Application of C11 Tools to a Sample of Existing TPICS Cases

Transcription

Application of the SHRP C11 Tools to a Sample of Existing TPICS Cases
SHRP2 Project C32:
Application of the SHRP C11
Tools to a Sample of Existing
TPICS Cases
Prepared for:
The Strategic Highway Research Program II (SHRP2),
Transportation Research Board (TRB)
Prepared by:
Economic Development Research Group, Inc.
155 Federal Street, Suite 600, Boston, MA 02110
with
Susan Moses & Associates
Texas A&M Transportation Institute
December 2014; rev March 2014
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The following examples and text were extracted from a forthcoming report entitled SHRP2
Project C32: Enhancement and Outreach for TPICS and Other Economic Analysis Tools.
Application of the SHRP C11 Tools to a Sample of
Existing TPICS Cases
This section presents the results of Task 6 which applied the C11 Analysis Tools to a set of nine
existing TPICS case studies. The cases were selected based on their project motivations, to
correspond to the three drivers of wider economic benefits captured by the C11 tools:
Accessibility, Connectivity (intermodal), and Reliability. The intent of this application was to: a)
demonstrate the use of the C11 tools to capture important wider economic effects of
transportation investments, and b) to expand our understanding of the relationship between ex
post TPICS case study findings and high-level estimates of economic impact from the C11 tools.
The C11 tools were designed to assess wider economic effects not captured by traditional travel
time valuations. This means they should not be expected to capture the economic impact of
travel time or distance savings which accrue to travelers as a result of improved transportation
system performance. Rather, the C11 tools capture broader economic effects above and
beyond those traditional performance effects.
The C11 tools generate estimates of GDP impacts from changes in reliability, market access, and
intermodal connectivity. Market access and intermodal connectivity are assessed using an
elasticity which produces estimates of the change in value added (GDP) from revenue growth.
Reliability is assessed using per-hour cost factors applied to estimates of buffer time for freight,
business, and, in certain cases, commuting trips. The resulting estimates can be interpreted as
cost savings accruing to businesses.
The impacts predicted by any of the three C11 tools should be less than the total economic
impacts of a transportation investment because they concentrate on one aspect of “wider
benefits” and therefore should, in most cases, also be less than the impacts identified by the
TPICS ex post case evaluations. However, because the TPICS case study methodology is best
suited to the identification of specific localized economic development and land use changes in
the vicinity of a highway project, there may be special cases in which dispersed economic
impacts resulting from a highway project are not fully captured in the TPICS economic impact
figures. In these cases, the TPICS case narrative includes discussion of dispersed impacts
associated with broad transportation performance changes in a region. Conversely, the C11
“wider impact” results are based on empirical relationships between transportation
performance and gains in productivity that are generalized for a region. Because each
approach varies in its methodology (e.g. ex post interviews versus generalized modeling),
results may be vary. For example, if productivity gains for a transportation project are
considered marginal at the business level for a metropolitan region, interviews conducted for a
TPICS case study may not unveil any observable impacts. However using one of the C11 tools
with empirically estimated aggregate elasticities will yield an estimate that combines marginal
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improvements into an economic impact gain for the same metropolitan region. Previous
research to date in the UK, Canada, and the US have found market access effects for major
projects in urban areas that account for as much as 30% of the total economic impact of a
project. Reliability effects should be expected to be only a fraction of the total economic
impacts of a project.
In addition to only capturing a portion of overall economic impacts due to changes in “wider
benefit” measures, the C11 tools are also subject to certain limitations because they were
designed to estimate aggregate economic impacts. They are not to be regarded as substitutes
for full economic modeling. In particular:

The elasticities of business response to improvements in market access or intermodal
connectivity are high-level empirical estimates of aggregate economic response. They
do not account for the ways in which one industry’s response to certain types of market
access might differ from another, nor do they account for differences in the
characteristics and size of a regional economy (e.g. differentiating responses for large
urban areas versus dispersed rural regions). To perform this type of differentiated
analysis requires a full economic modeling framework with detailed sector-specific data
and response elasticities.

Similarly, the per-buffer-hour cost factors used to assess reliability are based on an
average valuation of travel time and operating cost factors for truck transportation,
along with a reliability ratio which accounts for the average relationship between the
value of time and the value of reliability. The actual response of a particular industry to
reliability improvements can be higher or lower than these estimates. Certain sectors
are more sensitive to reliability than others. For these sectors, unreliability imposes
additional costs (beyond driver hourly costs) such as stock-out costs, labor cost for
workers at distribution and warehousing facilities, and costs associated with late
delivery of perishable goods or goods critical to just-in-time production processes.
These costs also have multiplier effects within the economy, as they affect the cost of
doing business along the supply chain.
Despite these limitations, the C11 tools are important to the suite of available options for
project evaluations (for either benefit-cost or economic impact assessments). They are
particularly suited to middle stage analysis when planners seek to understand the key drivers
and scale of project impacts. At a later stage of detailed evaluation, other more advanced tools
can be used to fully capture detailed economic impacts. With advanced modeling comes the
ability to address market segmentation as well as indirect and induced effects related to
supplier industries or the tracing of wages through the economy.
The subsequent sections present the results of nine applications of the C11 tools—three each
that address market access, reliability, and intermodal connectivity. Each application provides a
general overview of the TPICS project, details of the data collection process for the selected C11
tool, a summary of tool inputs, presentation of the tool results, and a discussion of how the C11
tool results relate to the ex post economic impacts identified in the TPICS case study.
------------------------------------------------------------3
Arizona State Highway 101: Application of SHRP2 C11 Reliability Tool
1.0 Overview
Arizona Loop 101 is a 62 mile beltway around the Phoenix metropolitan area. As a typical innerurban orbital beltway, it is designed to bypass congestion on routes through the metro area by
providing a direct circumferential connection between the outer suburbs. Construction of the
$2.8 Billion project (in 2008 dollars) began in the late 1980s and was completed in 2001. The
intent was to connect the rapidly-developing bedroom communities in the West Valley with the
suburban employment centers to the East in Tempe, Chandler, Gilbert, and Scottsdale. The
highway was routed through farmland and desert to the West and East of Phoenix. In response,
economic development growth began to occur at sites near planned exits, transforming open
space into high-density hubs of mixed use development.
Loop 101 provides a high speed, limited access, multi-lane route between Phoenix’s outer
suburbs, resulting in significant time savings over the growing congestion on other arterial
routes. From the west, Loop 101 provides a bypass for traffic which saves approximately 25
minutes over the former routes through Phoenix: East on I-10 then North on I-17 or South on I17 and then West on I-10 (see Figure 1). For traffic on the east side of Phoenix, time savings
using Loop 101 instead of traveling through the city center were found to be insignificant.
The C11 Reliability tool was only applied to the AZ Loop 101 segment between I-10 East and I17 North since this is the only section where estimated time savings occurred. This segment of
Loop 101 was treated as a capacity improvement to the existing I-10 (East & West) and I-17
(North & South) route as shown below in Figure 1.
Figure 1: Map of Arizona Route 101
AZ Hwy 101
section of analysis
I-17
AZ 101
I-10
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2.0 Data Collection Process
The base and build scenario inputs are shown in Table 1. In the base scenario, the volume of
vehicles on I-10, I-17, and AZ Loop 101 are forced to use the use the 4 one-way lane capacity of
I-10/I-17. In the project scenario AZ Loop 101 provides extra capacity (8 one-way lanes) to
handle vehicle volume for all three highways.
Arizona Department of Transportation (AZDOT) provided the number of lanes, AADT, traffic
growth rate (2002-2007) and percentage of trucks. Some state DOTs have this type of
information available on their websites; however in this particular case, information dating
back to 2002 was not posted on-line and required direct contact with AZDOT staff. Speed limits
on Urban Highways were provided by the Governors Highway Safety Association and peak
capacity values were estimated using the Highway Capacity Manual (HCM).
Table 1: Description and Source of Inputs
Data Element
Lanes
AADT
Speed Limit
Annual Traffic
Growth Rate
% Trucks
Peak Capacity
Description
Number of Lanes
Annual Average Daily Trips
Highway Speed Limit in Arizona
CAGR for I-10, I-17, & AZ 101
Data Source
Arizona Dept. of Transportation
Governors Highway Safety Assoc.
Percentage of all Traffic that are Trucks Arizona Dept. of Transportation
Vehicles Per Hour Per Lane * # of lanes Highway Capacity Manual
3.0 Summary of Inputs
All of the inputs required by the C11 Reliability tool are presented in Table 2 below. Arizona
Loop 101 was completed in 2001. Evaluation of reliability improvements were estimated for
2002 and 2007 (for a time horizon of 5 years) because economic development impacts often
take 5 to 10 years to unfold after construction completion. The Highway Type was selected as
“Freeway” since both I-10 and I-17 are Interstate Highways and AZ Loop 101 is a State Highway.
The average number of one way lanes on I-10 and I-17 was 4 which increased to a total of 8 on
the combined corridors with the addition of AZ Loop 101. The Annual Average Daily Trips
(AADT) was 163,910 for the I-10 to I-17 segment and 89,133 AADT for the AZ Loop 101
segment. These two estimates combined together equal 253,043 AADT for both highway
segments (I-10/I-17 and AZ Loop 101). Given data constraints, it is assumed that if AZ Loop 101
did not exist, all travelers would choose to travel on both I-10 and I-17. The average annual
traffic growth from 2002 to 2007 (4.5%) and the percentage of vehicles that are trucks (7.5%)
were both provided by AZDOT. The final input of peak capacity was calculated by multiplying
the average number of vehicles per hour per lane from the Highway Capacity Manual (HCM)
times the number of one-way lanes.
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Table 2: Project inputs for C11 Reliability tool
Reliability Inputs
Year
Time Horizon (# years)
Analysis Period
Highway Type
Number of Lanes (one-way)
Free Flow Speed
AADT
Annual Traffic Growth Rate
% Trucks in traffic
Peak Capacity (pcphr, one-way)
Base
2002
5
6am-7pm
Freeway
4
65
253,043
4.50%
7.50%
9,200
Build
2002
5
6am-7pm
Freeway
8
65
253,043
4.50%
7.50%
18,400
4.0 Results
After entering in the necessary inputs, the C11 Reliability tool provides the results in Table 3.
“Incident Equivalent Delay” represents the time saved due to a decrease in delays from
unexpected incidents for both passenger and commercial travel. Reductions in congestion
supported by the additional capacity serve to reduce both average travel time and the effect of
unexpected incident delay, thus improving reliability and on-time arrivals. “Base hours” and
“Project hours” columns present the vehicle hours of delay due to unreliable conditions. In the
year 2002 (first year after construction) if AZ Loop 101 did not exist, a total of 2.4 million
additional recurring vehicle delay hours and nearly 479,000 vehicle incident delay hours would
have occurred if travelers had to drive on I-10 and I-17 instead. After applying average vehicle
occupancy rates and average value of time, approximately $45 Million worth of recurring delay
and $9 Million worth of incident delay costs were saved by the existence of AZ Loop 101. Value
of time savings per hour for passengers represents a weighted average value for personal,
commuting, and business purposes ($11.85) and for commercial purposes reflects the average
value of time per hour for crew members ($26.89).
Table 3: Annual Reliability Savings 2002
Categories of Delay
Recurring Delay
Passenger Recurring
Commercial Recurring
Incident Delay
Passenger Incident
Commercial Incident
Change
Psgr. hours
Annual
Reliability
Savings
$45,225,815
Project
hours
Change in
veh. hours
2,413,017
25,479
2,387,538
2,218,144
22,927
2,195,217
1.5
3,292,825
$11.85
$39,019,977
194,874
2,552
192,322
1.2
230,786
$26.89
$6,205,838
478,853
117
478,736
427,242
101
427,141
1.5
640,711
$11.85
$7,592,426
51,611
16
51,595
1.2
61,914
$26.89
$1,664,874
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Veh.
Occp.
Value
of
Time
Base
hours
3,523,611
702,625
$9,257,300
Five years after the project, with a significant travel growth rate of 4.5%, the amount of time
savings due to reduced recurring delay is $126 Million. Savings due to improved reliability reach
$26 Million in the year 2007 as shown in Table 4.
Categories of Delay
Recurring Delay
Passenger Recurring
Incident Delay
Passenger Incident
Commercial Incident
Veh.
Occp.
Value
of
Time
Annual
Reliability
Savings
$126,506,741
$9,229,424
$11.85
$109,368,679
$637,340
$26.89
$17,138,061
Base hours
Project
hours
Change in
veh. hours
Change in
Psgr. hours
6,838,819
154,753
6,684,066
6,293,557
140,607
6,152,950
1.5
545,262
14,145
531,116
1.2
1,356,626
709
1,355,918
1,212,218
622
1,211,595
1.5
$1,817,393
$11.85
$21,536,106
144,408
86
144,322
1.2
$173,187
$26.89
$4,656,990
$9,866,764
$1,990,580
$26,193,096
5.0 Comparison to case study impact
The final stage of this analysis seeks to compare the ex post results found in the TPICS case
studies, which are based on actual observations, with the predicted impacts obtained from the
C11 tools. The C11 tools were designed to assess wider economic effects not captured by
traditional travel time valuations, such as reliability impacts. This means that they should not be
expected to capture the economic impact of savings in average travel time or distance which
accrue to travelers as a result of improved transportation system performance. Therefore,
reliability impacts should be viewed as only part of the story, relative to total economic impacts
of the AZ Loop 101 project.
To compare the reliability benefit from Table 4with the macro economic impacts reported by
the TPICS case study (which are expressed in terms of regional output), four additional steps
are necessary.
1) Adjustment to Consistent Year Basis. The benefits in Table 4were $126.5 million of
reduced travel time savings (recurring delay) and $26.2 million of reliability (nonrecurring) delays. These benefits are expressed in terms of the value of time savings,
calculated in terms of 2014 dollars. Converted to 2008 dollars to be comparable to
TPICS, these benefits would be $115.5 million and $23.8 million, respectively.
2) Adjustment to Consistent GDP Basis. The impact reported by TPICS is $7.117 billion/year
of business output, which is total business sales. Depending on an area’s industry mix,
GDP is typically in the range of ½ to ¾ the magnitude of output. So converting that
output change into GDP terms for comparability leads to a direct impact of around
$4.270 billion. This value is still 30 times larger than the delay and reliability benefits
alone.
3) Recognition of Other Sources of Economic Benefit. There are likely additional regional
benefits of the project which can occur in terms of travel speed limit increases, business
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operating cost reductions, collision reductions and localized land access enhancements
that are all in addition to the reduction in congestion bottleneck delays and
nonrecurring (reliability) delays. In this case study, the localized land development
impacts were documented.
4) Calculation of Impact on the Local Area Economy. Development along AZ Loop 101 was
driven by regional demographic and economic factors which gave shape and structure
to “mega-projects” than would not have been possible without direct freeway access to
accommodate large volumes of traffic. These include entertainment, retail, office, and
residential developments. This was also made possible by new zoning put in place along
AZ Loop 101 to encourage high density regional commercial development. The total
impact included over 27 major commercial development projects, comprising
investment of over $100 Billion influenced by AZ Loop 101. Impacts due to
improvements in reliability are included in these overall impacts but represent only a
small portion compared to economic development attracted to newly accessible land,
customer, and employees.
Table 5 compares the reliability benefit from Table 4 with the macro economic impacts
reported earlier by the TPICS case study, and shows how the preceding four adjustments come
into consideration.
Table 5: Comparison of TPICS Case Study Impacts with Calculations from the Reliability Tool
C11 Reliability Tool:
Productivity Impact
Reported by
TPICS Case Study
Value Derived from
TPICS Case Study
Reliability
Impact on
Productivity
Reduced Congestion Delay
$126.5 million/yr.(2014 $)
$115.5 million/yr. (2008 $)
Reliability Related Savings
$26.2 million/yr.(2014 $)
$23.8 million/yr. (2008 $)
--
--
Other Benefits
--
speed increases, cost
reductions, fewer collisions,
localized land access
Not Available
Area Economic
Growth Impact
--
Output: $7.117 billion direct
GDP: $4.270 billion direct
In general, reliability effects are an important component of the economic impacts of highway
projects, but only account for a small part of the overall story. For that reason, the preceding
discussion and table serve to highlight the need for additional calculations and assumptions to
fully assess the economic impacts associated with reliability enhancement projects. That
process could be accomplished more precisely by utilizing information on vehicle mix and trip
characteristics in an economic impact model. However, even without such a model, this case
serves to show that the reliability and congestion delay calculations made using the C11 tool
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are within the realm of believability and consistency with observed impacts reported by TPICS
case studies.
Big “I” Interchange (I-25 & I-40), Albuquerque, NM: Application of SHRP2 C11
Reliability Tool
1.0 Overview
By the late 1990s the “Big I” interchange in Albuquerque, New Mexico (I-25 and I-40), had
become the 10th worst bottleneck in the nation. Traffic volumes higher than anticipated
combined with obsolete design features such as quick weaves, short on-ramps and left-hand
exits contributed to significant congestion and frequent crashes. The redesign, fast tracked and
completed between 2000 and 2002, ultimately reduced congestion delays by more than 90%.
In addition to the physical redesign, an Intelligent Transportation System (ITS) to monitor traffic
flow and provide delay and routing information to drivers and agency personnel during and
after the project completion was also installed. Figure 2 below presents an aerial view of the
completed “Big I” interchange.
Figure 2: Aerial view of I-25 & I-40 Interchange in Albuquerque, MN
Source: www.businessinsider.com
The base and build scenario inputs are shown in Table 6. The New Mexico Department of
Transportation (NMDOT) provided data on number of lanes, AADT, traffic growth rate (20032008) and percentage of trucks. Some state DOTs have this type of information available on
their website. However in this particular case, information dating back to 2003 was not posted
on-line and required direct contact with NMDOT staff. Speed limits on Urban Highways were
9
provided by the Governors Highway Safety Association and peak capacity values were
estimated using the Highway Capacity Manual (HCM). Because Intelligent Transportation
Systems (ITS) were included in the project, input estimates of a reduction in incident frequency
and duration were also included.
Data Element
Lanes
AADT
Speed Limit
Traffic Growth Rate
% Trucks
Peak Capacity
Reduction in Incident
Frequency/Duration
Description
Number of Lanes
Highway Speed Limit in New Mexico
C.A.G.R.1 for I-25 & I-40 from 20032008
Percentage of all Traffic that are Trucks
Passenger Cars Per Hour Per Lane * # of
lanes
Reduction in incident
frequency/duration, expressed as a
percentage, due to the addition of an
incident management program/
strategy
Data Source
New Mexico Dept. of Transportation
Governors Highway Safety Association
Highway Capacity Manual
Doyle & Magno. "Big I (I-40/I-25)
Reconstruction and ITS Infrastructure"
FHWA & Mid-Region Council of
Governments. 20092.
All of the inputs required by the C11 Reliability tool are presented in Table 7 below. The I-25/I40 interchange was completed in 2002. Estimates for improvements in reliability were
calculated for years 2003 and 2008 (a 5 year time horizon) because economic development
impacts often take 5 to 10 years to unfold after construction completion. The Highway Type
was selected as “Freeway” since both I-25 and I-40 are Interstate Highways. Interstate 25 was
expanded by two one-way lanes and I-40 was expanded by one one-way lane for a total of
three additional one-way lanes. The Annual Average Daily Trips (AADT) for both I-25 and I-40
was 303,034 in 2003. Information provided by NMDOT was used to estimate average traffic
growth from 2003 to 2008 along both interstates in the interchange (3.2%) and the percentage
of vehicles that are trucks (11.8%). Peak capacity was calculated by multiplying the average
number of vehicles per hour per lane (2,300) from the Highway Capacity Manual (HCM) times
the number of one-way lanes. Because the C11 tool is more suited to open stretches of
highway rather than interchanges, these numbers will yield high-level approximations of
congestion effects. Closed circuit television (CCTV), dynamic messaging signs, portable traffic
management systems, and safety service patrol, all part of an integrated ITS, were estimated to
reduce incident frequency and duration by 10%, a conservative estimate.
1
C.A.G.R. = Compounded Annual Growth Rate.
http://www.ttsitalia.it/wp-content/uploads/2010/04/big_i__i-40_i25__reconstruction_and_its_infrastructure.pdf
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Reliability Inputs
Year
Analysis Period
Highway Type
Free Flow Speed
AADT
% Trucks in traffic
Peak Capacity (pcphpl, one-way)
Reduction in Incident Frequency
Reduction in Incident Duration
Base
2003
5
6am-7pm
Freeway
6
65
303,034
3.2%
11.8%
13,800
0%
0%
Build
2003
5
6am-7pm
Freeway
9
65
303,034
3.2%
11.8%
20,700
10%
10%
4.0 Results
After entering in the necessary inputs, the C11 Reliability tool provided the results in Table 8 for
the year 2003. The table presents the changes in vehicle hour delay, passenger hour delay, and
annual delay savings for both Recurring (congestion) and Incident (variable) delay for passenger
and commercial travel. Recurring delay is due to congested conditions where speeds are
consistently below free flow conditions. In this example the “Big I” project was estimated to
reduce over 1.5 million vehicle hours (a 99% reduction) and 2.3 million passenger hours caused
by Recurring delay. Incident delay represents the time saved due from decreased variation in
travel time as travelers experience fewer unexpected delays. The “Big I” project resulted in a
decrease of almost 160,000 vehicle hours and over 232,000 passenger hours caused by Incident
delay. After applying average values of time, project savings are estimated to be $30 Million in
time savings from reduced congestion (Recurring delay) and over $3 Million in time savings
from reduced variation in travel times (Incident delay).
11
Categories of Delay
Base
hours
Project
hours
Recurring Delay
Passenger Recurring
Incident Delay
Passenger Incident
Commercial Incident
1,572,280
1,392,049
180,231
159,950
135,778
24,172
22,273
19,647
2,626
67
56
10
Change in
veh. hours
1,550,008
1,372,402
177,606
159,883
135,722
24,161
Veh.
Occp.
1.5
1.2
1.5
1.2
Change in
Psgr.
hours
2,271,730
2,058,603
213,127
232,576
203,583
28,994
Value
of
Time
$11.85
$26.89
$11.85
$26.89
Annual
Reliability
Savings
$30,125,426
$24,394,445
$5,730,981
$3,192,093
$2,412,453
$779,640
Five years after the project, with a travel growth rate of 3.2%, time savings from reducing
congestion delay increased to nearly $47 Million per year and times savings from reducing
variation in travel time increased to over $5 Million per year as shown in Table 9.
Veh.
Occp.
Rate
Annual
Reliability
Savings
Project
hours
Change in
hours
2,552,302
146,653
2,405,649
2,259,730
129,364
2,130,366
1.5
3,195,549
$11.85
$37,867,255
292,572
17,289
275,283
1.2
330,340
$26.89
$8,882,836
259,648
439
259,210
Passenger Incident
220,410
371
220,040
1.5
330,059
$11.85
$3,911,203
Commercial Delay
39,238
68
39,170
1.2
47,004
$26.89
$1,263,944
Categories of Delay
Recurring Delay
Passenger Recurring
Incident Delay
Passenger
hours
Value
of
Time
Base
hours
3,525,889
$46,750,091
377,064
$5,175,147
1) Adjustment to Consistent Year Basis. The benefits in Table 9 were $46.7 million of
2) Adjustment to Consistent GDP Basis. The impact reported by TPICS is $0/year of
business output, which is total business sales. Economic developers at both the state
and local level stated that although the project is considered a key regional asset, it did
not play a role in the recruitment or expansion of any specific firm.
3) Recognition of Other Sources of Economic Benefit. The re-construction of the Big I
interchange in Albuquerque not only reduced congestion, bringing associated delay
reduction and reliability improvement, but it also increased traffic speeds, enhanced
capacity and improved safety. Thus, the congestion delay and reliability benefits were
only one element of the overall impact.
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4) Calculation of Impact on the Local Area Economy. Although congestion and reliability
benefits were identified, they likely accrued at the regional level whereas the case study
focused on identifying only local impacts.
Table 10 compares the accessibility benefit from Table 9 with the macro economic impacts
reported earlier by the TPICS case study. It shows that while the reliability tool calculated over
$47 million/year of delay and reliability improvement, there was no measurable impact on local
or regional economic growth.
Reliability
Impact on
Productivity
Productivity Impact
$46.7 million/yr.(2014 $)
$42.5 million/yr. (2008 $)
$5.2 million/yr.(2014 $)
$4.7 million/yr. (2008 $)
Reported by
TPICS Case Study
Value Derived from
TPICS Case Study
--
--
Not Available
GDP: $0 direct
Other Benefits
--
Capacity increases, cost
reductions, regional
access
Area Economic
Growth Impact
--
Output: $0 direct
The reason for the lack of observed local impact is simple -- the area around the Big I was
essentially built-out long before the interchange redesign began in 2000. Office and industrial
uses dominate the areas north of the interchange, residential and institutional uses are
immediately southwest of the interchange, and residential neighborhoods characterize the
southeast quadrant. Freeway-visible commercial businesses already line much of I-25 north of
the interchange all the way to Alameda Boulevard, and significant concentrations can also be
found along I-40 east of the Big I. As a result, the Big I replacement project did not significantly
alter nearby land use patterns and was not determined by the TPICS ex post assessment to
contribute to any localized economic development impacts associated with new businesses, or
increased property values. Furthermore, since the completion of the Big I interchange,
employment within Albuquerque has grown less rapidly than in the rest of Bernalillo County,
and both Albuquerque and the county have experienced slower job growth than the state as a
whole.
Employment trends in Albuquerque’s leading industries are largely driven by factors outside the
region such as national defense spending levels and federal Department of Energy research
budgets and priorities. Population has followed these jobs. Albuquerque has also experienced
growth as a result of nationwide shifting of jobs and population to the southwest. These factors
appear to have been far more influential on demographic, economic and land use trends and
patterns than the replacement of the Big I interchange, despite its undisputable impacts in
terms of congestion reduction.
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All of the interview findings point to the same conclusion – that this project did not cause new
development to occur, but enabled existing economic growth patterns to continue. In other
words, the project prevented the prospect of economic stagnation that could have occurred if
there was not continued investment in capacity to accommodate the continued growth.
This application of the C11 reliability tool to the Big I Interchange highlights (a) the importance
of reliability effects for highway project that significantly improve congestion, and (b) the fact
that ex post assessments of localized economic development impacts, such as those
undertaken as part of the TPICS case studies, may not be able to fully identify dispersed
regional economic impacts associated with travel time or reliability savings.
I-15 Reconstruction, Salt Lake City, UT: Application of SHRP2 C11 Reliability Tool
1.0 Overview
The $1.5 Billion I-15 Reconstruction Project involved the rebuilding and widening of a
deteriorated congested 17-mile stretch of I-15 running through Salt Lake City. The project was
necessary to accommodate the rapid growth the region was experiencing. State-of-the-art
technology was used to keep the traffic moving during construction and to expedite the
construction schedule in order to finish the project before the 2002 Winter Olympics. The new,
expanded section of highway increased peak hour freeway speeds by 20% and decreased
freeway delay by 36%.
Figure 3: Construction work on I-15
Source: Salt Lake Tribune
14
The SHRP C11 Reliability spreadsheet tool is a sketch planning tool that involves minimal data
inputs and calibration. It provides estimates of recurring and incident (nonrecurring) delay using
predictive equations that relate capacity information to reliability performance.
The base and build scenario inputs are shown in Table 11. The Utah Department of
Transportation (UDOT) data on the number of lanes, AADT, traffic growth rate (2001-2006) and
percentage of trucks. Information on traffic dating back to 2000 is available on UDOT website3.
Speed limits on urban highways were provided by the Governors Highway Safety Association
and peak capacity values were derived from the Highway Capacity Manual (HCM). Because
Intelligent Transportation Systems (ITS) were included in the project, input estimates for
reduction in incident frequency and duration were also included.
Data Element
Lanes
AADT
Speed Limit
Annual Traffic Growth
Rate
% Trucks
Peak Capacity
Reduction in Incident
Frequency / Duration
Description
Number of Lanes
Highway Speed Limit in Utah
Data Source
Utah Dept. of Transportation
Utah Dept. of Transportation
Governors Highway Safety
Association
Compound Annual Growth Rate for I-15 Utah Dept. of Transportation
for 2001-2006
Percentage of all Traffic that are Trucks Utah Dept. of Transportation
Passenger Cars Per Hour Per Lane
Highway Capacity Manual
multiplied by the number of lanes
Reduction in incident
Association for Commuter
frequency/duration, expressed as a
Transportation. "Mitigating Traffic
percentage, due to the addition of an
Congestion - the Role of Demandincident management program/
Side Strategies." U.S. DOT-FHWA.
strategy
20044
All of the inputs required by the C11 Reliability tool are presented in Table 12 below. The I-15
Project was completed in July of 2001 and estimates for improvements in reliability were
calculated for the years 2002 and 2007 (a 5 year time horizon) because economic development
impacts often take 5 to 10 years to unfold after construction completion. The Highway Type
was selected as “Freeway” since I-15 is an Interstate Highway. I-15 was expanded from three
one-way lanes to five one-way lanes. The Annual Average Daily Trips (AADT) on I-15 from State
Road 151 (10600 South Sandy) to State Road 268 (600 North) was 174,341 in 2002. It was
assumed that this volume of traffic would have remained on I-15, using three one-way lanes,
3
http://www.udot.utah.gov/main/f?p=100:pg:0::::V,T:,529
4
http://ops.fhwa.dot.gov/publications/mitig_traf_cong/slc_case.htm
15
had the widening to five one-way lanes not occurred. Information provided by UDOT was used
to estimate average traffic growth from 2002 to 2007 along the interstate (1.36%) and the
percentage of vehicles that are trucks (9.1%). Peak capacity was calculated by multiplying the
average number of vehicles per hour per lane (2,300) provided from the Highway Capacity
Manual (HCM) times the number of one-way lanes. An ITS system, CommuterLink, was
installed to improve traffic management. This included a Traffic Operations Center (TOC),
control software, and field equipment (e.g. VMS signs, cameras, signal controllers, coordinated
signals, ramp meters, and speed/volume/weather/pavement sensors). During its first years of
operation, CommuterLink was attributed with the following successes:

Increased peak hour freeway speeds by 20%,

Decreased freeway delay by 36%,

Decreased traffic signal stops by 15%, and

Decreased intersection delay by 27%.
Based on the above, a conservative estimate of 20% reduced incident frequency and duration
was entered into the tool.
Reliability Inputs
Base
Year
2002
5
Analysis Period
6am-7pm
Highway Type
Freeway
3
Free Flow Speed
65
AADT
174,341
1.36%
% Trucks in traffic
9.1%
Peak Capacity (pcphr, one-way)
6,900
Reduction in Incident Frequency
0%
Reduction in Incident Duration
0%
Build
2007
5
6am – 7pm
Freeway
5
65
174,341
1.36%
9.1%
11,500
20%
20%
4.0 Results
After entering in the necessary inputs, the C11 Reliability tool provided the results in Table 13
for the year 2002. The table presents the changes in vehicle hour delay, passenger hour delay,
and annual delay savings for both Recurring (congestion) and Incident (variable) delay for
passenger and commercial travel. Recurring delay is due to congested conditions where speeds
are consistently below free flow conditions. In this example the I-15 Reconstruction project
was estimated to reduce over 1 million vehicle hours and over 1.6 million passenger hours
caused by Recurring delay. Incident delay, on the other hand, represents the time saved from
decreased variation in travel time as travelers are able to reduce the “padding” of their
scheduled departures (extra buffer time is added in order to prevent late arrivals due to
16
unexpected delay). The I-15 project resulted in a decrease of over 155,000 vehicle hours and
over 228,000 passenger hours caused by Incident delay. After applying average values of time,
estimated project savings amount to almost $21 Million in time savings from reduced
congestion (Recurring delay) and over $3 Million in time savings from reduced variation in
travel times (Incident delay).
Categories of Delay
Recurring Delay
Passenger Recurring
Incident Delay
Passenger Incident
Commercial Incident
Veh.
Occp.
Rate
Passenger
hours
Value
of
Time
Annual
Reliability
Savings
Base
hours
Project
hours
Change in
hours
1,105,974
11,962
1,094,012
1,009,284
10,874
998,409
1.5
1,497,614
$11.85
$17,746,727
96,690
1,088
95,603
1.2
114,724
$26.89
$3,084,915
155,672
33
155,639
137,553
29
137,524
1.5
206,286
$11.85
2,444,490
18,119
4
18,115
1.2
21,738
$26.89
584,547
1,612,338
$20,831,642
228,025
$3,029,037
Five years after the project, with a travel growth rate of 1.36%, time savings from reducing
congestion delay increased to over $41 Million per year and times savings from reducing
variation in travel time increased to over $5.9 Million per year as shown in Table 14.
Categories of Delay
Recurring Delay
Passenger Recurring
Incident Delay
Passenger Incident
Commercial Incident
Veh.
Occp.
Rate
Passenger
hours
Value
of Time
Annual
Reliability
Savings
Base
hours
Project
hours
Change in
hours
2,177,507
16,412
2,161,095
1,987,137
14,921
1,972,217
1.5
2,958,325
$11.85
$35,056,149
190,370
1,492
188,878
1.2
226,654
$26.89
$6,094,720
306,497
60
306,437
270,822
52
270,770
1.5
406,155
$11.85
$4,812,933
35,674
7
35,667
1.2
42,801
$26.89
$1,150,911
3,184,979
$41,150,869
448,955
$5,963,844
To compare the reliability benefit from Table 14 with the macro economic impacts reported by
the TPICS case study (which are expressed in terms of regional output), four additional steps
are necessary.
1) Adjustment to Consistent Year Basis. The benefits in Table 14 were $41.1 million of
17
2) Adjustment to Consistent GDP Basis. The impact reported by TPICS is $1.02 billion/year
of business output, which is total business sales. Depending on an area’s industry mix,
GDP is typically in the range of ½ to ¾ the magnitude of output. So converting that
output change into GDP terms for comparability leads to a direct impact of around $612
million. This value is still 14 times larger than the congestion delay and reliability
benefits alone.
3) Recognition of Other Sources of Economic Benefit. The re-construction of I-15 not only
reduced congestion, bringing associated delay reduction and reliability improvement,
but it also increased traffic speeds, enhanced capacity and improved safety. Thus, the
congestion delay and reliability benefits were only one element of the overall impact.
4) Calculation of Impact on the Local Area Economy. It was the joint outcome of all of the
above-noted effects which enabled the project to have a strong impact on the economic
development on Salt Lake County and Utah. Based on interviews and a comparison
analysis, the estimated number of jobs created by the re-construction of I-15 is 7,500
enabling over $1 billion/year of direct growth in business output.
5) The economic effect was strongly focused on growth of distribution, manufacturing, and
warehousing firms – growth already occurring on the west side of Salt Lake City that
could not have continued without the investment in added highway capacity to prevent
further congestion delays. Altogether, over 70 million square feet of space added,
including 12 million square feet of office space. In these areas, the City focused their
planning efforts on supporting commercial growth and some limited residential growth.
Of the businesses in the industrial/warehousing sector, roughly half already existed in
the corridor and the remainder located or relocated there after the reconstruction was
completed. Salt Lake City is becoming western regional transportation hub, a “cross
roads of the west”, and rail transportation is increasingly being used.
reported earlier by the TPICS case study, and shows how the preceding four adjustments come
into consideration.
Reliability
Impact on
Productivity
Productivity Impact
$41.1 million/yr.(2014 $)
$37.4 million/yr. (2008 $)
$5.9 million/yr.(2014 $)
$5.4 million/yr. (2008 $)
Reported by
TPICS Case Study
Value Derived from
TPICS Case Study
--
--
Other Benefits
--
Capacity & speed increases,
cost reductions, fewer
collisions, local land access
Not Available
Area Economic
Growth Impact
--
GDP: $612
million direct
18
Although the contribution of over $5.4 million in savings due to improvement in reliable travel
along I-15 is significant, the vast majority of the impacts are largely attributed to new
distribution, manufacturing, and warehousing businesses that moved to the region because
they highly value access to the transportation network. Reliability-related impacts are
important to consider and value, but only account for one component of a given highway
project’s economic impacts.
The preceding discussion and table highlight the need for additional calculations and
assumptions to fully assess the economic impacts associated with reliability enhancement
projects. That process could be accomplished more precisely by utilizing information on vehicle
mix and trip characteristics in an economic impact model. However, even without such a
model, this case serves to show that the reliability and congestion delay calculations made
using the C11 tool are within the realm of believability and consistency with observed impacts
reported by TPICS case studies.
Corridor Q, Appalachia: Application of SHRP2 C11 Accessibility Tool
1.0 Overview
Built on pre-existing US highways, Corridor Q is an east-west oriented four-lane highway that
runs through Kentucky, Virginia, and West Virginia and is part of the Appalachian Development
Highway System (Figure 4 and Figure 5). The TPICS case study covers a section that runs
through four counties in Virginia and one county in West Virginia. The analyzed section begins
at Grundy and terminates at Christiansburg. The corridor was constructed between 1967 and
1986. In addition to the material provided in the TPICS case study, additional data on the
impacts of the project are available from an economic impact study performed in 1998, using
1995 data.5
Figure 4: Corridor Q alignment in Virginia
Source: ARC. Status of Corridors in Virginia.
http://www.arc.gov/images/programs/transp/adhs_status_report_2011/ADHS2011StatusReportVirginia.pdf
Wilbur Smith Associates. Appalachian Development Highways Economic Impact Studies. July 1998.
Retrieved from: http://tpics.us/attachments/ADHS%20Economic%20Impact%20Studies.pdf
5
19
Figure 5 Corridor Q alignment in West Virginia
Source: ARC. Status of Corridors in Virginia.
http://www.arc.gov/images/programs/transp/adhs_status_report_2011/ADHS2011StatusReportWestVirginia.pdf
This case study was identified for analysis using the C11 Effective Density Market Access Tool
because the case narrative explicitly points to the importance of increased accessibility for
commuting trips:
The travel savings resulting from the corridor improvements in some cases
altered regional travel patterns, especially commuting patterns. Effective
commuting distances and times to places of employment have been reduced
sufficiently to enable cross-jurisdictional movements of residents within the
region. Commuting between Mercer County and Tazewell County, and Mercer
and Montgomery Counties has increased since the corridor development and
increased the economic interconnectivity of the region.6
Because of the focus on commuting, rather than freight access, the C11 tool was used with
population data to represent access to labor.
The following table summarizes the data elements used as inputs to the effective density
calculation, along with information about data sources, and any preliminary calculations used
to derive appropriate inputs. The year of analysis was chosen to be 1995 in order to match the
year for which information is available about transportation performance changes from the
ADHS economic impact study.7 The project team conducted the analysis using a county zonal
system.
TPICS. Corridor Q, Appalachia. Narrative. Retrieved from:
http://tpics.us/CaseSelected.aspx?case_study_id=59
6
7
Wilbur Smith Associates. Appalachian Development Highways Economic Impact Studies. July 1998.
20
Table 16: Source and derivation of tool inputs
Data
Element
Activity
GRP Proxy
Description


Population, 1995
Buchanan, Tazewell, Giles, and
Montgomery counties, VA; Mercer, WV

Total Earnings by Place of Work in 1995
from the BEA, for the 5 counties
Total Employment in 1995
Calculate Earnings by Place of Work per
Employee
State GDP and Total Earnings by Place
of Work for 1995, for VA and WV
Calculate Ratio of GDP: Earnings for VA
and WV
Apply ratio to Earnings by Place of Work
per Number of Employees, to get per
employee GRP proxy
Adjust result from 1995 to 2014 $, using
BLS CPI
Origin points based on the settlement
patterns - major towns, or in the case of
Tazewell, the midpoint of a linear
distribution of population centers
Inter-zonal: current Google Maps travel
times
Intra-zonal: based on area of county,
the "radius" method (as if the county
were a circle), and a speed of 30 mph,
based on sample of speeds on local
roads obtained from Google Maps (and
constrained to be no higher than base
case inter-zonal speeds).
1998 Economic Impact Study developed
VMT and VHT estimates with and
without Corridor Q
Use Car VMT and VHT to calculate the
ratio between base and build speeds
(Car because we are interested in
commuting).






Impedance Build



Impedance –
Base
Data Source


21
US Census. Population
Estimates: Virginia and
West Virginia. Retrieved
from:https://www.census.g
ov/popest/data/counties/a
srh/1990s/CO-99-09.html
BEA. Regional Data: GDP &
Personal Income. CA05
Personal income by major
source and earnings by SIC
industry. Earning by place
of work. Retrieved from:
http://www.bea.gov/iTable
/index_regional.cfm
BLS. CPI Inflation
Calculator.
http://www.bls.gov/data/in
flation_calculator.htm
Google Maps
https://www.google.com/
maps/
Wilbur Smith Associates.
Appalachian Development
Highways Economic Impact
Studies. July 1998. Retrieved
from:




Apply ratio of speeds to existing interzonal speeds (using Google Maps info
on travel time and distance); calculate
new inter-zonal travel times.
Apply half the inter-zonal percent speed
change to the intra-zonal speeds,
because Corridor Q will only be used for
a portion of intra-zonal trips (assumes
approximately 50% of an intra-zonal trip
will occur on corridor Q); calculate new
intra-zonal travel times.
Result is a uniform 36% reduction in
impedance from based to build for
inter-zonal travel times, and an 18%
reduction for intra-zonal travel times.
Note: for inter-zonal impedances,
Corridor Q comprises nearly 100% of
the route, and so the speed
improvement is applied to the entire
length of each inter-zonal trip.
http://TPICS.us/attachment
s/ADHS%20Economic%20I
mpact%20Studies.pdf
Exhibit 3-24. 1995 Daily
Vehicle Miles and Hours of
Travel With and Without
ADHS Improvements
(1,000’s)
Parameters
In addition to the data inputs which characterize the project, the following parameters were
selected for the tool calculations, to appropriately capture the effects of improvements to labor
market access.
Table 17: Parameters for C11 Effective Density calculations
Parameter
Productivity
elasticity
Decay factor (α)
Value
 Value of 0.05, for commuting, which particularly affects service industries
 Source: Accounting Framework Tool. Retrieved from:
http://TPICS.us/tools/documents/Accounting%20Framework-Final.xlsx
 1.8 based on the decay factor put forward by Graham, et al. for
consumer/business services (which are most sensitive to shorter-distance
commuting relationships, as opposed to longer distance freight
relationships)
 Sensitivity test with an a decay factor of 1, according to Graham, et al.,
which is the value used for manufacturing
 Source: Graham, et al. TRANSPORT INVESTMENT AND THE DISTANCE
DECAY OF AGGLOMERATION BENEFITS. 2009. Retrieved from:
http://personal.lse.ac.uk/gibbons/Papers/Agglomeration%20and%20Dista
nce%20Decay%20Jan%202009.pdf
22
The following tables summarize the final data inputs entered into the tool.
Table 18: Activity – Population, 1995, Base & Build
Zone
Buchanan, VA
Tazewell, VA
Mercer, WV
Giles, VA
Montgomery, VA
Population
30,440
47,044
64,673
16,319
75,664
Table 19: GRP Proxy per Employee
Zone
Buchanan, VA
Tazewell, VA
Mercer, WV
Giles, VA
Montgomery, VA
GRP per
Employee (2014 $)
$73,525
$54,462
$62,242
$67,306
$60,710
Table 20: Zonal Employment
Zone
Buchanan, VA
Tazewell, VA
Mercer, WV
Giles, VA
Montgomery, VA
Employment
11,706
20,183
28,388
7,462
50,071
Table 21: Impedance – Base
(Travel Time, Hrs)
Buchanan, VA
Tazewell, VA
Mercer, WV
Giles, VA
Montgomery, VA
Buchanan,
VA
0.52
1.753
2.381
3.270
3.977
Tazewell,
VA
1.753
0.52
0.942
1.884
2.668
23
Mercer, WV
Giles, VA
2.381
0.942
0.47
1.203
1.936
3.270
1.884
1.203
0.44
0.811
Montgomery,
VA
3.977
2.668
1.936
0.811
0.45
Table 22: Impedance – Build
(Travel Time, Hrs)
Buchanan, VA
Tazewell, VA
Mercer, WV
Giles, VA
Montgomery, VA
Buchanan,
VA
0.42
1.117
1.517
2.083
2.533
Tazewell,
VA
1.117
0.43
0.600
1.200
1.700
Mercer, WV
Giles, VA
1.517
0.600
0.39
0.767
1.233
2.083
1.200
0.767
0.36
0.517
Montgomery,
VA
2.533
1.700
1.233
0.517
0.37
4.0 Results
Based on the above outlined inputs, the following tables summarize results from the Effective
Density tool. The results are presented using both the adopted decay value of 1.8, and the
sensitivity test value of 1 which allows for a more gradual spatial decay. They indicate an effect
on productivity (equivalent to a value added or GDP increase) that is in the range of $103 to
$162 Million per year.
Table 23: Effective density and productivity impact results, adopted decay parameter of 1.8
(alpha), in 2014 dollars
Alpha = 1.8
NO BUILD
1995
BUILD
1995
ZONES
EFFECTIVE
DENSITY
EFFECTIVE % Change
DENSITY
in ED
Buchanan, VA
Tazewell, VA
Mercer, WV
Giles, VA
Montgomery, VA
TOTAL
139170
251879
343901
248054
369319
1352323
231398
443974
569592
499014
573778
2317756
66%
76%
66%
101%
55%
71%
24
Per
Worker
GRP
$73,525
$54,462
$62,242
$67,306
$60,710
Zonal
EMP
Productivity
Impact
11,706
20,183
28,388
7,462
50,071
$22,160,803
$31,598,090
$45,143,775
$17,863,339
$67,707,532
$162,312,735
Table 24: Effective density and productivity impact results, using sensitivity value of 1 for
alpha, in 2014 dollars
Alpha = 1
NO BUILD
1995
BUILD
1995
ZONES
EFFECTIVE
DENSITY
EFFECTIVE
DENSITY
Buchanan, VA
Tazewell, VA
Mercer, WV
Giles, VA
Montgomery, VA
TOTAL
137038
212846
252570
218756
246003
1067213
194569
302853
348714
330350
327966
1504452
Per
% Change
Worker
in ED
GRP
42%
$73,525
42%
$54,462
38%
$62,242
51%
$67,306
33%
$60,710
41%
Zonal
EMP
Productivity
Impact
11,706
20,183
28,388
7,462
50,071
$15,217,595
$19,555,237
$28,728,439
$10,458,476
$44,023,231
$102,765,383
5.0 Comparison to Case Study Impact
To compare the accessibility benefit from Table 23 (expressed in terms of value added or GDP
equivalent terms) with the macro economic impacts reported by the TPICS case study (which
are expressed in terms of regional output), four additional steps are necessary.
1) Adjustment to Consistent Year Basis. The accessibility impact on productivity that was
predicted by the C11 tool is $162 Million per year in 2014 dollars. Converted to 2008
dollars to be comparable to TPICS, the estimated accessibility impact value would be
$148 Million per year.
2) Adjustment to Consistent GDP Basis. The impact reported by TPICS is $748 Million per
year of business output (which is total business sales). Depending on an area’s industry
mix, GDP is typically in the range of 1/2 to 3/4 the magnitude of output. So converting
that output change into GDP terms for comparability leads to a direct impact of around
$448 Million.
3) Recognition of Other Sources of Economic Benefit. Accessibility benefits are additional
to travel time and operating cost savings benefits, which are typically the largest factors
driving economic benefits. The TPICS case study narrative for Corridor Q reported that
the project led to a VHT savings of 22.7 million hours per year. and a VMT saving of 87.3
travel miles per year. From a benefit-cost perspective, the travel time savings has a
value in the range of $300 – 400 Million per year, and the travel mileage savings has a
value in the range of $50 to 60 Million per year, depending on mix of vehicles and trip
purposes.The total social benefit is the sum of the accessibility, time and cost savings
effects (as well as additional collision and environmental benefits which are not
measured here).Thus, from a social benefit perspective, the accessibility effect
represents no more than 25-30% of the total benefit, which is well within the bounds of
reasonable expectations, and consistent with other benefit/cost studies conducted in
the US, Canada and UK .
25
4) Calculation of Impact on the Area Economy. The impact on a region’s economy depends
on both the productivity benefit of accessibility enhancement (here $148 Million per
year) and the productivity from business cost savings (associated with the business and
worker related travel time and expense changes), as well as factors related to labor cost
and business competitiveness (which are not known here). The business productivity
gain requires information on vehicle and trip purpose mix, as well as regional economic
mix, but in this case it is likely to be in the range of 40 – 50% of the total value of social
benefits from VMT and VHT savings described above (in the range of $160 - $230 Million
per year). Savings in wage premiums are most common in rural areas affected by
bottlenecks, and that is likely to bring the business cost savings to the upper end of the
range in this particular case. Summing the accessibility impact and the business cost
savings effects lead to an expected GDP increase in the range of $308 – 378 Million per
year, which is consistent with the TPICS finding noted in preceding item 2.
reported earlier by the TPICS case study, and shows how the preceding four adjustment
calculations can be used.
Table 25: Comparison of TPICS Case Study Impacts with Calculations from the C11
Productivity Tool
C11 Effective Density
Tool: Productivity Impact
Accessibility
Impact on
Productivity
Other Benefits
Area Economic
Growth Impact
Reported by TPICS
Case Study
Value Derived from TPICS
Case Study
103-162 million (2014 $)
93-148 million (2008 $)
VHT: -22.7 mil
hrs/yr.
VMT: -87.3 miles/yr.
Output (2008 $):
$748 mil direct
$1,197 mil total
From VHT: $300 – 400 mil/yr.
From VMT: $ 50 – 60 mil/yr.
(Business: $160 – 230 mil/yr.)
GDP (2008 $): $448 mil direct
$718 mil total
Altogether, the preceding discussion and table illustrate the need for significant additional
calculations and assumptions to assess the likely range of economic impacts associated with
wider accessibility benefits, something that could be accomplished more precisely by utilizing
information on vehicle mix, trip characteristics and regional economic patterns in an economic
impact model. However, even without such a model, they do serve here to show that the
accessibility calculation made using the C11 tool are within the realm of believability and
consistency with observed impacts reported by TPICS case studies.
Finally, it should be noted that there are a number of reasons that the economic response to
market access improvements in the study area may be either higher lower than the value
assessed using the C11 tool.
26
 In this case, the long-distance, inter-city and multi-regional context of Corridor Q differs
considerably from the urban areas where market access response elasticities have been
empirically estimated.
 Given the nature of the Appalachian Development Highway System, Corridor Q likely
had multiple overlapping market access effects (e.g. improving freight access,
improving access to intermodal terminals). However, the C11 tools and existing
research limit analysts to assessing one effect at a time (because given aggregate
elasticity estimation methods, there are concerns about double counting).
 Returns to market size are unlikely to be constant. That is: an area that begins with very
low accessibility is likely to have a greater response to a 1% change in accessibility than
is an area that already has very good access. This could lead to a higher productivity
elasticity for areas such as Appalachia that start with lower baseline levels of
accessibility.
Despite these analytical limitations, this case study demonstrates how the C11 market access
tool may be used to assess wider economic effects. The C11 tool results are to be regarded as
high level estimates and demonstrate the importance of market access improvements for
projects such as Corridor Q. Moreover, the case points to future refinements needed in market
access methodologies to better understand factors that may affect the appropriate elasticity
response for a given region, industry, and type of market access.
I-43, Wisconsin: Application of SHRP2 C11 Accessibility Tool.
1.0 Overview
When the Interstate system was created in the 1950s and 1960s, the only two interstate routes
providing access to the state of Wisconsin were I-90 and I-94. Madison and Milwaukee both
benefited from high-quality Interstate access to Chicago and Minneapolis. However, Green Bay,
Wisconsin’s third largest city, lacked a connection to the Interstate system. After a protracted
process of advocacy on the part of the state, the federal government ultimately approved a
connection between Milwaukee and Green Bay that followed US 141.8 The TPICS case study
assesses the economic impacts of constructing the initial section of new interstate I-43, from
Green Bay to Milwaukee, over the period from 1963 to 1981. It passes through the following
Wisconsin counties: Brown, Manitowoc, Sheboygan, Ozaukee, and Milwaukee.
The case study narrative points out the significant role of manufacturing in the study region and
the importance of improved connections to the Chicago market for businesses in the 5-county
area. For example, the narrative reports benefits for Brown County’s paper and beef processing
industries:
I-43 has been identified as being a beneficial link to manufacturing, particularly
the paper industry, in combination with other transportation infrastructure
8
TPICS. Interstate 43. Narrative. Retrieved from: http://tpics.us/CaseSelected.aspx?case_study_id=22
27
projects, in the area. The FHWA study also reported that a major beef processor
had expanded their business in the area because I-43 provided faster access to
the Chicago consumer market.
Because of this focus on businesses and their supply chains, the C11 Effective Density tool was
used with employment data to represent buyer-supplier market access effects. The productivity
analysis was performed for the five Wisconsin counties covered in the TPICS case study, taking
into account improved connections both within the study region, and with the Greater Chicago
Metropolitan area, to the south (Figure 6).
Figure 6: I-43 Study Region: Wisconsin Origin Counties in Blue, Chicago Area Destination
Counties in Yellow, and Points Represent Employment-Weighted Centroids
Source: EDR Group, using ESRI Basemap
28
The following table summarizes the data elements used as inputs to calculate effective density,
along with information about data sources, and any preliminary calculations used to derive
appropriate inputs. The year 2000 was chosen as the year of analysis in order to match the
post-construction study date of the TPICS case study. The analysis was performed using a
county zonal system. The “destination” counties were chosen to approximate the Chicago
metropolitan area to which improved access was provided by I-43.
Data
Element
Activity
Description



GRP Proxy







Impedance
- Build



Data Source
Employment, 2000.
Study Region: Brown, Manitowoc,
Sheboygan, Ozaukee and Milwaukee,
WI.
“Destination” Counties: Kenosha and
Racine, WI; Kendall, DuPage, Lake, Kane,
McHenry, Cook, and Will, IL; Lake, IN.
Total Earnings by Place of Work in 2000
from the BEA.
Total Employment in 2000.
Calculate Earnings by Place of Work per
Employee.
State GDP and Total Earnings by Place of
Work, 2000.
Calculate Ratio of GDP: Earnings.
Apply ratio to Earnings by Place of Work
per number of Employees, to get per
employee GRP proxy.
Adjust result from 2000 to 2014 $, using
BLS CPI.
Origin points are calculated as the
employment-weighted centroid of each
county. Employment data is obtained at
the census tract level, from LEHD.
Inter-zonal: current travel times (using
ArcGIS online to compute from
geocoded centroid points)
Intra-zonal: based on area of county, the
"radius" method (as if the county were a
circle), and a speed of 45mph (counties
Bureau of Economic Analysis Regional Data, Economic profiles
(CA30)
Retrieved from:
http://www.bea.gov/iTable/index
_regional.cfm
BEA. Regional Data: GDP &
Personal Income. CA05 Personal
income by major source and
earnings by SIC industry. Earning
by place of work. Retrieved from:
http://www.bea.gov/iTable/index
_regional.cfm
BLS. CPI Inflation Calculator.
http://www.bls.gov/data/inflation
_calculator.htm
LEHD:
Data: Workplace Area
Characteristics, 2002. Retrieved
from:
http://lehd.ces.census.gov/data/
Documentation available at:
http://lehd.ces.census.gov/data/l
odes/LODES7/LODESTechDoc7.0.p
df
ArcGIS Online:
29
are a mix of urban roads and rural
highways).
Impedance
– Base




Speed change from conversion of US141 to I-43 is estimated based on the
average speed difference between a
principal arterial and an interstate in
Wisconsin (using HPMS data).
Apply % speed change to the section of
each inter-zonal trip that occurs on the
northern (above Milwaukee) section of I43 (the southern section is not included
in the TPICS case study).
Apply 1/4 of the above % speed change
to the intra-zonal speeds, because I-43 is
just one major road in each of the 5
counties.
Result is a uniform 7% reduction in
impedance from based to build for intrazonal travel times, and a reduction for
inter-zonal travel times that depends on
the proportion of the route along the
analyzed section of I-43.
http://www.arcgis.com
Published map of study area
viewable at: http://bit.ly/1up9PqO
Wisconsin’s 2008 HPMS submittal,
accessed using HERS-ST software.
For more information see:
http://www.fhwa.dot.gov/infrastr
ucture/asstmgmt/hersrprep.cfm
Parameters
In addition to the data inputs which characterize the project, the following parameters are
selected for the tool calculations, to appropriately capture the effects of improvements to
buyer-supplier market access.
Parameter
Productivity
elasticity
Decay factor
(α)
Value
 Value of 0.04, for commercial truck trips, which particularly affects
manufacturing industries
http://TPICS.us/tools/documents/Accounting%20Framework-Final.xlsx
 1, based on the decay factor put forward by Graham, et al. for
manufacturing (a more gradual decay than that used for service industries,
due to the longer distance of buyer-supplier relationships).
 Source: Graham, et al. TRANSPORT INVESTMENT AND THE DISTANCE DECAY
OF AGGLOMERATION BENEFITS. 2009. Retrieved from:
http://personal.lse.ac.uk/gibbons/Papers/Agglomeration%20and%20Distan
ce%20Decay%20Jan%202009.pdf
30
Table 28: Activity – Employment, 2000, Base & Build
Zone
Brown, WI
Manitowoc, WI
Sheboygan, WI
Ozaukee, WI
Milwaukee, WI
Racine, WI
Kenosha, WI
Lake, IL
McHenry, IL
Kane, IL
Cook, IL
DuPage, IL
Kendall, IL
Will, IL
Lake, IN
Employment
171,785
45,501
75,850
50,757
622,127
94,452
68,450
415,628
111,081
240,147
3,323,661
697,109
21,505
184,592
242,775
Table 29: GRP Proxy per Employee
Zone
Brown, WI
Manitowoc, WI
Sheboygan, WI
Ozaukee, WI
Milwaukee, WI
GRP Per
Employee
$80,417
$70,515
$74,328
$78,451
$86,832
(Note: GRP is only required for the “origin” counties for which GRP impacts are being
calculated)
31
Table 30: Impedance – Base (Travel Time in Minutes)
County
Brown
Manitowoc
Sheboygan
Brown, WI
18.5
49.9
Ozaukee
Wisconsin
80.8
120.0
Manitowoc, WI
49.9
19.5
36.8
Sheboygan, WI
80.8
36.8
Ozaukee, WI
120.0
Milwaukee, WI
148.6
Milwaukee
Racine
Kenosha
Lake
McHenry
Kane
148.6
171.9
183.9
203.9
230.9
264.9
Cook
Illinois
245.9
DuPage
Kendall
Will
241.9
271.9
262.9
Lake
Indiana
273.9
76.1
105.6
128.0
141.0
160.0
187.6
222.0
203.0
199.0
228.0
220.0
231.0
18.2
49.3
78.8
101.8
113.8
133.8
160.8
194.8
175.8
171.8
201.8
192.8
203.8
76.1
49.3
12.3
35.2
58.5
70.5
90.5
117.2
151.5
132.5
128.5
158.5
149.5
160.5
105.6
78.8
35.2
12.5
31.0
44.0
64.0
85.0
125.0
106.0
102.0
132.0
123.0
134.0
DuPage
Kendall
Will
207.00
237.00
228.00
Lake
Indiana
239.00
Table 31: Impedance – Build (Travel Time in Minutes)
County
Brown
Manitowoc
Brown, WI
17.31
41.00
Ozaukee
Wisconsin
64.00
93.00
Manitowoc, WI
41.00
18.26
30.00
Sheboygan, WI
64.00
30.00
Ozaukee, WI
93.00
114.00
Milwaukee, WI
Sheboygan
Milwaukee
Racine
Kenosha
Lake
McHenry
Kane
114.00
137.00
149.00
169.00
196.00
230.00
Cook
Illinois
211.00
59.00
81.00
103.00
116.00
135.00
163.00
197.00
178.00
174.00
203.00
195.00
206.00
17.01
39.00
61.00
84.00
96.00
116.00
143.00
177.00
158.00
154.00
184.00
175.00
186.00
59.00
39.00
11.48
29.00
52.00
64.00
84.00
111.00
145.00
126.00
122.00
152.00
143.00
154.00
81.00
61.00
29.00
11.69
31.00
44.00
64.00
85.00
125.00
106.00
102.00
132.00
123.00
134.00
Kendall
Will
-15%
-15%
Lake
Indiana
-15%
Table 32: Percent Change in Travel Times from Base to Build (Travel Time in Minutes)
County
Brown
Manitowoc
Sheboygan
Brown, WI
-7%
-22%
-26%
-7%
-23%
-7%
Manitowoc, WI
Sheboygan, WI
Ozaukee, WI
Milwaukee, WI
Ozaukee
Wisconsin
-29%
Milwaukee
Racine
Kenosha
Lake
McHenry
Kane
-30%
-25%
-23%
-21%
-18%
-15%
Cook DuPage
Illinois
-17%
-17%
-29%
-30%
-24%
-22%
-19%
-15%
-13%
-14%
-14%
-12%
-13%
-12%
-26%
-29%
-21%
-19%
-15%
-12%
-10%
-11%
-12%
-10%
-10%
-10%
-7%
-21%
-13%
-10%
-8%
-6%
-4%
-5%
-5%
-4%
-5%
-4%
-7%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Note: The connections between Milwaukee and the destination counties to the south were not improved by the project, because the TPICS case
only deals with the I-43 section north of and terminating in Milwaukee.
32
4.0 Results
Based on the above outlined inputs, the following tables summarize output results from the
tool. The total productivity impact calculated for the five-county study area is $241 Million of
value added in 2014 dollars. Expressed in the same constant dollar year as TPICS, this comes to
$220 Million of value added, in 2008 dollars.
However, it is important to note that the market access tool was calibrated based on data
concerning the effective scale of urban markets and not long-distance (190 mile) intercity
corridors such as I-43. In particular, there is reason to believe that the portion of economic
activity that is actually sensitive to buyer-supplier access improvements—particularly at the
large scale of the I-43 corridor—is significantly smaller than total GRP in the region. Research
indicates that not all sectors are equally responsive to longer-distance buyer-supplier
connections. In fact, the strongest response can be expected in manufacturing supply chains,
with relatively little response in more local service-oriented sectors. If the assessment is
adjusted to only apply to GRP in manufacturing sectors, total estimated impacts (from the same
change in effective density) are then reduced to $71 Million of value added in 2014 dollars ($64
Million in 2008 dollars).
Table 33: Effective density and productivity impact results (expressed in terms of value
added, 2014 dollars)
Alpha = 1
ZONES
Brown, WI
Manitowoc, WI
Sheboygan, WI
Ozaukee, WI
Milwaukee, WI
Total Based on Entire Economy
Total Based on Manufacturing Economy
NO BUILD
2000
EFFECTIVE
DENSITY
38,160
41,747
48,240
68,501
109,819
BUILD
2000
EFFECTIVE
DENSITY
44,475
49,147
55,754
76,027
114,281
306,467
339,684
TOTAL
PRODUCTIVITY($)
$84,881,506
$21,012,246
$32,739,881
$16,637,823
$86,127,560
$241.4 million
$ 71.0 million
5.0 Comparison to Case Study Impact
predicted by the C11 tool is $71 - 241 Million per year, in 2014 dollars. Converted to
2008 dollars to be comparable to TPICS, the estimated accessibility impact value would
be $64 - 220 Million per year.
33
2) Adjustment to Consistent GDP Basis. The direct impact reported by TPICS is $81 Million
per year of business output (which is business sales). Depending on an area’s industry
$49 Million.
3) Recognition of Other Sources of Economic Benefit. Accessibility benefits are wider
effects enabled by speed and distance changes, and thus they are additional to travel
time and operating cost savings benefits. Together, they lead to additional economic
activity, as well as shifted travel movements. However, in this case there is limited
information available on the full nature of those impacts. The TPICS case study links to
an FHWA project page which reports significant growth of traffic after completion of the
I-43 project (traffic increasing 51-138% in Green Bay, 0-57% in the rest of Brown County,
2-17% in Manitowoc County and 27% in Sheboygan County.) However, the actual
magnitude of the time and cost benefits that generated this growth in traffic on I-43 is
not reported.
on both the productivity benefit of accessibility enhancement and also the productivity
from business cost savings (associated with the business and worker-related travel time
and expense changes), as well as factors related to labor cost and business
competitiveness. In this case, though, only the accessibility enhancement has been
calculated as there is insufficient data to assess the business cost savings or other
competitiveness impacts.
reported earlier by the TPICS case study, and reflects the preceding adjustment calculations.
Table 34: Comparison of TPICS Case Study Impacts with Calculations from the Effective
Density (Accessibility Impact) Tool
C11 Effective Density Tool:
Productivity Impact
Accessibility
Impact on
Productivity
Reported by TPICS
Case Study
Value Derived from
TPICS Case Study
71-241 million (2014 $)
64-220 million (2008 $)
Traffic increases
reported
(VMT, VHT change is
unknown)
Output: $81 mil
direct
Other Benefits
Area Economic
Growth Impact
Not Available
GDP: $49 mil direct
Altogether, the preceding discussion and table indicate that there are many factors affecting
productivity and economic impact, and the accessibility tool addresses just one of those factors.
However, in this case it appears that the market access tool alone is predicting an economic
34
impact of a magnitude that is larger than actually reported by TPICS, even without considering
the expected further effects of travel time and cost savings. That suggests the need for
reevaluation of the applied methodology.
There are a number of possible reasons for this outcome. In particular, the TPICS case study
notes that observable ex post impacts were concentrated in the northern section of the
corridor (Brown, Monitowoc, and Sheboygan counties) and that Ozaukee and Milwaukee
counties did not experience new economic development attributable to the I-43 corridor,
because land around the interchanges was already developed. However, the gross productivity
responses predicted by the C11 tool, due to improvements in access, are not based on any
knowledge of the land available for new development in a region. While Ozaukee and
Milwaukee counties may still have experienced some dispersed economic gains, these gains
would not materialize in the form of observable adjacent development, given that land was
unavailable in immediate proximity to the I-43 interchanges.
This suggests one of two potential explanations for: 1) The TPICS case study was unable to
observe economic impacts in Ozaukee and Milwaukee counties because of their dispersed
nature in the region. True total impacts should be higher and more in line with the C11
predictive values; or 2) Economic impacts predicted by the C11 tools for Ozaukee and
Milwaukee counties were not able to materialize, because of constraints on developable land.
True total impacts should be closer to the values observed by the TPICS case study. If one nets
out the predicted impacts for Ozaukee and Milwaukee counties from the C11 tool predictions,
the updated manufacturing industry impacts would be on the order of 65% of the total direct
economic impact found in TPICS. While still high, this adjustment moves the results closer to
the scale expected for market access effects.
Overall, application of the C11 market access tool to I-43 offers a number of insights into
economic valuation: a) Buyer-supplier connections can play a significant role in the overall
economic effects of a project, b) The correspondence between aggregate predictions of
economic impact and the actual impacts identified by ex post case research will be influenced
by non-transportation factors such as the availability of land for development, and c) given that
most existing empirical research on market access was performed in urban contexts, further
research is needed to understand the scale and geographic scope of buyer-supplier market
access effects.
I-476, Blue Route: Application of SHRP2 C11 Accessibility Tool
1.0 Overview
The Blue Route is the name given to Interstate 476 (I-476) in the Philadelphia Region. Between
1964 and 1992, this 21.5 mile interstate was constructed between I-95 in the south and the
Pennsylvania Turnpike (along I-276) in the north. The motivation for the project was to “provide
a bypass of the Philadelphia region and relieve traffic on local roads and state highways west of
35
the city.”9 The southern portion of the corridor, a 15.9 mile section from I-76 to I-95, was
constructed in pieces between 1979 and 1992. It is this portion that was analyzed in the TPICS
case study.
This case study was identified for analysis using the C11 Effective Density Market Access Tool
because the case narrative points to the importance of increased accessibility for commuting
trips: 10
The completion of the southern half of the Blue Route has had profound impacts
on growth and development in the greater Philadelphia metropolitan area
extending as far south as Wilmington, Delaware. The Blue Route opened up the
western and southwestern portions of the region for development, and allowed
for substantial shifts in commuting patterns between Bucks, Montgomery,
Delaware, Chester and New Castle (DE) Counties. Journey to work data from the
US Census for 1990 and 2000 show significant increases in work trips between
these counties over that time period. In some cases where commuting between
counties had been limited prior to the highway construction, commuting
increased by over 100%. For example, the number of work trips by people living in
New Castle County and working in Montgomery County increased by 105%
(+948) and the number living in Montgomery and working in New Castle
increased by 107% (+621). For counties where inter-county commuting had
already been strong, commute trips have also increased. For example, the
number of people commuting from Delaware County to jobs in Bucks County
increased by 3,946 (27%), and the number living in Delaware County and
commuting to Montgomery County increased by 4,210 (18%.) This expanded
labor market undoubtedly resulted in decreased labor costs and increased
productivity, the quantification of which would require a more in-depth analysis.
Because of the focus on commuting, rather than freight access, the C11 tool will be used with
population data to represent access to labor.
TPICS. I-476 Blue Route. Narrative. Retrieved from:
http://tpics.us/CaseSelected.aspx?case_study_id=90
9
10
TPICS. I-476 Blue Route. Narrative.
36
Figure 7: I-476 Blue Route Alignment
Section
Analyzed for
TPICS Case
Source: Delaware Valley Regional Planning Commission. 1994. Traffic Impacts of I-476 (MidCounty Expressway). Retrieved from: http://www.dvrpc.org/reports/94024.pdf
The following table summarizes the data elements used as inputs to the effective density
calculation, along with information about data sources, and any preliminary calculations used
to derive appropriate inputs. The researchers chose to make the year of analysis 1994 in order
to match the year for which information is available about transportation performance
changes. A study published by the Delaware Valley Regional Planning Commission (DVRPC) in
1994 reported time savings achieved by the corridor, relative to alternate routes:11
A travel time survey comparing 1-476 with parallel roads shows that a motorist on I-476 can
travel between I-95 and the Pennsylvania Turnpike in 23 minutes, compared to 49 minutes on
the alternate route (PA 320, Matsonford Road, Fayette Street, Butler Pike, and Germantown
Pike). This comparison shows a 26 minute or 53 percent saving in travel time for each driver
using the entire length of I-476.
Delaware Valley Regional Planning Commission. 1994. Traffic Impacts of I-476 (Mid-County
Expressway). Retrieved from: http://www.dvrpc.org/reports/94024.pdf
11
37
The analysis was performed using a county zonal system.
Data
Element
Activity
GRP Proxy
Description









Impedance Build



Impedance –
Base

Data Source
Population, 1994
Bucks, Montgomery, Delaware,
and Chester, PA; New Castle,
DE
US Census. Population Estimates:
Pennsylvania and Delaware.
Retrieved from:
Total Earnings by Place of Work
in 1994 from the BEA, for the 4
counties
Total Employment in 1994
Calculate Earnings by Place of
Work per Employee
State GDP and Total Earnings by
Place of Work for 1994, for PA
and DE
Calculate Ratio of GDP: Earnings
for PA and DE
Apply ratio to Earnings by Place
of Work per Number of
Employees, to get per
employee GRP proxy
Adjust result to 2014 $, using
BLS CPI
Origin points are population
weighted centroids
Inter-zonal: current travel times
(using ArcGIS online to
compute from geocoded
centroid points)
Intra-zonal: based on area of
county, the "radius" method (as
if the county were a circle), and
a speed of 40 mph, based on
sample of speeds on local roads
1994 DVRPC Study found a ratio
of 2.13 between the old
corridor travel times (using
alternate roads) and the new
ones on the Blue Route.
BEA. Regional Data: GDP & Personal
Income. CA30 Economic Profiles:
Earning by place of work;
Employment.
GDP By State: Real GDP (1997)
dollars, all industry total.
Retrieved from:
38
https://www.census.gov/popest/data/cou
nties/asrh/1990s/CO-99-09.html
http://www.bea.gov/iTable/index_regional
.cfm
BLS. CPI Inflation Calculator.
http://www.bls.gov/data/inflation_calculat
or.htm
ArcGIS Online:
http://www.arcgis.com
Published map of study area
viewable at:
http://bit.ly/1sidEZC
Delaware Valley Regional Planning
Commission. 1994. Traffic Impacts
of I-476 (Mid-County Expressway).
Retrieved from:
http://www.dvrpc.org/reports/94024.pdf



Calculate the base case travel
time for the portion of each OD pair travel time on the I-476
Corridor (southern portion),
using the above ratio.
Delaware County was identified
as the only county for which
intra-zonal travel times would
have been significantly affected
by I-476.
Apply half the above percent
change in travel time to the
intra-zonal travel time for
Delaware County, because I476 will only be used for a
portion of intra-zonal trips.
Parameters
In addition to the data inputs which characterize the project, the following parameters were
selected for the tool calculations, to appropriately capture the effects of improvements to labor
market access.
Parameter
Productivity elasticity
Decay factor (α)
Value
 Value of 0.05, for commuting, which particularly affects
service industries
http://TPICS.us/tools/documents/Accounting%20Framewor
k-Final.xlsx
 1.8 based on the decay factor put forward by Graham, et al.
for consumer/business services (which are most sensitive to
shorter-distance commuting relationships, as opposed to
longer distance freight relationships)
 Source: Graham, et al. TRANSPORT INVESTMENT AND THE
DISTANCE DECAY OF AGGLOMERATION BENEFITS. 2009.
Retrieved from:
http://personal.lse.ac.uk/gibbons/Papers/Agglomeration%2
0and%20Distance%20Decay%20Jan%202009.pdf
39
Table 37: Activity – Population, 1994, Base & Build
Zone
Population
Bucks, PA
567,106
Montgomery, PA
700,227
Delaware, PA
549,174
Chester, PA
396,318
New Castle, DE
464,471
Table 38: GRP Proxy per Employee, 1994, Base & Build
Zone
Bucks, PA
Montgomery, PA
Delaware, PA
Chester, PA
New Castle, DE
GRP per
Employee (2014 $)
$71,749
$86,002
$77,050
$84,425
$110,537
Table 39: Zonal Employment, 1994, Base & Build
Zone
Bucks, PA
Montgomery, PA
Delaware, PA
Chester, PA
New Castle, DE
Employment 1994
276,296
505,200
260,547
223,903
293,232
Table 40: Impedance – Base
(Travel Time,
Minutes)
Bucks, PA
Montgomery, PA
Delaware, PA
Chester, PA
New Castle, DE
Bucks, Montgomery Delaware,
Chester,
New Castle,
PA
, PA
PA
PA
DE
20.8
44.0
70.6
65.0
96.1
44.0
18.6
44.6
37.0
71.2
70.6
44.6
18.0
39.0
32.4
65.0
37.0
39.0
23.2
42.0
96.1
71.2
32.4
42.0
17.5
40
Table 41: Impedance – Build
(Travel Time,
Minutes)
Bucks, PA
Montgomery, PA
Delaware, PA
Chester, PA
New Castle, DE
Bucks, Montgomery, Delaware,
Chester,
New Castle,
PA
PA
PA
PA
DE
20.8
44.0
57.0
65.0
78.0
44.0
18.6
31.0
37.0
52.0
57.0
31.0
11.5
39.0
29.0
65.0
37.0
39.0
23.2
42.0
78.0
52.0
29.0
42.0
17.5
4.0 Results
Based on the above outlined inputs, Table 42 summarizes output results from the tool. The
total productivity impact calculated for the five-county study area is $1.121 Billion of value
added. Given the large scale of the estimated impact, it also makes sense to subject the results
to a sensitivity analysis with respect to the regional economic activity (the GDP) that is expected
to respond to labor market access improvements. Research indicates that service-providing
industries respond strongly to labor market access, while goods-producing industries are more
sensitive to buyer-supplier connections. The Blue Route primarily enabled improved labor
market access within the outer areas of the Philadelphia metropolitan region. For that reason,
economic impacts (based on the same change in effective density) might be more appropriately
assessed for only the portion of GDP associated with service-providing industries in the study
area. According to the Bureau of Economic Analysis, approximately 60% of the region’s
economic activity is associated with the service sectors, indicating that the productivity impact
would be roughly $673 million if the benefit was actually focused on worker and customer
access to service industries in the region.
Table 42: Effective density and productivity impact results (expressed in terms of value
added, 2014$) Comparison to Case Study Impact
ZONES
Bucks, PA
Montgomery, PA
Delaware, PA
Chester, PA
New Castle, DE
TOTAL based on
entire economy
TOTAL Based on
service sector
NO BUILD
1994
EFFECTIVE
DENSITY
3774
5658
5484
4052
4696
BUILD
1994
EFFECTIVE
DENSITY
3952
6367
10260
4052
5245
% Change
in ED
5%
13%
87%
0%
12%
23664
29876
12%
41
Per Worker
GRP
$71,749
$86,002
$77,050
$84,425
$110,537
Zonal
EMP
276,296
505,200
260,547
223,903
293,232
Productivity
Impact
$45,733,539
$257,230,112
$638,720,126
$0
$179,681,816
$1,121 million
$673 million
predicted by the C11 tool is $0.673– 1.121 Billion per year in 2014 dollars. Converted to
2008 dollars to be comparable to TPICS, the estimated accessibility impact value would
be $0.611 – 1.017 Billion per year.
2) Adjustment to Consistent GDP Basis. The impact reported by TPICS is $2.864 Billion per
year of business output (which is total business sales). Depending on an area’s industry
$1.718 Billion.
3) Recognition of Other Sources of Economic Benefit. Accessibility benefits are additional
to travel time and operating cost savings benefits, which are typically the largest factors
driving economic benefits. The TPICS case study narrative for the I-476 (Blue Route)
reported that significant savings in travel time and congestion reduction, with
congestion relief leading to reductions in travel on parallel routes by as much as 50%. If
more information were available regarding changes in volumes and speed
improvements, then it would be possible to assess overall VMT and VHT reductions.
Travel Time and Traffic Network Diversion Impacts
As noted in a report by the Delaware Valley Regional Planning Commission:*
In summary, the impact of the long-planned, much-delayed 1-476 has been very positive from the
standpoint of traffic flow. Separation of local travel from through traffic, a problem that had
plagued mid-Delaware County for years, has been accomplished, with the result that the majority
of the previously congested streets and roads have now been freed for local drivers. From a
regional perspective, this "parkway-like" expressway has been beneficial in saving travel time,
improving mobility, and offering additional options to the region's motorists.
…. Traffic on routes parallel to 1-476 have seen reductions by as much as 50 percent on certain
segments. To a large extent this is caused by shifting traffic to the new expressway, thus relieving
congestion on local roads. Not only has 1-476 provided a new link for daily commuters, it also has
ensured that through traffic does not interfere with local traffic, Routes such as PA 420, PA 452, PA
252, and PA 320 have all experienced sharp declines in traffic, ranging from 12 percent (PA 420) to
36 percent (PA 320) since the opening of 1-476.
*Source: Delaware Valley Regional Planning Commission. 1994. Traffic Impacts of I-476
(Mid-County Expressway). Retrieved from: http://www.dvrpc.org/reports/94024.pdf
on both the productivity benefit of accessibility enhancement and the productivity from
business cost savings (associated with the business and worker related travel time and
expense changes), as well as factors related to labor cost and business competitiveness
42
(which are not known here). Unfortunately, in this case, there is insufficient information
to estimate the business worker travel time and business operating cost savings. We
can note that the accessibility gain alone (shown in preceding item 1) has a value that is
36 - 59% of the total estimated economic impact reported by TPICS (shown in preceding
item 2).
5) This is higher than the range typically found in economic impact studies. One possible
reason is the existence of additional dispersed impacts in the Philadelphia region not
identified in the TPICS case study. The TPICS methodology is best suited to identifying
localized land use impacts around a highway. However, in the case of the Blue Route,
economic effects accrued over a long period of time and were therefore a challenge to
isolate from other regional trends.
reported earlier by the TPICS case study, and shows how the preceding four adjustment
calculations can be used.
Table 43: Comparison of TPICS Case Study Impacts with Calculations from the Effective
Density (Accessibility Impact) Tool
Value Derived
Reported by TPICS Case
from TPICS
Study
Case Study
C11 Effective Density Tool:
Productivity Impact
Accessibility
Impact on
Productivity
$0.673– 1.121 bil/yr.(2014 $)
$0.611 – 1-017 bil/yr. (2008 $)
Time Savings & Traffic
shifts reported (VMT,
Not Available
VHT change is unknown)
GDP: $1.718 bil
Output: $2.864 bil direct
direct
Other Social
Benefits
Area Economic
Growth Impact
The preceding discussion and table illustrate the need for significant additional calculations and
assumptions to assess the likely range of economic impacts associated with wider accessibility
benefits, something that could be accomplished more precisely by utilizing information on
vehicle mix and trip characteristics in an economic impact model. However, even without such
a model, they do serve here to show that the accessibility calculations made using the C11 tool
are within the realm of believability and consistency with observed impacts reported by TPICS
case studies.
Overall, application of the C11 market access tool to the I-476 Blue Route demonstrates a) the
important of labor market access effects above and beyond standard travel time savings, and b)
the need to refine economic impact analyses with a sector-specific understanding of industry
dependence on different types of market access—in this case, the particular importance of
labor market access to service-producing industries.
43
Bayport Terminal, TX: Application of SHRP2 C11 Connectivity Tool
1.0 Overview
The Port of Houston is a 25-mile long maritime-industrial complex on the Houston Shipping
Channel a few hours sailing time from the Gulf of Mexico. The new Bayport Terminal was built
to service post-Panamax vessels and to relieve congestion at the existing Barbours Cut
Terminal, which was operating at 120% of capacity, resulting in delays of up to 24 hours.
Phase 1 of the new Bayport Terminal opened in 2007 on 56 acres with two container berths
and three cruise ship berths, at a cost of $800 million. Due to the increase in container volume
from 2006 to 2008 (an additional 789,000 tons of container cargo) and warehouse
development in the local area, an estimated 719 jobs were created. When recovery from the
current economic recession gains momentum, plans for Phase II of the facility are expected to
proceed. By 2030, Bayport Terminal is expected to span 526 acres, have seven berths, and
include an intermodal rail-to-truck facility.
The C11 intermodal connectivity tool was initially designed to assess the relative connectivity of
different types of intermodal facilities based on their overall level of activity, the value of goods
moved, their degree of connectivity to origins and destinations, and ease of access to the
facility. For each facility, the tool has a pre-loaded connectivity raw score which is the product
of the volume moved, the value per ton for cargo, and the number of unique origins and
destinations reachable from the terminal. A change in any of these factors will result in a
percent change in the overall weighted connectivity score of the terminal, which is then
associated with an economic impact, via an elasticity measure. In this case, the tool was applied
to assess the value of the additional containerized cargo (a measure of the scale of activity at
the port) that was supported by the new Bayport Terminal. The ability to handle more capacity
between the combined Bayport and Barbours Cut terminals was the major impetus for the
project, so the additional volume was used to identify those impacts.
Figure 8: Bayport Container Terminal, Port of Houston, Harris County, TX
Source: Google Maps
44
The focus of this application of the intermodal connectivity tool was to identify the economic
contribution of additional containerized cargo at the Port of Houston. Because this type of
analysis was not originally anticipated in the design of the tool, it required some adjustments to
figures in the Results page and manual calculations of the connectivity raw score.
To do so required a quantification of the additional capacity provided by the new terminal, as
well as a conversion from tons to containers (the unit of measure for the container connectivity
index is the container). The Port of Houston provided information on the total number of tons
that the new port processed in 2008, and the Texas DOT provided information on how many
tons per container are typical for the Port of Houston. The number of trucks is an estimate
based on information in the C11 tool of total volume at the Port of Houston, and a conversion
of volume to trucks.
Data Element
# of Trucks
Additional
capacity
Tons per
container
Description
Based off of port of Houston statistics.
Assumed 20 tons of bulk cargo per
truck, and 1 container per truck.
The change in capacity of the new
terminal, which is an indication of the
additional containers that can be
processed by the new facility.
The number of tons per shipping
container
Data Source
Estimated based on C11 tool data
and unit conversions
Port of Houston
Texas Dept. of Transportation
http://ftp.dot.state.tx.us/pub/txdotinfo/panama/research/variables.pdf
The Facility inputs required by the C11 intermodal connectivity tool are presented in Table 40
below. The Port of Houston is a marine port in the state of Texas. The tool identifies the port as
“Houston, TX,” located in Harris County. The unit lift capacity is left blank as it only applies to
rail freight intermodal facilities.
Table 45: Intermodal Facility Inputs
Input
1a. State
1b. Facility Type
1c. Facility Name
County
1d. Unit Lift Capacity
Value
TX
Marine
Houston, TX
Harris
In this unique project, the key inputs are actually entered on the Results page. Therefore,
“dummy figures” were entered in the improvement inputs tab. None of these figures will
45
change between the base and build case and therefore they do not affect the ultimate
calculation of economic impacts. The distance to the facility does not change, and a value of 10
is entered as a placeholder. The number of trucks was estimated as 12.7 million trucks per
year, based on Port of Houston statistics and calculations discussed above. The travel time is
another dummy figure of 1.0, as is the fraction of trucks associated with the intermodal center.
Other adjustments are made directly in the Results section, as discussed in the following
section.
Table 46: Improvement Inputs
Connectivity Input
1b. Distance of Improvement from Facility (miles)
1c. Number of trucks within study area
1d. Travel time (hrs) per truck
Default value per truck hour saved
Fraction of trucks assoc. with intermodal center
Facility #1
10
12,698,595
1.0
$57
1.0
Facility #2
10
12,698,595
1.0
$57
1.0
4.0 Results
After entering in the necessary inputs, the C11 intermodal connectivity tool provides the results
presented in the first column of Table 47. The first rows present the baseline characteristics of
the intermodal facility as stored in the tool: The total number of containers is 1,341,897
containers per year, the average value of each container is $32,694 and the number of unique
origins and destinations that it is connected to are 84. The Connectivity Raw Value combining
all of these categories is 36.9.
For the Project scenario, a manual override was used to capture the change in activity levels
resulting from the new Bayport Terminal. The Port of Houston indicated that the new facility
allowed for 789,000 tons of additional freight handled in 2008. This translates into 84,111
additional containers (at a rate of 9.38 tons per container). Thus, the values for the project
case differ from those in the base case: the total number of containers in the Project scenario is
1,426,008 containers per year (using a manual override), the average value per container is the
same $32,694 per containers, with the same 84 unique origins and destinations. The Project
Connectivity Raw Value combining all of these categories is 39.2. The higher value reflect an
increase in connectivity associated with additional freight activity.
The Relative Value and Relative Origins and Destinations scores indicate how these categories
compare to the highest values of other intermodal facilities in the United States, and this does
not change in the base and project. The Relative Value of 7.1% means that on a scale from 0%
to 100% in terms of all container value across all marine facilities, the Port of Houston facility is
on the lower scale of value. With respect to Relative Origin and Destination connections, the
Port of Houston facility is near the top 2/3 of marine facilities across the country.
The Relative Activity of 24.5% in the base case indicates an activity in the lower quartile of
marine facilities. Note that the increase in activity in the build case would change this relative
46
activity score slightly, but the tool is not set up to recalculate the value based on a manual
override.
The number of trucks and truck hours associated with the facility do not change. The
combination of activity, value, origins/destinations, # of trucks, and value of time are all used
within the tool to calculate a weighted connectivity score, listed the last row of the table below.
Table 47: Index Variables
Measurement Category
Activity
Value
Unique Origins/Destinations
Facility Connectivity Raw Value
Relative Activity
Relative Value
Relative Origins and Destinations
# of trucks associated with facility
Truck hours - facility
Value of time - facility
Weighted connectivity
Base
1,341,897
$32,694
84
36.9
24.5%
7.1%
67.7%
12,698,559
12,698,559
$722,899,666
26,640,980,896
Project
1,426,008
$32,694
84
39.2
(Approx. 24.5%)
7.1%
67.7%
12,698,559
12,698,559
$722,899,666
28,310,761,539
Units
Containers
Per Container
Orgins/Dest.
Index
Index
Index
Index
Trucks
Hrs
$ Value
Index
The weighted connectivity score includes multiple variables. In this case, the change in port
activity drives the change in the weighted connectivity score. In order to calculate a resulting
productivity impact, the percent change (6.3%) between the Base and Project case is calculated
as shown in Table 48.
An elasticity value of .015 (from the SHRP C11 Accounting Framework Tool) is then multiplied
times this % improvement and also by the Harris County Gross Domestic Product (GDP) to
estimate the total productivity benefit. According to this initial estimate, the additional capacity
at Port of Houston provides a potential productivity impact (representing GDP change) of
$257.4 Million per year.
However, a more refined estimate is appropriate because this case focuses specifically on
container capacity for overseas shipments at a seaport as opposed to the broader passenger
and freight flow impacts that can occur at other intermodal (airport, marine port or rail)
terminals. In this case, the portion of the economy that can reasonably be expected to benefit
from improved connectivity at the Port of Houston is highly focused on goods-producing
industries that generate outbound products and utilize inbound parts. Therefore, an
adjustment is applied in order to only estimate an economic response for those industries.
Regional economic data indicates that 35.6% of the GRP in Harris County is associated with
good-producing industries, reducing the potential productivity impact to $91.6 Million per year.
47
Table 48: Productivity Benefits
Weighted Connectivity
Base
Project
% Improvement
Elasticity
% Change GRP
County GRP (2008)
Productivity Benefit (GRP)
Based on total economy
Based on goods-producing sectors
26,640,980,896
35,529,892,910
6.3%
0.015
0.095%
$272,420,800,000
$257.4 million
$ 91.6 million
Table 50 compares the connectivity benefit from Table 48 with the macro economic impacts
reported by the TPICS case study. The TPICS case study impacts are reported in terms of
business output (which is total business sales). Depending on an area’s industry mix, GDP is
typically in the range of 1/2 to 3/4 the magnitude of output. Converting the reported output
change from TPICS into GDP terms for comparability leads to a direct impact of around $112.5
Million. This is on the order of 35% larger than the calculation of productivity improvement
associated just with enhanced intermodal connectivity.
Table 49: Comparison of TPICS Case Study Impacts with Calculations from the
Intermodal Connectivity Tool
C11 Connectivity Tool:
Productivity Impact
Reported by
TPICS Case Study
Value Derived from
TPICS Case Study
Connectivity
Impact on
Productivity
$91.6 million/yr.(2014 $)
$83.4 million/yr. (2008 $)
--
--
Other Benefits
--
Area Economic
Growth Impact
--
Congestion reduction,
operating cost savings
Output: $187.6 million
direct
Not Available
GDP: $112.5 million
direct
This finding is not surprising since the project not only increased intermodal connectivity, but
also provided much needed additional capacity to serve an increasing volume of overseas
cargo. This project enabled more imports and exports to be processed in Texas, while saving
some shippers the added distance that their cargo would have had to travel to get to a more
distant port.
To accurately calculate the total expected economic impact, it would be necessary to also
consider factors such as business operating cost savings (associated with the business and
worker related travel time and expense changes), as well as factors related to labor cost and
business competitiveness (which are not known here). In addition, calculation of the full
48
business productivity gain requires information on vehicle and trip purpose mix as well as
regional economic mix. Nevertheless, this application of the C11 connectivity tool highlights the
importance of intermodal connectivity effects that differ from traditionally measured travel
time and distance savings.
SH-170 Road and Alliance Logistics Park: Application of SHRP2 C11 Connectivity
Tool
1.0 Overview
The Alliance Global Logistics Hub in Fort Worth, TX, completed in 1994 at a cost of $115 Million
(in 1994 dollars), is one of the most successful multi-modal logistics parks in the United States,
combining rail, trucking, and air freight facilities in close proximity. The Logistics Hub is part of
the 17,000 acre Alliance mixed-use, master planned development in the far northwest suburbs
of the Dallas-Fort Worth area. The construction of SH-170, at a cost of $6.8 Million, not only
improved access for the high volume of commercial traffic to the Logistics Hub, it also provided
development sites that attracted related shipping, logistics, communications, and technology
operations to the area. Between 1992 and 2000, private investors developed eight million
square feet of commercial space, bringing 8,500 new jobs to the area. The Alliance Global
Logistics Hub has sparked the growth of a new sub-region of the northwest DFW Metroplex.
The focus of this application of the C11 Connectivity tool is on the economic contribution of the
new access road, SH-170, which was built to provide access between adjacent building sites,
the airport, and the rail intermodal center. It was designed to operate without traffic lights,
allowing a faster and more efficient flow of trucks. The base and build inputs for the
Connectivity tool came from a variety of sources as shown in Table 50. The Unit Lift Capacity,
the number of containers that can be transferred from one mode to another at the rail
intermodal center, was identified as 600,000 lifts. This information was found through a webbased search of facility characteristics. Distance to access the Alliance Logistics Park with and
without SH-170 was estimated using Google Maps. Truck counts on SH-170 were gathered
from the Texas Department of Transportation and average truck speeds collected from the
Federal Highway Administration (FHWA).
49
Data Element
Description
Data Source
Unit Lift Capacity
Container capacity of
intermodal center
Alliance Global Logistics Hub.
Miles from facility to
improvement
Number of Trucks on
SH-170
Truck miles per hour
Google Maps
Distance of
Improvement
# of Trucks
Truck Speed
http://www.alliancetexas.com/Portals/0/PDF/Allianc
e_Global_Logistics_Hub_Brochure.pdf
U.S. DOT FHWA. Freight Facts and Figure 2012:
Dallas, TX.
http://ops.fhwa.dot.gov/freight/freight_analysis/nat_
freight_stats/docs/12factsfigures/table3_15.htm
The Facility inputs required by the C11 Connectivity tool are presented in Table 51 below.
Located northwest of Fort Worth, TX, the Alliance Logistics Park includes both air freight and
rail intermodal terminals. The tool was used to assess the productivity benefits of improving
access to the intermodal center by building SH-170. The Connectivity tool identifies the rail
intermodal center in the Alliance Logistics Hub as the “Santa Fe Railway Intermodal Facility
(DFW)” although the name has since been changed to BNSF (Burlington Northern Santa Fe).
Because the majority of the BNSF intermodal facility is located in Denton County, the tool
automatically displays Denton as the county where the facility is located. However, SH-170 is
primarily located in Tarrant County. The county in which the facility is located does not affect
the tool results and is for informative purposes only. The unit lift capacity (i.e., the number of
containers) previously identified, is entered into the tool. Unit Lift Capacity information must
be provided by the user since this information is not included in the tool.
Table 51: Intermodal Facility Inputs
Input
1a. State
1b. Facility Type
1c. Facility Name
County
Value
TX
Rail Freight
Santa Fe Railway Intermodal Facility (DFW)
Denton
600,000
The additional Connectivity inputs required by the tool are presented in Table 52. SH-170
shortened the travel distance to the intermodal center (by 3.3 miles) and therefore also the
travel time (by 4 minutes). According to TxDOT, nearly 6,000 trucks travel along SH-170 each
year (the earliest available count was in 2007). The Connectivity tool default value of $57 per
truck hour was used to estimate the value of time saved which represents the combined crew
50
cost and freight logistics12 cost per hour. All of the trucks on SH-170 were assumed to be
associated with the intermodal center because SH-170 directly serves adjacent warehouses,
distribution centers, logistics facilities, and other manufacturing plants located along the
highway.
Connectivity Input
1b. Distance of Improvement from Facility
(miles)
1c. Number of trucks within study area
Estimated Speed (mph)
1d. Travel time (hrs) per truck
Default value per truck hour saved
Fraction of trucks assoc. with intermodal center
Facility
Facility #2
#1 (Base) (Build)
12.9
9.6
5,853
55
0.23
$57
1.0
5,853
55
0.17
$57
1.0
4.0 Results
After entering in the necessary inputs, the C11 Connectivity tool presents the various
components used in calculating a connectivity index, as shown in Table 53. The first three rows
contain the basic characteristics of the intermodal facility: 1) The unit lift capacity (600,000 lifts
per year), 2) the average value of each container ($32,694 per container) and 3) the number of
unique origins and destinations to which the facility has connections (122). Next, the
Connectivity Raw Value of 24 represents a combination of all of these characteristics into one
index value. Up to this point, all of the facility characteristics are the same for both the base
and project scenarios
The next three rows include the Relative index score indicating how the basic characteristics of
this facility rank in measure across the spectrum of other intermodal facilities throughout the
United States. The Relative Value of 7.1% means that on a scale from 0% to 100%, with 100%
representing the highest value container value in the U.S., the container value at the BSNF
facility only represents 7% of that value compared to other rail intermodal facilities. The
Relative Origin and Destinations percentage indicates that the BSNF facility is near the midpoint
(51%) compared to other facilities nationwide. The Relative Activity is not listed because the
tool does not contain a list of lift capacities for all intermodal centers across the county which is
necessary to enable a comparison.
The next row shows that the number of trucks associated with the facility do not change
between the base and project case. However, because of the direct route SH-170 provides, the
number of truck hours and associated time costs decrease in the project scenario. The
combination of facility characteristics (activity, value, origins/destinations), number of trucks,
Freight Logistics Costs represents the business opportunity cost of freight delay, including shipper
inventory, dock handling & consignee schedule disruption
12
51
and value of time are all used within the tool to calculate a weighted connectivity score, listed
in the last row of Table 53.
Because all of the facility characteristic measures remain the same, the decrease in the
weighted connectivity index is driven by the decrease in the truck hours and overall value of
time.
Activity
Value
Relative Activity
Relative Value
Relative Origins and Destinations
# of trucks associated with facility
Truck hours - facility
Weighted connectivity index
Base
Project
Units
600,000
600,000
Containers
$32,694
$32,694
Per Container
122
122 Origins/Destinations
24
24
Index
Index
7.1%
7.1%
Index
51.5%
51.5%
Index
5,853
5,853
Trucks
1,373
1,022
Hrs
$78,153
$58,160
$ Value
1,870,374 1,391,906
Index
The percent change in the weighted connectivity index (26%) is used to calculate productivity
gains, as shown in Table 54. This percent change is multiplied times the elasticity value of .005
(from the SHRP C11 Accounting Framework Tool) to estimate the % change in Gross Regional
Product (GRP).
Since GRP is only available at the scale of a Metropolitan Statistical Area (MSA), GRP was
estimated using information on local personal income. First, a GRP to personal income ratio of
1.38 was calculated by dividing the GRP by total personal income for the Dallas/Ft. Worth MSA
as shown in Table 55.
The post-construction analysis year, the same year used for estimating economic impacts, was
2000. Because GRP data for the Dallas/Ft. Worth MSA is not available for that year, 2001 was
chosen as the nearest available year.
Table 54: GRP to Wage Ratio Calculation
Year
2001
Dallas/Ft. Worth
Dallas/Ft. Worth Total
GRP/Wage Ratio
GRP ($M's)
Personal Income ($M's)
$255,913
$186,022
1.38
Since the intermodal center and businesses that use SH-170 to receive materials and ship their
finished goods are located in both Denton and Tarrant counties, total personal income for both
counties was then multiplied times the GRP/Personal Income ratio to estimate a GRP
equivalent of $86.9 Billion for the two counties (Table 55).
52
Table 55: Wage to GRP Equivalent Conversion
County
Denton
Tarrant
Total
Total Personal
GRP/Pers.
GRP
Income ($M's)
Income Ratio
Equivalent
$15,475
1.38
$21,289
$47,707
1.38
$65,631
$63,182
$86,920
Further refining the portion of GRP affected by this project, Transportation & Warehousing and
Manufacturing sectors were identified as the primary industries that use SH-170 to access the
rail intermodal center. These two industries represent an estimated 25% of all Personal Income
within Denton and Tarrant counties. Applying this estimate for the GRP affected, the C11
Connectivity Tool estimates an increase in regional GRP in the amount of $27.6 Million per year
due to improved rail terminal access (Table 56).
Base
1,870,374
Project
1,391,906
% Improvement
26%
Elasticity
0.005
% Change GRP
0.128%
County (ies) GRP
$86,919,794,000
% of Industry impact
25%
GRP Affected
$21,593,687,507
Productivity Benefit
$27,620,000
The final part of this analysis seeks to compare the ex post results found in the TPICS case
studies, which are based on actual observations, and the predicted impacts obtained from the
C11 tool. These tools were designed to assess wider economic effects such as connectivity
impacts that are not captured by traditional travel time and cost savings. This means that they
should not be expected to also capture the economic impact of standard travel benefits such as
savings in average travel time or distance which also accrue to travelers as a result of improved
transportation system performance. Therefore, connectivity impacts should be viewed as only
part of the story, relative to total economic impacts of the Logistics Park project.
reported by the TPICS case study. The productivity impacts was converted from 2014 dollars to
2008 dollars to enable a direct comparison. In addition, The TPICS case study impacts are
reported in terms of business output (which is total business sales). Depending on an area’s
industry mix, GDP is typically in the range of 1/2 to 3/4 the magnitude of output. For
comparability the reported output change from TPICS is converted into GDP, leading to a direct
impact of around $830 million. Table 57 shows that the actual economic impact of SH-170,
enabling growth of Alliance Business Park, was far greater than the productivity improvement
53
associated with enhanced intermodal connectivity. This is not surprising since the highway
project also played a key role in enhancing the balance of rail, truck and aviation related
activities that could occur at the adjacent logistics park.
Table 57: Comparison of TPICS Case Study Impacts with Calculations from the
Intermodal Connectivity Tool
Productivity Impact
Reported by
TPICS Case Study
Value Derived from
TPICS Case Study
Connectivity
Impact on
Productivity
$27.6 million/yr.(2014 $)
$25.1 million/yr. (2008 $)
Other Benefits
--
Access to undeveloped land,
congestion reduction, travel
distance and cost savings
Not Available
Area Economic
Growth Impact
--
GDP: $830
million direct
--
--
To understand the reason for this difference, it is important to recognize how SH-170 serves a
crucial role by linking large developable sites with the intermodal facilities and I-35W. In
addition to providing access, the construction of SH-170 prevented overdevelopment of the
land next to the airport and rail intermodal site, thereby reducing congestion and allowing for
more balanced pattern of development at Alliance in the long-term. The six-lane road capacity
on SH-170 is much greater than the two-lane roadways closer to the BNSF Intermodal Facility
and can better accommodate a concentrated grouping of intensive truck users.
The construction of SH-170 was only one small part of the overall Alliance Logistics Park freight
project and therefore only accounts for a portion of the total economic impacts of the entire
facility which includes the rail intermodal center, the freight airport, and nearby large-scale new
commercial developments.
U.S. 281, McAllister Freeway, San Antonio, TX: Application of SHRP2 C11
Connectivity Tool
1.0 Overview
The McAllister Freeway in San Antonio is an 8 mile-long freeway built between 1969 and 1978.
It is part of U.S. 281, which runs from the Mexican border to the Canadian border. The
McAllister Freeway is a new alignment that replaced surface streets connecting downtown San
Antonio at I-35 to a point just north of I-410 near the San Antonio International Airport. The
highway passes through sensitive environmental areas, limiting its development impact in large
sections of the corridor. Much of the economic impact has been to support airport related
economic activity including the tourism trade in downtown. The project also provides capacity
supporting constant residential growth that has occurred north of the city over the past three
decades. It is estimated that the highway has directly and indirectly helped attract 10,000 jobs
to the San Antonio region.
54
Figure 9: US-281, San Antonio, TX
Source: Google Maps
The focus of this application of the connectivity tool was on the economic contribution of the
new U.S. 281 highway, and in particular the highway’s improvement of passenger access to the
airport. The highway was built primarily for the purpose of enhancing airport access, enhancing
access to downtown, and providing a viable highway alternative through the core of the city.
The most viable north-south surface road alternative to U.S. 281 is San Pedro Avenue, a
signalized street that becomes two lanes in many segments, has various school-zones, and has
posted speed limits from 25 mph to 35 mph.
The base and build inputs for the Connectivity tool came from a variety of sources as shown in
Table 58. The overall facility distance is 8.0 miles, as reported by the Texas DOT and verified in
Google Maps. The overall number of vehicles using U.S. 281 is reported to be 147,000 AADT by
Texas DOT, equivalent to 44,100,000 vehicles per year (at an annualization rate of 300). Finally,
posted speed limits are used to identify travel times: 65 miles per hour is used for U.S. 281, and
25 miles per hour is used for the alternative route, San Pedro Avenue.
55
Data Element
Distance of
Improvement
# of passenger
vehicles
Travel Speed
Description
Miles from facility to
improvement
number of vehicles on U.S. 281
Data Source
Google Maps
Miles per hour
Posted speed limits, Google
Maps
The Facility inputs required by the C11 Connectivity tool are presented in Table 59 below.
Because the U.S. 281 facility is a primary road used to access San Antonio International Airport,
the Passenger Airport Facility Type is used. The tool identifies the airport as “San Antonio
International Airport,” and it is located in Bexar County, Texas. Unit Lift Capacity is left blank
because it is not pertinent to a passenger airport.
Table 59: Airport Facility Inputs
Input
1a. State
1b. Facility Type
1c. Facility Name
County
Value
TX
Passenger Airport
San Antonio International Airport
Bexar
The various Connectivity Inputs required by the tool are presented in Table 60 below. The new
U.S. 281 highway provides a similarly distanced alternative to the existing north-south roads in
San Antonio, but provides highway speeds. The overall distance of the facility is 8.0 miles, which
is assumed for the previously existing alternatives. The posted speed limits on U.S. 281 are 65
mph, compared to posted speed limits as low as 25 mph on San Pedro Avenue (and other
alternative north-south roads). Texas DOT reports an AADT of 147,000 vehicles per day in 2007,
translating to 41,100,000 vehicles per year. The Connectivity tool default value of $18 per
passenger hour was used to estimate the value of time saved. All of the passenger trips along
U.S. 281 were assumed to be associated with the airport for purposes of this analysis. In this
case, the only variable of interest is the percent change in the weighted connectivity score,
which is a function of the percent change in travel time to the airport. Therefore, as long as
volumes are held constant in the base and build cases, the actual number of passenger vehicles
using the highway to access the airport is immaterial to the end-results of the economic impact
calculation. Another adjustment will be made later on to account for the fact that U.S. 281 may
not be used by all airport patrons in the study region.
56
Connectivity Input
1b. Distance of Improvement from Facility (miles)
1c. Number of passenger vehicles within study area
Estimated Speed (mph)
1d. Travel time (hrs) per passenger vehicle
Default value per passenger hour saved
Fraction of passenger cars assoc. with intermodal center
Facility #1 Facility #2
8.0
8.0
44,100,000 44,100,000
25
65
0.32
0.12
$18
$18
1.0
1.0
4.0 Results
After entering in the necessary inputs, the C11 Connectivity tool provides the results in Table
61. The first rows present the characteristics of the airport: the airport serves 8.2 million
passengers, and it has 157 unique origins and destinations connected to it. All of these
characteristics remain the same in the Base and Project scenarios.
The Relative Value and Relative Origins and Destination percentages indicate how these
categories compare to the highest values of other intermodal facilities in the United States.
With respect to Relative Origin and Destinations connections, San Antonio International Airport
is near the midpoint, or considered average, relative to other facilities across the country
(43.3%). The Relative Activity is 9.5%, making the activity level of San Antonio Airport on the
lower end of US airport.
The number of annual passenger vehicles associated with the facility does not change between
the base and project cases. However the number of passenger hours changed due to U.S. 281
providing a more direct route with an associated value of time savings. The combination of
activity, value, origins/destinations, number of passenger vehicles, and value of time are all
used within the tool to calculate a weighted connectivity score, listed in the last row of the
table below.
57
Activity
Value
Relative Activity
Relative Value
Relative Origins and
Destinations
# of passenger vehicles
associated with facility
Passenger hours - facility
Weighted connectivity
Base
Project
Units
8,238,824
8,238,824
Passengers
157
12.9
9.5%
43.3%
44,100,000
157 Origins/Dest.
12.9
Index
9.5%
Index
Index
43.3%
Index
44,100,000
14,112,000
5,427,692
$252,285,767
$97,032,987
3,263,304,710 1,255,117,196
Passenger
vehicles
Hrs
$ Value
Index
The weighted connectivity score includes multiple variables. In this case, the savings in
passenger hours is what drives a change in overall weighted connectivity score. In order to
calculate a resulting productivity impact, the relative percent change between the Base and
Project case is calculated as shown in Table 62. An elasticity value of .015 (from the SHRP C11
Accounting Framework Tool) is then multiplied times this percent improvement and also by the
county Gross Domestic Product (GDP) to estimate the total Productivity Benefit. According to
this initial estimate, the additional access of U.S. 281 yields a productivity impact estimate of
$700.9 Million per year. However, this estimate assumes that all sectors of the economy and all
parts of the metropolitan area benefit from the U.S. 281 project.
There are a number of adjustments that can be made to refine the intermodal connectivity
benefit, by identifying, more specifically, the share of economic activity within Bexar County
that is actually sensitive to increased airport access. First, while the time savings for a trip to the
airport are significant for those who actually use U.S. 281, not all travelers within Bexar County
will actually use the highway to get to the airport. It is estimated that only one third of the
region actually benefits from the U.S. 281 improved airport access. In general, service-providing
industries tend to be more reliant on passenger air access than are goods-producing industries.
These industries depend on passenger air access for business travel and for its role in
supporting tourism. 83.9% of the GRP in Bexar County is associated with service-providing
industries. With these two adjustments combined, the predicted impact on productivity is
reduced to 28% of the magnitude initially calculated, or approximately $196 Million in
additional county GRP per year.
58
Base
Project
% Improvement
Elasticity
% Change GDP
County GRP
Productivity Benefit
Based on full economy
Based on service industries along corridor
3,263,304,710
1,255,117,196
61.54%
0.015
0.923%
$75,929,000,000
$700.9 million/year
$196.2 million/year
reported by the TPICS case study. The TPICS case study impacts are reported in terms of
business output (which is total business sales). Depending on an area’s industry mix, GDP is
typically in the range of 1/2 to 3/4 the magnitude of output. Converting the reported output
change from TPICS into GDP terms for comparability leads to a direct impact of around $392.8
Million. This impact is double that of the SHRP2 C11 tool calculation of GDP growth associated
with enhanced intermodal connectivity.
Table 63: Comparison of TPICS Case Study Impacts with Calculations from the Intermodal
Connectivity Tool
Connectivity Impact on
Productivity
Productivity Impact
$196.2 million/yr. (2014 $)
$178.6 million/yr. (2008 $)
Other Benefits
--
Area Economic Growth
Impact
--
Reported by
TPICS Case Study
Value Derived from
TPICS Case Study
--
--
Congestion reduction,
operating cost savings,
capacity for local land
development
Output: $654.7 million
direct
Not Available
GDP: $392.8 million
direct
This finding is logical, since U.S. 281 provides more benefit than just airport access. It also
provides a critical north-south thoroughfare in San Antonio that is used to access the airport,
the downtown core, and to bypass local streets at much higher speeds. The highway has
spurred development along the corridor, allowed direct access to San Antonio, saved travel
time by reducing congestion growth, and provided greater capacity for economic activity along
the transportation network.
The construction of U.S. 281 is recognized locally as a factor that contributed to the
development of downtown tourism and the convention industry, and provided the business
community in the downtown with more reliable access to the airport. The total economic
59
impacts identified by the TPICS ex post case study include the overall development in the city
that were spurred by the new highway. Thus, the finding that TPICS showed a greater total
economic impact is consistent with the conclusion that intermodal connectivity effects are only
one of several factors driving the overall economic impact of U.S. 281.
60

Application of C11 Tools to a Sample of Existing TPICS Cases

Transcription

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