autorit richting Den Haag?
1: woon-werk
2: school-studie
3: anders, te weten: ...
4: wil niet zeggen
Vraag 07A
Kunt u de 4 cijfers van de postcode van deze vaste bestemming geven?
Als u die niet weet mag u ook de straat en plaatsnaam geven.
Enq.: WEET NIET / W.N.Z. is 9999
Vraag 07C
In welke plaats is dat?
1: Den Haag
2: Voorburg
3: Rijswijk
4: Leidschendam
5: Wateringen
6: Wassenaar
7: anders, te weten: ...
8: wil niet zeggen
Vraag 08
Staat de auto waarmee u deze rit meestal maakt op uw eigen naam, op naam van
uw werkgever of nog anders?
1: op eigen naam
2: naam van de werkgever, auto van de zaak of lease-auto
3: anders, te weten: ...
4: wil niet zeggen
Vraag 09
Rijden er regelmatig anderen met u mee?
Enq.: Minimaal 2 keer per week 1: ja
2: nee
3: wil niet zeggen
Als Vraag 09 is groter dan 1 dan door naar Vraag 12
Vraag 10
Hoeveel anderen?
1: 1 ander
2: 2 anderen
3: 3 of meer anderen
4: wil niet zeggen
Vraag 11
Wie zijn die anderen?
Als Vraag 10 is 1
Wie is die andere persoon?
1: partner
2: kind(eren)
3: collega
4: persoon/kennis die bij mij in de buurt werkt
5: (nog) anders, te weten: ...
6: wil niet zeggen
Spitsmijden | Experimental design and modelling
105
Vraag 12
Hoe lang duurt uw autorit naar deze vaste bestemming gemiddeld?
Enq.: WEET NIET / W.N.Z. is 999
Vraag 13
Hoe laat vertrekt u meestal van huis als u met de auto naar deze vaste bestemming gaat?
Vraag 14
Gebruikt u wel eens een ander vervoermiddel dan de auto?
1: ja
2: nee
3: wil niet zeggen
Als Vraag 14 is groter dan 1 dan door naar Vraag 17
Vraag 15
Met welk ander vervoermiddel gaat u ook wel eens naar deze vaste bestemming?
1: meerijden met iemand anders
2: motor
3: trein/RandstadRail
4: bus (Enq.: De bus vanuit Zoetermeer)
5: (brom)fiets
6: (nog) anders, te weten: ...
7: wil niet zeggen
Vraag 16A
Als Vraag 15 is 1
Vraag 16B
Als Vraag 15 is 2
Vraag 16C
Als Vraag 15 is 3
Vraag 16D
106
Hoe vaak rijdt u ongeveer met iemand anders mee naar de vaste bestemming?
1: 2 keer per week
2: 1 keer per week
3: ongeveer 1 keer per 2 weken
4: ongeveer 1 of 2 keer per maand
5: nog minder vaak dan 1 keer per maand
6: wil niet zeggen
Hoe vaak gaat u ongeveer met de motor naar de vaste bestemming?
1: 2 keer per week
2: 1 keer per week
3: ongeveer 1 keer per 2 weken
4: ongeveer 1 of 2 keer per maand
5: nog minder vaak dan 1 keer per maand
6: alleen bij mooi weer
7: wil niet zeggen
Hoe vaak gaat u ongeveer met de trein of RandstadRail naar de vaste bestemming?
1: 2 keer per week
2: 1 keer per week
3: ongeveer 1 keer per 2 weken
4: ongeveer 1 of 2 keer per maand
5: nog minder vaak dan 1 keer per maand
6: wil niet zeggen
Experimental design and modelling | Spitsmijden
Als Vraag 15 is 4
Vraag 16E
Als Vraag 15 is 5
Hoe vaak gaat u ongeveer met de bus naar de vaste bestemming?
1: 2 keer per week
2: 1 keer per week
3: ongeveer 1 keer per 2 weken
4: ongeveer 1 of 2 keer per maand
5: nog minder vaak dan 1 keer per maand
6: wil niet zeggen
Hoe vaak gaat u ongeveer met de (brom)fiets naar de vaste bestemming?
1: 2 keer per week
2: 1 keer per week
3: ongeveer 1 keer per 2 weken
4: ongeveer 1 of 2 keer per maand
5: nog minder vaak dan 1 keer per maand
6: alleen bij mooi weer
7: wil niet zeggen
Vraag 17
Is het openbaar vervoer voor u een realistisch alternatief voor uw woon-werkrit?
1: ja
2: nee
3: weet niet/w.n.z.
Vraag 18
Is de fiets of bromfiets voor u een realistisch alternatief voor uw woon-werkrit?
1: ja
2: nee
3: weet niet/w.n.z.
Vraag 19A1
Wat is de gebruikelijk begintijd van uw werk?
Enq.: WEET NIET/W.N.Z. is 99
Vraag 19B1
En wat is de gebruikelijk eindtijd van uw werk?
Enq.: WEET NIET/W.N.Z. is 99
Vraag 20
Hoe vaak per week kunt u, als u dat wilt, later dan gebruikelijk beginnen?
Enq.: KAN NIET LATER BEGINNEN is 0
WEET NIET/W.N.Z.is 99
Als Vraag 20 is 0 of 99 dan door naar Vraag 22
Vraag 21
Hoeveel minuten kunt u dan maximaal later beginnen?
Enq.: WEET NIET/W.N.Z. is 999
Vraag 22
Ik noem u een aantal situaties op die zouden kunnen ontstaan wanneer u VROEGER dan gebruikelijk op uw werk aankomt.
Kunt u steeds aangeven of die situatie voor u van toepassing is?
Spitsmijden | Experimental design and modelling
107
Enq.: LEES OP IN DE GOEDE VOLGORDE. INDIEN MEN ERGENS “JA” ZEGT, HOEVEN DE ANDERE MOGELIJKHEDEN NIET
MEER GENOEMD TE WORDEN.
1: ik kan direct aan mijn werk beginnen
2: ik kan nog niet echt beginnen, maar wel alvast voorbereidingen treffen voor
mijn werk
3: ik moet echt wachten op een bepaald tijdstip, voordat ik met mijn werk kan
beginnen (bijv. ploegendienst)
4: ik moet wachten op collega’s voordat ik met mijn werk kan beginnen
5: ik kan het gebouw niet in
6: (nog) anders, te weten: ...
7: weet niet / w.n.z.
Vraag 23
Ik noem u een aantal situaties die van invloed kunnen zijn op uw vertrektijden van
huis. Kunt u mij voor ieder van deze zeggen of dit voor u van toepassing is?
Enq.: LEES OP
1: zorg voor kinderen
2: samen willen ontbijten
3: het brengen van kinderen naar school of kinderopvang
4: het afzetten van partner bij zijn of haar werk (of anders)
5: carpoolafspraken
6: geen van deze
7: wil niet zeggen
Vraag 24
Zijn er nog andere redenen die voor u van invloed zijn op uw vertrektijd van huis?
En zo ja, welke redenen zijn dat?
1: ja, te weten: ...
2: nee
3: weet niet / w.n.z.
Vraag 25
Rekening houdend met de eerder genoemde privé- en werkomstandigheden; hoeveel minuten zou u dan maximaal EERDER dan dat u doorgaans doet van huis naar
uw werk kunnen vertrekken?
1: ... minuten eerder
2: kan niet, kan onmogelijk eerder vertrekken
3: weet niet/w.n.z.
Vraag 26
En ook weer rekening houdend met de eerder genoemde privé- en werkomstandigheden; hoeveel minuten zou u dan maximaal LATER dan dat u doorgaans doet
van huis naar uw werk kunnen vertrekken?
1: ... minuten later
2: kan niet, kan onmogelijk later vertrekken
3: weet niet/w.n.z.
Vraag 27
Zijn er bij de organisatie waar u werkt mogelijkheden om thuis te werken?
1: ja
2: nee
3: weet niet/w.n.z.
Vraag 28
Als Vraag 27 is 1
108
Hoeveel dagen per week kunt ú gemiddeld thuiswerken?
Experimental design and modelling | Spitsmijden
Enq.: Als respondent vanwege bijv. functie NIET kan thuiswerken, typ “0”
WEET NIET/W.N.Z. is 99
Vraag 29 INTRO
Ik wil nog even met u terugkomen op het spitsmijden. Onlangs heeft er een proef
plaatsgevonden waarbij automobilisten gedurende 10 weken een beloning van 5
euro ontvingen voor iedere dag dat ze niet in de spits met de auto naar hun werk
gingen.
Vraag 29
Stel dat dit experiment binnenkort herhaald zou worden en u om medewerking
gevraagd zou worden, zou u dan zeker wel, waarschijnlijk wel, misschien, waarschijnlijk niet of zeker niet aan deze proef meedoen?
1: zeker wel
2: waarschijnlijk wel
3: misschien
4: waarschijnlijk niet
5: zeker niet
6: weet niet / w.n.z.
Vraag 30
Als Vraag 29 is 1 of 2 of 3
Wat zouden voor u de motieven zijn om aan deze proef te willen meedoen?
Enq.: NIET OPLEZEN
1: de beloning
2: een bijdrage leveren aan de kennis over weggebruik in de spits
3: een bijdrage leveren aan de vermindering van de fileproblematiek
4: experimenteren met mogelijkheden om het eigen gedrag aan te passen
5: (nog) anders, te weten: ...
6: weet niet / w.n.z.
Vraag 31
Als Vraag 29 is 4 of 5
Waarom zou u niet aan de proef willen meedoen?
Enq.: NIET OPLEZEN, WEL DOORVRAGEN: En waarom nog meer niet?
1: ik kan vanwege werktijden de spits niet mijden
2: ik kan vanwege gezinsverplichtingen de spits niet mijden
3: ik heb geen alternatieve vervoermiddelen
4: ik vind de beloning niet voldoende
5: ik vind het teveel administratieve rompslomp
6: ik moet mijn gewoonlijke gedrag te veel aanpassen
7: (nog) anders, te weten: ...
8: weet niet/w.n.z.
Vraag 32INTRO
Tenslotte zou ik nog enkele algemene vragen willen stellen.
Vraag 32
Wat is uw leeftijd?
Enq.: WEET NIET/W.N.Z. is 999
Vraag 33
Enq.: NOTEER GESLACHT.
1: man
2: vrouw
Spitsmijden | Experimental design and modelling
109
Vraag 35
Wat is uw hoogst genoten schoolopleiding?
1: universiteit
2: HBO (HTS, HEAO, Sociale academie)
3: HAVO / VWO / HBS / Gymnasium / Lyceum / Atheneum
4: MBO (MTS, MEAO)
5: MAVO (MULO / VGLO)
6: VMBO/LBO (LEAO, LTS, huishoudschool)
7: uitsluitend lager -of basisonderwijs
8: geen onderwijs
9: weet niet / w.n.z.
Vraag 34
Hoe is het huishouden waartoe u behoort, samengesteld?
De hoofdkostwinner is ...
1: alleenstaand
2: getrouwd / samenwonend zonder kinderen
3: getrouwd / samenwonend met kinderen, jongste kind jonger dan 12 jaar
4: getrouwd / samenwonend met kinderen, jongste kind 12 jaar of ouder
5: alleenstaande ouder
6: anders, te weten: ...
7: weet niet / w.n.z.
Vraag 36
Wat is het bruto maandelijks inkomen van uw huishouden?
1: minder dan 2.000 euro
2: tussen 2.000 en 3.000 euro
3: tussen 3.000 en 4.000 euro
4: tussen 4.000 en 5.000 euro
5: tussen 5.000 en 6.000 euro
6: meer dan 6.000 euro
7: weet niet / w.n.z.
Vraag 37
Hoeveel personen werken er in de organisatie (of vestiging) waar u werkt?
Enq.: WEET NIET/W.N.Z. is 999999
Vraag 38
Tot welke sector behoort het bedrijf of de organisatie waar u werkt?
Enq.: Eerst respondent zelf proberen te antwoorden, anders op weg helpen.
1: landbouw, tuinbouw, visserij
2: industrie, openbare nutsbedrijven
3: bouw
4: groothandel, detailhandel
5: horeca
6: transport, communicatie
7: financiële of zakelijke dienstverlening
8: gezondheidszorg of welzijnszorg
9: onderwijs
10: overheid (gemeente, provincie, rijk)
11: anders, te weten: ____________________________________________________
12: weet niet / w.n.z.
110
Experimental design and modelling | Spitsmijden
Afsluiting
Afsluiting 2
Afsluiting 3
Afsluiting 4
Dan waren dit al mijn vragen. Ik dank u hartelijk voor uw medewerking aan dit
onderzoek en ik wens u verder een prettige dag/avond.
Dan waren dit al mijn vragen. U behoort helaas niet tot de doelgroep van het
onderzoek. Ik dank u voor uw medewerking en ik wens u verder een prettige
dag/avond.
Dan waren dit al mijn vragen. Wij interviewen personen die 3 keer of vaker per
week in de spits naar Den Haag rijden. Ik dank u voor uw medewerking en ik wens
u verder een prettige dag/avond.
Dan waren dit al mijn vragen. U behoort dan niet tot de doelgroep van het onderzoek. Ik dank u voor uw medewerking en ik wens u verder een prettige dag/avond.
Spitsmijden | Experimental design and modelling
111
Colophon
Publication Consortium Spitsmijden
Project management p2 managers, Rossum (the Netherlands)
Authors Dirk van Amelsfort, Michiel Bliemer, Dick Ettema, Dusica Joksimovic,
Jasper Knockaert (ed.), Albert Mulder, Jan Rouwendal
Translator UvA Vertalers, Amsterdam (the Netherlands)
Production CoMMunicom, Utrecht (the Netherlands)
Photography Roelof Pot, Kelle Schouten, Rein van der Zee
Design Raadgever en Partners, Amersfoort (the Netherlands)
Print work Drukkerij Tuijtel, Hardinxveld (the Netherlands)
© May 2007
This is a publication by Spitsmijden. Use of this information is permitted only
if reference is made to the Spitsmijden experiment.
THE SPITSMIJDEN EXPERIMENT IS AN INITIATIVE BY:
Ministerie van Verkeer en Waterstaat
Rijkswaterstaat
i.s.m.