NJ Pilot Report Appendics

Transcription

Testing Potential MAP-21
New Jersey Pilot Study
Appendices
October 2014
System Performance Measures
for Two Corridors
ABOUT THE NJTPA
THE NJTPA IS THE FEDERALLY AUTHORIZED Metropolitan Planning
Organization for 6.6 million people in the 13-county northern New Jersey
region. Each year, the NJTPA oversees more than $2 billion in transportation
improvement projects and provides a forum for interagency cooperation and
public input. It also sponsors and conducts studies, assists county planning
agencies and monitors compliance with national air quality goals.
DISCLAIMER
THIS DOCUMENT WAS PREPARED by the North Jersey Transportation
Planning Authority, Inc. with funding from the Federal Transit Administration
and the Federal Highway Administration and technical contributions from the
New Jersey Department of Transportation. The NJTPA is solely responsible for
its content. The document is meant for informational purposes only.
PROJECT TEAM
BRIAN FINEMAN , NJTPA, Director, Systems Planning
KEITH MILLER , NJTPA, Principal Planner, Data Analysis and Forecasting
SUTAPA BHATTARCHARJEE , NJTPA, Principal Transportation Planner
JOHN ALLEN , NJDOT, Section Chief, Commuter/Mobility Strategies
Appendix A
Draft Scope of Work
for Two Corridors
NEW JERSEY PILOT STUDY
A-1
New Jersey’s Pilot Study of Using AASHTO’s Performance Measure Methodology for MAP-‐21 (Secondary Effort -‐ Partners in Using Archived Ops Data “Shared” Measures) Proposed Scope Outline July 26, 2013 (Note: All measures, alternatives, and variations discussed in this scope are to be investigated, and performed where possible) Background/Context Moving Ahead for Progress in the 21st Century (MAP-‐21) establishes these national (system) performance goals for Federal Highway programs (among others): Congestion Reduction -‐ to achieve a significant reduction on congestion on the NHS System Reliability -‐ to improve the efficiency of the surface transportation system To date, USDOT has not formalized performance measures for these goals; however, AASHTO’s Standing Committee on Performance Management (SCOPM) recommends these national-‐level system performance measures in their Findings on National Performance Measures document (11.09.2012): Annual Hours of Delay (AHD) – travel time above a congestion threshold (defined by State DOTs and MPOs) Reliability Index (RI80) – the ratio of the 80th percentile travel time to the agency-‐determined threshold travel time These measures are to be calculated for Interstate and the National Highway System (NHS) corridors within each State. At the kick-‐off NJ MAP-‐21 Performance Measure Committee (called for and hosted by NJDOT), John Allen (NJDOT) expressed concern of the SCOPM’s PM methodologies, in terms of data availability, level of effort and usability of results in “telling the story” of project effectiveness and prudent fiscal expenditures for New Jersey. John suggested that a pilot corridor be chosen to “test” the proposed methodologies to answer some or all of these concerns. Subsequently, Keith Miller of the North Jersey Transportation Planning Authority (NJTPA) took the lead in assembling a Scope of Work for a Pilot Study of two corridors in New Jersey – an Interstate (I-‐78) and a non-‐
Interstate NHS (NJ Route 18). The following SOW, a collaborative effort between NJDOT and the NJTPA, lays out general procedures, assumptions, and some up-‐front issues and decision points for evaluating the corridors. To the extent possible, this scope also covers “shared” measures, as agreed to by the Partners in Using Archived Operations Data Committee, a multi-‐state group essentially aligned with the I-‐95 Corridor Coalition. ICON KEY Issues/Considerations Progress Assumptions Calculations Page | 1 Corridors All performance measures will be calculated on a TMC level, and then aggregated up to sub-‐corridors and corridors using appropriate weighting factors (straight summation for AHD, weighted average for RI80 and travel time (see below)). Only the mainline will be evaluated (no ramps/interchanges). •
I-78 (Interstate; rural to urban; local & express lanes; toll & free) 78-C
78-B
78-A
o
•
78-D
Entire length (I-‐78 Toll Bridge/PA border to Holland Tunnel/NY Border – 67.8 miles), subdivided into 4 sub-‐corridors: § PA border to I-‐287 (30.8 miles) § I-‐287 to GSP (22.6 miles) § GSP to NJ Turnpike (5.4 miles) § NJ Turnpike to Holland Tunnel (9.0 miles) NJ 18 (NHS freeway & arterial; limited access & traffic signals; urban, commercial, semi-‐rural) 18
-D
18
-C
18
-B
18-A
o
Entire length (NJ 138 to Hoes Lane – 45.3 miles), subdivided into 4 sub-‐corridors: § NJ 138 to GSP (14.3 miles) § GSP to US 9 (16.1 miles) § US 9 to NJ Turnpike (9.5 miles) § NJ Turnpike to Hoes Lane (5.4 miles) Issues/Considerations Progress Assumptions Calculations Page | 2 Analysis Period •
Calendar year 2012 (Jan 1 through Dec 31) Annual Hours of Delay (AHD) – National Highway Performance Program (NHPP) and Freight System Performance, CMAQ Traffic Congestion & Shared Measure •
Data: o
o
o
o
o
o
o
Obtained hourly average travel times for each day of an “average week” using the VPP Suite Massive Raw Data Downloader for the entire calendar year 2012 Calculate average travel times by day of week and hour of day over entire year (e.g., average 8:00-‐8:59AM Mondays, average 8:00-‐8:59AM Tuesdays, etc.) to create “synthetic average week” Obtained hourly vehicle volumes from the NJCMS (manually conflated to the corridor’s TMCs) Obtain hourly truck volumes from the NJCMS and/or Weigh-‐in-‐Motion (WIM) data Obtain hourly bus volumes from NJ TRANSIT, private carrier schedules Obtain vehicle occupancy from sources such as Plan4Safety, NJ TRANSIT and the NJRTM-‐E Considerations/Issues: § Concept of “average week” from AASHTO recommended method, but how this is to be determined was unspecified. • Intention is to calculate a “synthetic average week” from averaging days of week over 52 weeks in a year (e.g., an “average Sunday”, “average Monday”, etc.). • While we could possibly look for a specific week that closely matches the “average week” (avoiding major/minor holidays, school vacation, special events, weather events, etc.), there would still be a concern whether that same week would be “average” from year to year. § NJCMS does not distinguish between days of week, but does provide estimated hourly volumes for a “typical weekday.” • Assume that all weekdays are identical. § Investigate whether weekend factors can/should be used (both weekend day to “typical weekday” and weekend hourly breakdown). § Conflation effort is underway for the two pilot corridors. This is a time-‐consuming, carriageway-‐by-‐carriageway process, but it results in an SRI (standard route identifier), and beginning and ending mileposts for each TMC segment. This can then be used to calculate an “average volume” for each TMC (weighted by segment length). § The NJCMS does not provide separate volumes for express and local lanes of I-‐78, whereas INRIX provides distinct travel times for these TMC links. • Initial approach is to use the number of lanes (3 in local and 2 in express) to divide the NJCMS volume. • CMS-‐21 may have a breakdown of volumes for local and express. § Limited NJ TRANSIT bus service on I-‐78 and NJ 18 § Some private carrier bus service on I-‐78 and NJ 18, but passenger volumes may be difficult to obtain for private carriers Issues/Considerations Progress Assumptions Calculations Page | 3 Investigate vehicle occupancy (from crash data) by link/subcorridor/corridor, or county & functional classification Incorporate bus passenger volumes §
§
•
AASHTO Recommended Calculation Method (Assumptions): AASHTO Example (pg 25): “…use…Threshold Speed in comparison with a dataset of hourly speeds for each day of the average week… Any of the 168 speeds (7 days x 24 hours) that are below the…threshold speed would be determined ‘experiencing delay’; the vehicle-‐miles of travel for that hour on that road section would be multiplied by the minutes of extra travel time…” o Don’t count “negative delay” (travel time less than threshold) o “Volume” can be vehicle volumes or person volumes (vehicle volume x vehicle occupancy) o Threshold can be constant (e.g., freeflow travel time) or based on hour of day and day of week (e.g., hourly medians; see TT Threshold Variations below) o
•
AASHTO Recommended Calculation Method (Calculations): o
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!"#!! !" !"# ×!"# 0, !!!! !" !"#,!"# !" !""# − !ℎ!"#ℎ!"# !!!! !" !"#,!"# !" !""#
!"#
o
!!!"
!"# = 52×
!"# !" !""#!!"#
•
!! !" !"#!!"!"
!"#$%!!,!""#$%&
Alternate Calculation Method: Rationale: using an “average week” may underestimate actual annual delay; using actual hourly data may give a fuller representation of delay. o Instead of using a synthetic “average week”, calculate delay for all 8,760 hours in the year (24 hrs/day x 365 days) o Volume would correspond to hour of the day, and can be vehicle volumes or person volumes o Threshold can be constant or based on hour of day and day of week o
!"# =
!!!" !"# !"
!! !"!" !"# !
!"#!! !" !"# ×!"# 0, !!! − !ℎ!"#ℎ!"# !!!! !" !"#,!"# !" !""#
•
•
Considerations/Issues: o
Travel Time Threshold Variations (all capped by travel time at posted speed): § Freeflow travel time § Median travel time (for each hour of day and day of week) § Maximum throughput travel time (e.g., at 70% posted speed) § “Acceptable travel time” – Based on area type (assigned for each TMC segment) and time of day, travel time at “acceptable speeds”, e.g., Area Type Urban Suburban Rural Issues/Considerations Percent of Freeflow Speed Peak Off-‐peak 60% 75% 75% 85% 90% 95% Progress This table is merely a starting point, and needs to be examined in context of specific segments (e.g., Route 18 in New Brunswick) to determine whether adjustments are appropriate, and whether different factors are needed for freeways vs. arterials. Assumptions Calculations Page | 4 o
o
o
o
Measurement Unit Variations: § Vehicle-‐hours of delay § Person-‐hours of delay (shared measure) § Delay per capita/traveler (per capita may require definition of roadway catchment area) § Delay per mile/lane-‐mile Measurement Period Variations: § All Day • Default AASHTO recommendation. • Captures any congestion that might occur during off-‐peak or weekend time periods (e.g., construction-‐ or incident-‐related), to the extent that they show up in the probe data. • In the future, more hours may have real-‐time probe data, as number of probes increase (or other sources of travel time are made available). • Including off-‐peak/weekend delay shouldn’t dilute the impact of any congestion that occurs during the weekday peak periods. (However, it would be inappropriate to divide the AHD by 365 and present this as average daily delay.) Peak/off-‐peak § AASHTO mentions that it may be useful to present total annual delay during peak periods and off-‐peak periods separately. Vehicle Type Variations: § All Vehicles (NHPP Performance Measure) § Trucks only (Freight System Performance Measure) § Buses only Issues/Considerations Progress Assumptions Calculations Page | 5 Reliability Index (RI80) – NHPP and Freight System Performance & Shared Measure •
Data: o
o
o
•
Downloaded five-‐minute travel time data for one year – VPP Suite Massive Raw Data Downloader § Required for AASHTO-‐recommended method. § Captures short-‐lived delay. § Only consider records with some real-‐time probe data (confidence > 20) Volumes – NJCMS (as above) Vehicle occupancy – Plan4Safety, NJ TRANSIT, NJRTM-‐E (as above) AASHTO Recommended Calculation Method (Calculations): Represents the multiplier needed to be on time 80 percent of the time if you happen to be traveling in the worst five-‐minute period of the day (compared to a threshold travel time for that segment, regardless of time of day). o Divide the day into 288 five-‐minute intervals (24x12=288) o For each five-‐minute interval (e.g., 8:00-‐8:05AM), array travel times from all days (either calendar days or workdays) in ascending order o Calculate 80th percentile travel time (TT80) for each five-‐minute interval, across all days (either calendar days or workdays) o From the 288 values of TT80, select highest value over the entire day (or just over the peak period) !"#$%&% !!!"
o !"!" =
!ℎ!"#ℎ!"# !! o Note that the shared measures discussion coalesced around using median speed as the threshold speed for reliability. However, only non-‐time-‐dependent threshold travel times can be used in this formulation (one value per segment), and thus median travel time would need to be calculated over the entire day (or just the peak), not by time of day and/or day of week. •
Alternate Calculation Method: Rationale: to account for variations in median travel time (used as a threshold) over the course of a day. Varying the threshold over the day may be more reflective of the road users’ perception of reliability. o Represents the multiplier that is needed to be on time 80 percent of the time, compared to the typical/expected travel time for that segment for the time of day that you’re traveling. o For each of the 288 five-‐minute intervals, calculate the median travel time (either over all calendar days or just workdays), !!!" o For each five-‐minute period over entire year (288x365=105,120 periods), calculate Travel Time Ratio (!!"! ), using the median travel time corresponding to that five-‐minute period (!!!" ! ) !!!
!!"! =
!!!" !
o
o
Array values of TTR for entire day (or just peak period) from all days (either calendar days or workdays) in ascending order Calculate the 80th percentile value of TTR. Issues/Considerations Progress Assumptions Calculations Page | 6 •
Considerations/Issues: o
o
o
o
o
o
Travel Time Threshold Variations (all capped by travel time at posted speed): § Freeflow travel time § Median travel time (shared measure) • Over entire day or peak period – for AASHTO formulation • For each five-‐minute interval within a day – for Alternate formulation § Maximum throughput travel time (e.g., at 70% posted speed) § “Acceptable travel time” (see discussion under AHD) § Consider pluses/minuses of using same or different thresholds for AHD and RI80 Measurement Period Variations: § All Day (365 days) § Workdays1 § Peak/off-‐peak Segment/Corridor Aggregation Variations: § VMT § PMT § Peak period volumes Vehicle Type Variations: § All Vehicles Trucks only (Freight System Performance Measure) Buses only 1
AASHTO cites “240 days” as the number of workdays in a year, which is the typical citation for the number of workdays in a year. However, that includes accounting for vacation/sick days; we can really only exclude weekends and major holidays, so the number of workdays is closer to 250 days (260 weekdays, minus 10 major holidays—New Year’s, MLK Birthday, Presidents’ Day, Memorial Day, Independence Day, Labor Day, Columbus Day, Veterans Day, Thanksgiving, and Christmas) Issues/Considerations Progress Assumptions Calculations Page | 7 Travel Time – Shared Measure •
Data: o
o
o
•
Calculation Methods (compute all): o
o
o
•
Vehicle volumes Person volumes Measurement Unit Variations: o
o
o
•
Freeflow travel time § Reference speed from VPP Suite hourly travel time database “Usual” travel time § Median travel time calculated for thresholds • for AHD (by hour of day and day of week) • for RI80 (over entire day or just peak period) • for Alternate reliability measure (by hour of day) “Worst day of the week” travel time § Maximum TT80 calculated for RI80 § 80th percentile travel time from five-‐minute travel time database (over entire day or just peak period) Segment/Corridor Aggregation Variations: o
o
•
Downloaded hourly and five-‐minute average travel times for one year – VPP Suite Massive Raw Data Downloader Volumes – NJCMS (as above) Vehicle occupancy (by time of day?) – Plan4Safety, NJ TRANSIT (as above) Aggregate travel time Travel time per vehicle Travel time per capita/traveler (per capita may require definition of roadway catchment area) Measurement Period Variations: o
o
All Day Peak/off-‐peak Issues/Considerations Progress Assumptions Calculations Page | 8 Pilot Study Logistics There are several logistical elements of the Pilot Study that will be employed to ensure successful completion: •
VPP Suite Users Group Participation …to make this effort successful here in NJ as well as elsewhere, we will engage and coordinate with the VPP Suite Users Group (via facilitation through the I-‐95 Corridor Coalition) to leverage the experience, talents and expertise of group members. By doing this, we will achieve a better overall process that can be used in other States successfully. •
Step-‐by-‐Step Documentation/Presentations…as we move through each task, there will be thorough and comprehensive documentation of each step of the processes, to ensure that it can be replicated elsewhere. Also, presentations will be done to highlight and summarize efforts at key junctures, through webcasts or other means. A feedback loop may be established to receive comments, as well as provide information to AASHTO and the USDOT of our experiences, problems and challenges as they occur. •
Lessons Learned…this piece may turn out to be one of the most important sections in the document, as capturing how we dealt with problems and challenges, and what we learned from the overall process will help establish better analytical methodologies, visualizations, and ultimately, effectiveness of MAP-‐
21. •
Additional Evaluations…this scope provides for just one of the possible tests that could be done of SCOPM’s methodologies and the overall philosophy of asset management (performance measurement, target-‐setting and achieving real results). For example, multi-‐year analyses should be conducted to see what PM changes occur over time, and whether those changes are meaningful, at varying scales (sub-‐
corridor, corridor, state). It would also be very beneficial, indeed critical, to conduct analyses of roadways that have had projects recently completed, to see the effects of the improvement in terms of PM changes. Issues/Considerations Progress Assumptions Calculations Page | 9 Appendix B
Detailed Tables of Results
by Sub-corridor and Corridor
for Two Corridors
B-1
Calculation Method->
Threshold->
Corridor/
Subcorridor
18 Corridor
18 Corridor
18 Corridor
18A
18A
18A
18B
18B
18B
18C
18C
18C
18D
18D
18D
78 Corridor
78 Corridor
78 Corridor
78A
78A
78A
78B
78B
78B
78C
78C
78C
78D
78D
78D
Direction
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
Miles
76.43
38.29
38.14
17.61
8.69
8.92
32.75
16.56
16.18
17.90
8.89
9.01
8.18
4.15
4.03
155.58
79.18
76.40
61.45
30.05
31.39
54.58
28.38
26.20
21.36
10.34
11.02
18.19
10.40
7.79
AppendixB_DetailedTables.xlsm
Free-flow
Vehicles
1,191,000
470,400
721,100
31,570
16,440
15,130
4,781
1,212
3,569
703,800
330,800
373,000
451,300
121,900
329,300
2,969,000
1,940,000
1,029,000
365,200
181,300
184,000
308,700
183,300
125,500
714,900
243,100
471,800
1,580,000
1,332,000
247,700
Persons
(no transit)
1,595,000
632,700
962,100
40,020
20,840
19,180
6,111
1,549
4,561
960,500
451,400
509,100
588,200
158,900
429,200
4,126,000
2,702,000
1,425,000
517,800
257,000
260,800
405,000
240,400
164,600
987,600
335,800
651,800
2,216,000
1,868,000
347,500
Annual Hours of Delay (AASHTO Method)
"Acceptable" Speed
Persons
(w/ transit)
1,666,000
663,700
1,002,000
40,400
21,050
19,350
6,206
1,574
4,632
1,018,000
478,500
539,400
601,100
162,600
438,600
4,392,000
2,823,000
1,569,000
563,900
277,900
286,000
440,800
260,400
180,400
1,130,000
384,100
745,700
2,257,000
1,900,000
356,900
Buses Trucks Vehicles
2,222 58,470 489,200
1,014 18,580 160,300
1,208 39,890 328,900
16.68
1,429
0.70
9.112
820.3
0.70
7.573
608.5
0.00
4.386
425.7
2,418
1.208
69.45
763.7
3.178
356.2
1,654
1,795 47,840 269,900
898.5 14,440 122,800
896.7 33,400 147,100
405.8
8,774 216,800
105.5
3,255
36,690
300.3
5,519 180,200
7,440 213,700 1,008,000
3,263 130,700 743,200
4,177 82,990 264,500
1,288 46,160 240,200
576.1 23,920 105,000
711.5 22,240 135,200
1,118 19,650
32,600
583.9
9,062
13,940
534.4 10,580
18,660
4,085 51,100
39,610
1,389 17,820
2,090
2,696 33,280
37,520
949.6 96,770 695,300
714.1 79,890 622,200
235.5 16,880
73,060
Appendix B
Persons
(no transit)
654,100
216,500
437,700
0.88
0.88
0.00
3,090
976.1
2,114
368,400
167,700
200,700
282,600
47,820
234,800
1,413,000
1,043,000
370,600
340,600
148,800
191,800
42,760
18,280
24,480
54,720
2,888
51,840
975,200
872,800
102,500
Persons
(w/ transit)
684,300
228,400
455,900
0.90
0.90
0.00
3,135
993.1
2,142
392,300
178,000
214,300
288,800
49,390
239,400
1,474,000
1,071,000
402,800
370,600
160,400
210,200
46,460
20,250
26,210
66,640
3,576
63,060
990,500
887,200
103,300
Buses
894.9
370.0
524.9
0.00
0.00
0.00
2.188
0.84
1.342
713.4
332.7
380.7
179.3
36.47
142.8
1,639
738.8
900.4
885.3
347.0
538.3
128.2
53.96
74.20
281.9
15.47
266.4
343.9
322.4
21.53
Trucks
21,580
6,216
15,360
0.07
0.07
0.00
225.8
48.06
177.7
17,690
5,222
12,470
3,668
946.1
2,722
75,400
50,090
25,310
32,620
15,320
17,300
2,756
871.3
1,884
2,515
71.72
2,443
37,510
33,830
3,685
Page 1 of 9
Threshold->
Corridor/
Subcorridor
18 Corridor
18 Corridor
18 Corridor
18A
18A
18A
18B
18B
18B
18C
18C
18C
18D
18D
18D
78 Corridor
78 Corridor
78 Corridor
78A
78A
78A
78B
78B
78B
78C
78C
78C
78D
78D
78D
Direction
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
Miles Vehicles
76.43 172,300
38.29
68,730
38.14 103,600
17.61
2,798
8.69
757.3
8.92
2,041
32.75
3,484
16.56
1,024
16.18
2,460
17.90
85,520
8.89
46,730
9.01
38,790
8.18
80,540
4.15
20,220
4.03
60,320
155.58 1,165,000
79.18 637,600
76.40 527,900
61.45 252,100
30.05 122,600
31.39 129,500
54.58 230,900
28.38 149,000
26.20
81,910
21.36 340,600
10.34 117,200
11.02 223,300
18.19 341,900
10.40 248,800
7.79
93,130
Day/Hour Median
Maximum Throughput
Persons
(no transit)
229,700
92,400
137,300
3,547
960.0
2,587
4,453
1,309
3,145
116,700
63,780
52,940
105,000
26,360
78,610
1,610,000
880,100
730,300
357,500
173,900
183,700
302,900
195,400
107,500
470,500
161,900
308,500
479,600
349,000
130,600
Persons
(w/ transit)
239,000
97,490
141,500
3,576
971.3
2,605
4,521
1,330
3,191
123,400
68,310
55,140
107,500
26,880
80,590
1,754,000
941,300
812,600
390,700
188,900
201,800
331,800
212,500
119,300
541,400
186,600
354,800
490,000
353,300
136,700
Buses
279.5
139.2
140.3
1.064
0.42
0.63
3.200
1.061
2.139
194.1
120.6
73.49
81.12
17.11
64.02
4,023
1,673
2,350
978.7
432.7
546.0
839.7
480.7
359.0
1,957
662.6
1,295
247.3
96.75
150.5
Trucks
8,413
2,853
5,559
101.0
41.56
59.45
318.7
63.65
255.1
6,149
2,108
4,041
1,844
640.1
1,204
95,790
50,910
44,880
35,560
18,200
17,370
13,220
7,410
5,813
23,680
8,086
15,600
23,310
17,210
6,101
Appendix B
Vehicles
686,000
232,000
454,000
243.4
14.61
228.8
1,713
379.6
1,334
385,600
179,000
206,600
298,500
52,660
245,800
2,018,000
1,394,000
623,400
97,540
35,200
62,340
107,000
67,250
39,720
490,100
104,200
385,900
1,323,000
1,188,000
135,400
Persons
(no transit)
917,700
313,400
604,300
308.5
18.52
290.0
2,190
485.2
1,705
526,200
244,200
282,000
389,000
68,630
320,400
2,812,000
1,948,000
863,500
138,300
49,900
88,390
140,300
88,210
52,110
677,000
143,900
533,100
1,856,000
1,666,000
189,900
Persons
(w/ transit)
961,900
330,900
630,900
310.7
18.82
291.9
2,223
494.3
1,729
561,600
259,800
301,700
397,800
70,570
327,200
2,974,000
2,013,000
961,500
150,600
53,700
96,890
151,400
95,660
55,740
784,400
170,100
614,300
1,888,000
1,694,000
194,600
Buses Trucks
1,304 30,950
543.5
9,084
760.3 21,860
0.08
7.687
0.01
1.395
0.06
6.292
1.651
188.5
0.45
32.68
1.196
155.8
1,050 25,550
496.9
7,669
553.4 17,890
251.8
5,198
46.12
1,380
205.7
3,817
4,442 129,100
1,631 82,470
2,811 46,580
347.6 12,300
102.3
4,413
245.3
7,892
374.6
7,355
212.7
2,986
161.9
4,369
2,987 32,720
700.4
6,640
2,286 26,080
733.4 76,680
616.1 68,430
117.2
8,246
Page 2 of 9
Threshold->
Corridor/
Subcorridor
18 Corridor
18 Corridor
18 Corridor
18A
18A
18A
18B
18B
18B
18C
18C
18C
18D
18D
18D
78 Corridor
78 Corridor
78 Corridor
78A
78A
78A
78B
78B
78B
78C
78C
78C
78D
78D
78D
Direction
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
Annual Median
Miles Vehicles
76.43 877,900
38.29 297,700
38.14 580,200
17.61
14,440
8.69
6,853
8.92
7,591
32.75
4,158
16.56
1,212
16.18
2,945
17.90 458,800
8.89 199,100
9.01 259,700
8.18 400,500
4.15
90,510
4.03 310,000
155.58 2,287,000
79.18 1,482,000
76.40 805,300
61.45 357,600
30.05 175,700
31.39 181,800
54.58 289,500
28.38 172,400
26.20 117,100
21.36 446,800
10.34 135,300
11.02 311,400
18.19 1,194,000
10.40 998,700
7.79 194,900
Persons
(no transit)
1,172,000
400,000
771,900
18,310
8,687
9,623
5,314
1,549
3,765
626,200
271,800
354,400
522,000
118,000
404,100
3,178,000
2,063,000
1,115,000
507,000
249,200
257,800
379,800
226,200
153,700
617,200
186,900
430,200
1,674,000
1,401,000
273,300
Persons
(w/ transit)
1,224,000
420,100
804,000
18,480
8,776
9,701
5,394
1,574
3,820
666,400
288,800
377,600
533,800
120,900
412,900
3,398,000
2,163,000
1,235,000
552,200
269,500
282,700
413,800
245,200
168,600
720,400
218,400
502,000
1,711,000
1,430,000
281,800
Buses Trucks
1,575 41,900
630.8 12,000
944.2 29,900
7.093
598.0
3.879
340.1
3.214
257.9
3.743
365.4
1.208
69.45
2.535
296.0
1,206 33,400
545.4
9,173
660.6 24,220
358.2
7,542
80.32
2,421
277.9
5,121
5,921 163,500
2,578 98,320
3,343 65,130
1,277 45,610
565.0 23,450
712.5 22,160
1,050 18,290
549.6
8,384
500.1
9,903
2,736 29,680
818.1
8,885
1,918 20,800
857.5 69,870
645.1 57,600
212.4 12,270
Appendix B
Annual Hours of Delay (NJTPA Method)
Free-flow
Vehicles
1,241,000
496,100
744,800
36,890
18,820
18,070
16,790
7,119
9,674
717,600
339,900
377,700
469,600
130,200
339,300
3,223,000
2,067,000
1,156,000
459,400
230,900
228,500
411,900
238,200
173,600
728,300
250,200
478,100
1,623,000
1,348,000
275,300
Persons
(no transit)
1,660,000
666,600
993,000
46,760
23,860
22,900
21,460
9,099
12,360
979,300
463,900
515,500
612,000
169,700
442,300
4,475,000
2,876,000
1,598,000
651,400
327,400
324,000
540,300
312,500
227,800
1,006,000
345,600
660,500
2,277,000
1,891,000
386,100
Persons
(w/ transit)
1,732,000
698,700
1,034,000
47,210
24,100
23,110
21,840
9,271
12,570
1,038,000
491,800
546,000
625,400
173,500
451,900
4,767,000
3,011,000
1,756,000
709,700
354,900
354,900
588,400
338,700
249,700
1,150,000
394,700
755,000
2,320,000
1,923,000
396,500
Page 3 of 9
Threshold->
Corridor/
Subcorridor
18 Corridor
18 Corridor
18 Corridor
18A
18A
18A
18B
18B
18B
18C
18C
18C
18D
18D
18D
78 Corridor
78 Corridor
78 Corridor
78A
78A
78A
78B
78B
78B
78C
78C
78C
78D
78D
78D
Direction
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
Miles
76.43
38.29
38.14
17.61
8.69
8.92
32.75
16.56
16.18
17.90
8.89
9.01
8.18
4.15
4.03
155.58
79.18
76.40
61.45
30.05
31.39
54.58
28.38
26.20
21.36
10.34
11.02
18.19
10.40
7.79
"Acceptable" Speed
Day/Hour Median
Vehicles
597,100
202,900
394,300
1,037
162.7
874.1
11,030
4,974
6,059
312,900
145,200
167,800
272,100
52,560
219,600
1,701,000
1,112,000
588,700
380,300
184,900
195,400
231,100
135,900
95,200
210,500
54,720
155,800
879,200
736,900
142,300
Persons
(no transit)
797,200
273,200
524,000
1,314
206.3
1,108
14,100
6,358
7,744
427,100
198,100
229,000
354,700
68,500
286,200
2,366,000
1,550,000
816,800
539,200
262,200
277,000
303,100
178,200
124,900
290,800
75,600
215,300
1,233,000
1,034,000
199,600
Persons
(w/ transit)
832,700
287,600
545,100
1,323
208.5
1,115
14,360
6,479
7,880
454,500
210,500
244,000
362,500
70,400
292,100
2,515,000
1,618,000
897,700
587,800
284,500
303,300
330,800
193,500
137,200
340,400
88,230
252,200
1,256,000
1,051,000
205,000
Vehicles
386,400
158,200
228,200
13,890
6,111
7,782
15,800
6,990
8,814
188,500
97,880
90,590
168,200
47,240
121,000
1,778,000
1,003,000
775,200
385,000
192,600
192,400
357,200
214,800
142,400
447,200
157,700
289,500
588,800
437,900
150,800
Appendix B
Persons
(no transit)
514,300
211,800
302,400
17,610
7,747
9,865
20,200
8,934
11,270
257,200
133,600
123,600
219,200
61,560
157,700
2,458,000
1,387,000
1,071,000
545,900
273,100
272,900
468,600
281,800
186,800
617,800
217,900
399,900
825,900
614,300
211,600
Persons
(w/ transit)
534,900
222,400
312,500
17,780
7,828
9,954
20,560
9,104
11,460
272,400
142,600
129,800
224,200
62,840
161,400
2,659,000
1,476,000
1,183,000
595,800
296,700
299,000
511,900
306,100
205,800
708,200
249,600
458,700
843,500
624,100
219,500
Page 4 of 9
Threshold->
Corridor/
Subcorridor
18 Corridor
18 Corridor
18 Corridor
18A
18A
18A
18B
18B
18B
18C
18C
18C
18D
18D
18D
78 Corridor
78 Corridor
78 Corridor
78A
78A
78A
78B
78B
78B
78C
78C
78C
78D
78D
78D
Direction
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
Miles
76.43
38.29
38.14
17.61
8.69
8.92
32.75
16.56
16.18
17.90
8.89
9.01
8.18
4.15
4.03
155.58
79.18
76.40
61.45
30.05
31.39
54.58
28.38
26.20
21.36
10.34
11.02
18.19
10.40
7.79
Maximum Throughput
Annual Median
Vehicles
763,600
268,100
495,500
1,621
258.7
1,362
7,637
3,376
4,261
417,200
195,700
221,500
337,200
68,810
268,400
2,590,000
1,689,000
901,300
298,000
142,100
155,900
281,800
167,000
114,800
579,400
149,000
430,400
1,431,000
1,231,000
200,100
Persons
(no transit)
1,021,000
361,400
659,300
2,055
327.9
1,727
9,761
4,315
5,446
569,400
267,000
302,300
439,500
89,690
349,800
3,600,000
2,353,000
1,247,000
422,500
201,500
221,100
369,700
219,000
150,700
800,400
205,800
594,600
2,007,000
1,726,000
280,700
Persons
(w/ transit)
1,068,000
380,800
687,700
2,069
331.6
1,738
9,945
4,396
5,549
607,100
284,000
323,000
449,400
92,010
357,400
3,830,000
2,451,000
1,379,000
461,900
219,100
242,800
402,900
237,600
165,300
921,900
239,800
682,100
2,043,000
1,755,000
288,900
Vehicles
943,500
332,300
611,200
22,830
10,970
11,860
16,270
7,119
9,150
480,900
212,400
268,600
423,500
101,800
321,700
2,629,000
1,658,000
970,700
453,000
226,300
226,800
396,500
229,400
167,100
512,700
167,000
345,800
1,267,000
1,036,000
231,100
Appendix B
Persons
(no transit)
1,258,000
445,500
812,500
28,940
13,910
15,030
20,790
9,099
11,690
656,300
289,800
366,500
552,000
132,700
419,300
3,648,000
2,305,000
1,343,000
642,400
320,800
321,600
520,100
300,900
219,200
708,300
230,600
477,700
1,777,000
1,453,000
324,100
Persons
(w/ transit)
1,313,000
467,400
845,600
29,210
14,040
15,170
21,160
9,271
11,890
698,400
308,200
390,200
564,300
135,900
428,400
3,902,000
2,422,000
1,479,000
700,000
347,800
352,200
566,800
326,400
240,400
819,300
266,300
553,000
1,816,000
1,482,000
333,800
Page 5 of 9
Threshold->
Corridor/
Subcorridor
18 Corridor
18 Corridor
18 Corridor
18A
18A
18A
18B
18B
18B
18C
18C
18C
18D
18D
18D
78 Corridor
78 Corridor
78 Corridor
78A
78A
78A
78B
78B
78B
78C
78C
78C
78D
78D
78D
Direction
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
Miles
76.43
38.29
38.14
17.61
8.69
8.92
32.75
16.56
16.18
17.90
8.89
9.01
8.18
4.15
4.03
155.58
79.18
76.40
61.45
30.05
31.39
54.58
28.38
26.20
21.36
10.34
11.02
18.19
10.40
7.79
Min.
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.03
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.14
1.14
1.18
1.00
1.00
1.02
Max.
5.26
5.26
4.76
1.32
1.32
1.22
1.11
1.08
1.11
4.09
4.09
3.46
5.26
5.26
4.76
7.53
7.53
4.53
3.22
1.82
3.22
3.71
2.22
3.71
4.53
2.17
4.53
7.53
7.53
3.80
Travel
Time Length
1.61
1.51
1.31
1.24
1.93
1.80
1.06
1.06
1.06
1.06
1.06
1.06
1.02
1.02
1.02
1.02
1.03
1.03
1.83
1.75
1.90
1.82
1.76
1.70
2.14
2.13
1.28
1.26
3.19
3.18
1.47
1.39
1.54
1.41
1.40
1.36
1.22
1.22
1.24
1.25
1.19
1.19
1.24
1.23
1.16
1.15
1.33
1.32
1.66
1.65
1.33
1.33
1.96
1.96
2.36
2.12
2.91
2.69
1.48
1.37
Reliability Index-RI80 (AASHTO Method)
Free-flow
Average Weighted By
Vehicle Person Vehicle- Person- Vehicle- Person- Bus Truck Direct
Vol.
Vol.
Miles
Miles
Hours
Hours
Vol. Vol.
Calc.
1.83
1.83
1.67
1.68
1.86
1.87 1.95
1.61
1.26
1.27
1.32
1.33
1.43
1.45 1.43
1.24
1.00
2.50
2.50
2.06
2.07
2.30
2.31 2.47
1.98
1.06
1.07
1.07
1.06
1.06
1.06
1.06 1.07
1.07
1.07
1.07
1.06
1.06
1.06
1.06 1.07
1.07
1.02
1.06
1.06
1.06
1.06
1.06
1.06 1.06
1.06
1.02
1.03
1.03
1.02
1.02
1.02
1.02 1.03
1.03
1.03
1.03
1.02
1.02
1.02
1.02 1.03
1.03
1.00
1.03
1.03
1.02
1.02
1.02
1.02 1.03
1.03
1.01
1.80
1.80
1.87
1.87
2.00
2.00 1.81
1.69
1.89
1.89
1.97
1.97
2.10
2.10 1.87
1.78
1.67
1.71
1.71
1.77
1.77
1.90
1.90 1.73
1.64
1.56
2.05
2.06
2.18
2.18
2.30
2.30 2.28
1.90
1.20
1.20
1.31
1.31
1.38
1.38 1.25
1.16
1.00
3.17
3.17
3.24
3.24
3.30
3.30 3.12
3.12
1.00
1.50
1.49
1.40
1.40
1.57
1.56 1.38
1.40
1.52
1.51
1.43
1.42
1.69
1.68 1.28
1.41
1.44
1.48
1.48
1.37
1.37
1.43
1.43 1.47
1.39
1.33
1.21
1.20
1.24
1.24
1.25
1.25 1.19
1.18
1.26
1.26
1.27
1.26
1.27
1.26 1.24
1.24
1.26
1.16
1.15
1.22
1.22
1.23
1.23 1.14
1.14
1.20
1.35
1.35
1.26
1.26
1.28
1.27 1.33
1.33
1.24
1.24
1.18
1.17
1.19
1.19 1.21
1.21
1.17
1.47
1.47
1.35
1.34
1.37
1.36 1.44
1.44
1.29
1.64
1.64
1.62
1.62
1.66
1.66 1.65
1.67
1.40
1.40
1.36
1.36
1.36
1.36 1.37
1.36
1.41
1.88
1.89
1.86
1.87
1.90
1.91 1.94
1.97
2.02
2.44
2.43
2.24
2.23
2.73
2.73 1.75
2.20
3.00
2.98
2.81
2.79
3.27
3.27 2.04
2.73
2.43
1.64
1.64
1.40
1.40
1.60
1.60 1.35
1.57
1.40
Appendix B
Page 6 of 9
Threshold->
Corridor/
Subcorridor
18 Corridor
18 Corridor
18 Corridor
18A
18A
18A
18B
18B
18B
18C
18C
18C
18D
18D
18D
78 Corridor
78 Corridor
78 Corridor
78A
78A
78A
78B
78B
78B
78C
78C
78C
78D
78D
78D
Direction
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
Miles
76.43
38.29
38.14
17.61
8.69
8.92
32.75
16.56
16.18
17.90
8.89
9.01
8.18
4.15
4.03
155.58
79.18
76.40
61.45
30.05
31.39
54.58
28.38
26.20
21.36
10.34
11.02
18.19
10.40
7.79
Min.
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Max.
4.47
4.47
4.05
1.20
1.20
1.13
1.00
1.00
1.00
3.48
3.48
2.94
4.47
4.47
4.05
8.13
8.13
5.82
2.73
1.54
2.73
3.15
1.89
3.15
5.82
3.45
5.82
8.13
8.13
3.23
Travel
Time Length
1.45
1.37
1.20
1.16
1.71
1.60
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.58
1.52
1.64
1.57
1.52
1.48
1.88
1.87
1.19
1.17
2.73
2.72
1.36
1.28
1.42
1.29
1.29
1.26
1.11
1.11
1.11
1.11
1.10
1.10
1.15
1.15
1.08
1.08
1.22
1.22
1.56
1.52
1.20
1.19
1.87
1.83
2.18
1.95
2.72
2.49
1.32
1.23
Maximum Throughput
Average Weighted By
Vol.
Vol.
Miles
Miles
Hours
Hours
Vol. Vol.
Calc.
1.64
1.64
1.50
1.51
1.65
1.65 1.73
1.46
1.19
1.19
1.22
1.22
1.30
1.31 1.31
1.16
1.00
2.17
2.17
1.81
1.82
2.01
2.01 2.16
1.76
1.00
1.01
1.01
1.00
1.00
1.00
1.00 1.01
1.01
1.01
1.01
1.00
1.00
1.00
1.00 1.01
1.01
1.00
1.01
1.01
1.00
1.00
1.00
1.00 1.01
1.01
1.00
1.00
1.00
1.00
1.00
1.00
1.00 1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00 1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00 1.00
1.00
1.00
1.61
1.61
1.62
1.62
1.73
1.73 1.61
1.52
1.67
1.67
1.71
1.71
1.81
1.81 1.66
1.59
1.42
1.54
1.54
1.54
1.54
1.65
1.65 1.56
1.49
1.33
1.82
1.82
1.91
1.91
2.00
2.01 2.00
1.70
1.14
1.14
1.21
1.21
1.26
1.26 1.17
1.11
1.00
2.71
2.71
2.77
2.77
2.82
2.82 2.68
2.67
1.00
1.42
1.42
1.29
1.28
1.45
1.44 1.31
1.31
1.42
1.41
1.31
1.30
1.56
1.55 1.20
1.30
1.25
1.43
1.43
1.26
1.26
1.32
1.32 1.43
1.32
1.15
1.10
1.10
1.12
1.12
1.13
1.13 1.09
1.09
1.12
1.12
1.12
1.12
1.12
1.12 1.11
1.11
1.07
1.08
1.08
1.12
1.12
1.14
1.13 1.07
1.07
1.02
1.24
1.23
1.17
1.16
1.18
1.18 1.22
1.22
1.14
1.13
1.10
1.09
1.11
1.10 1.12
1.12
1.00
1.34
1.33
1.24
1.24
1.26
1.25 1.32
1.32
1.09
1.67
1.67
1.50
1.50
1.56
1.57 1.68
1.63
1.37
1.37
1.22
1.22
1.24
1.24 1.35
1.29
1.26
1.99
1.99
1.75
1.76
1.82
1.83 2.03
1.97
1.89
2.30
2.28
2.08
2.07
2.55
2.55 1.65
2.04
2.88
2.86
2.64
2.62
3.10
3.09 1.96
2.58
2.18
1.47
1.46
1.25
1.25
1.42
1.42 1.23
1.41
1.23
Appendix B
Page 7 of 9
Threshold->
Corridor/
Subcorridor
18 Corridor
18 Corridor
18 Corridor
18A
18A
18A
18B
18B
18B
18C
18C
18C
18D
18D
18D
78 Corridor
78 Corridor
78 Corridor
78A
78A
78A
78B
78B
78B
78C
78C
78C
78D
78D
78D
Direction
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
Miles
76.43
38.29
38.14
17.61
8.69
8.92
32.75
16.56
16.18
17.90
8.89
9.01
8.18
4.15
4.03
155.58
79.18
76.40
61.45
30.05
31.39
54.58
28.38
26.20
21.36
10.34
11.02
18.19
10.40
7.79
Min.
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.03
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.10
1.10
1.16
1.00
1.00
1.02
Max.
5.26
5.26
4.76
1.25
1.25
1.18
1.08
1.08
1.07
3.84
3.84
2.98
5.26
5.26
4.76
7.07
7.07
4.34
3.22
1.82
3.22
3.71
2.19
3.71
4.34
2.08
4.34
7.07
7.07
3.37
Travel
Time Length
1.57
1.48
1.27
1.21
1.90
1.77
1.05
1.05
1.05
1.05
1.05
1.05
1.02
1.02
1.02
1.02
1.02
1.02
1.69
1.64
1.75
1.69
1.63
1.59
2.12
2.11
1.26
1.24
3.17
3.16
1.42
1.35
1.46
1.36
1.37
1.34
1.21
1.22
1.24
1.25
1.19
1.19
1.24
1.23
1.15
1.15
1.32
1.32
1.57
1.57
1.26
1.26
1.86
1.85
2.10
1.93
2.52
2.37
1.42
1.33
Annual Median
Average Weighted By
Vol.
Vol.
Miles
Miles
Hours
Hours
Vol. Vol.
Calc.
1.80
1.80
1.64
1.64
1.81
1.81 1.89
1.57
1.24
1.24
1.28
1.29
1.38
1.39 1.37
1.21
1.00
2.46
2.46
2.03
2.03
2.25
2.25 2.42
1.93
1.05
1.06
1.06
1.05
1.05
1.05
1.05 1.06
1.06
1.06
1.06
1.05
1.05
1.05
1.05 1.06
1.06
1.01
1.05
1.05
1.05
1.05
1.05
1.05 1.05
1.05
1.01
1.03
1.03
1.02
1.02
1.02
1.02 1.03
1.03
1.03
1.03
1.02
1.02
1.02
1.02 1.03
1.03
1.00
1.03
1.03
1.02
1.02
1.02
1.02 1.03
1.03
1.01
1.64
1.64
1.73
1.73
1.82
1.82 1.65
1.55
1.74
1.74
1.82
1.81
1.91
1.91 1.72
1.65
1.56
1.55
1.55
1.64
1.64
1.73
1.74 1.56
1.50
1.48
2.04
2.05
2.16
2.16
2.27
2.27 2.27
1.89
1.19
1.19
1.29
1.29
1.35
1.35 1.24
1.15
1.00
3.16
3.16
3.22
3.22
3.28
3.28 3.11
3.11
1.00
1.45
1.44
1.37
1.36
1.50
1.49 1.34
1.36
1.45
1.44
1.39
1.38
1.58
1.57 1.26
1.36
1.41
1.44
1.44
1.35
1.35
1.40
1.40 1.43
1.36
1.32
1.20
1.20
1.24
1.24
1.25
1.25 1.18
1.18
1.26
1.26
1.26
1.26
1.26
1.26 1.24
1.24
1.26
1.15
1.15
1.22
1.22
1.23
1.23 1.13
1.13
1.20
1.35
1.35
1.25
1.25
1.27
1.27 1.33
1.33
1.24
1.23
1.17
1.17
1.19
1.18 1.21
1.21
1.17
1.46
1.46
1.34
1.34
1.36
1.36 1.44
1.44
1.28
1.54
1.54
1.54
1.54
1.57
1.57 1.54
1.57
1.33
1.33
1.29
1.29
1.29
1.29 1.29
1.29
1.34
1.75
1.75
1.76
1.76
1.79
1.80 1.81
1.85
1.91
2.21
2.20
2.03
2.02
2.40
2.40 1.64
2.00
2.66
2.65
2.49
2.47
2.82
2.81 1.88
2.41
2.20
1.57
1.56
1.36
1.36
1.53
1.52 1.32
1.50
1.36
Appendix B
Page 8 of 9
Threshold->
Corridor/
Subcorridor
18 Corridor
18 Corridor
18 Corridor
18A
18A
18A
18B
18B
18B
18C
18C
18C
18D
18D
18D
78 Corridor
78 Corridor
78 Corridor
78A
78A
78A
78B
78B
78B
78C
78C
78C
78D
78D
78D
Direction
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
NORTHBOUND
SOUTHBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
BOTH
EASTBOUND
WESTBOUND
Miles
76.43
38.29
38.14
17.61
8.69
8.92
32.75
16.56
16.18
17.90
8.89
9.01
8.18
4.15
4.03
155.58
79.18
76.40
61.45
30.05
31.39
54.58
28.38
26.20
21.36
10.34
11.02
18.19
10.40
7.79
Min.
1.01
1.01
1.03
1.03
1.03
1.03
1.04
1.04
1.04
1.04
1.05
1.04
1.01
1.01
1.07
1.03
1.03
1.03
1.03
1.03
1.03
1.04
1.04
1.04
1.07
1.07
1.13
1.06
1.06
1.07
Max.
4.01
4.01
3.42
1.23
1.23
1.14
1.06
1.06
1.05
3.20
3.20
1.72
4.01
4.01
3.42
4.82
4.82
3.62
3.12
1.78
3.12
3.22
2.06
3.22
3.62
2.00
3.62
4.82
4.82
2.45
Travel
Vehicle
Time Length Vol.
1.41
1.36
1.61
1.21
1.18
1.20
1.62
1.55
2.09
1.05
1.05
1.06
1.05
1.05
1.06
1.04
1.04
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.04
1.04
1.04
1.43
1.41
1.41
1.56
1.52
1.55
1.31
1.30
1.27
1.81
1.82
1.81
1.20
1.19
1.17
2.56
2.57
2.64
1.33
1.30
1.37
1.35
1.30
1.37
1.31
1.30
1.37
1.21
1.21
1.21
1.24
1.25
1.26
1.18
1.18
1.16
1.23
1.23
1.33
1.17
1.16
1.24
1.31
1.30
1.41
1.47
1.46
1.46
1.23
1.23
1.30
1.68
1.68
1.62
1.68
1.60
1.75
1.93
1.86
2.04
1.27
1.26
1.34
Reliability-TTRMax (NJTPA Method)
Time-Dependent Median
Average Weighted By
Person Vehicle- Person- Vehicle- Person- Bus Truck Direct
Vol.
Miles
Miles
Hours
Hours
Vol. Vol.
Calc.
1.61
1.46
1.46
1.55
1.56 1.67
1.43
1.21
1.23
1.23
1.29
1.30 1.29
1.18
1.19
2.09
1.71
1.71
1.83
1.83 2.05
1.67
1.13
1.06
1.05
1.05
1.05
1.05 1.06
1.06
1.06
1.05
1.05
1.05
1.05 1.06
1.06
1.03
1.05
1.04
1.04
1.04
1.04 1.05
1.05
1.02
1.05
1.05
1.05
1.05
1.05 1.05
1.05
1.05
1.05
1.05
1.05
1.05 1.05
1.05
1.04
1.04
1.04
1.04
1.04
1.04 1.04
1.04
1.03
1.41
1.46
1.46
1.51
1.51 1.43
1.33
1.55
1.62
1.62
1.68
1.68 1.54
1.48
1.49
1.27
1.31
1.30
1.33
1.33 1.27
1.25
1.16
1.81
1.83
1.84
1.88
1.89 1.98
1.70
1.17
1.22
1.22
1.26
1.26 1.20
1.15
1.42
2.64
2.58
2.58
2.57
2.57 2.61
2.62
1.42
1.37
1.31
1.31
1.38
1.38 1.31
1.31
1.36
1.32
1.31
1.42
1.42 1.25
1.31
1.19
1.37
1.31
1.31
1.34
1.34 1.37
1.31
1.20
1.21
1.24
1.24
1.24
1.24 1.19
1.19
1.26
1.26
1.26
1.26
1.26 1.24
1.24
1.22
1.16
1.21
1.21
1.23
1.22 1.14
1.14
1.19
1.33
1.25
1.25
1.26
1.26 1.31
1.31
1.24
1.18
1.18
1.19
1.19 1.22
1.22
1.18
1.41
1.32
1.32
1.34
1.34 1.39
1.40
1.24
1.46
1.44
1.44
1.47
1.47 1.46
1.48
1.29
1.26
1.26
1.27
1.26 1.26
1.26
1.26
1.62
1.61
1.61
1.63
1.64 1.66
1.69
1.58
1.75
1.67
1.66
1.84
1.83 1.47
1.63
2.03
1.94
1.93
2.09
2.09 1.62
1.89
1.41
1.34
1.27
1.27
1.31
1.31 1.27
1.32
1.21
Appendix B
Page 9 of 9
Appendix C
Downloading and Processing
Archived Travel Time Data
for Two Corridors
C-1
C-2
The following steps document the process that we undertook to download archived travel time data
from the I-95 Corridor Coalition’s Vehicle Probe Project Suite (VPP Suite).
1. Download from VPP Suite Massive Raw Data Downloader
a. Log into RITIS VPP Suite page
b. Select the Massive Raw Data Downloader
C-3
c. Select your road segments (in this case, the entire stretch of I-78 and I-78 express); date range,
days of week and hours of day (in this case, all times for all days in 2012); fields (we were using
travel time, so we didn’t need speed), averaging time (we downloaded both 5 minute—shown
here--and 1 hour data—shown in the next screen shot).
C-4
Two Issues with INRIX “Reference Speed” Attribute: The INRIX data set includes a “reference
speed” for each link. INRIX defines this attribute as “the calculated ‘free flow’ mean speed for the
roadway segment in miles per hour (capped at 65 miles per hour)…calculated based upon the
85th percentile point of the observed speeds on that segment for all time periods.” The
reference speed attribute is included for each link for each reporting time. At the outset of the
C-5
project, it was our intention to use the reference speed attribute to calculate a free-flow travel
time.
In examining the data, however, we noticed two issues. The first issue was that the value of the
reference speed was not constant over time. From the data tables, it appears as if INRIX
recalculated reference speeds on 11/14/12, resulting in slight increases or decreases (depending
on TMC link) for several TMC links. Our assumption, that the later reference speeds (after
11/14/12) are more “accurate,” since they are based on more data, was confirmed by Rick
Schuman at INRIX.
The second issue with using the INRIX reference speed was that we noted some instances where
the reported reference speed was lower than the calculated median speed for that TMC
(conversely, the “reference travel time” was higher than the calculated median travel time),
which should not be the case. As stated above, the reference speed is supposed to represent the
85th percentile speed, and thus should by definition be higher than the median speed. In some
cases it seemed to be a matter of rounding (the reference speed attribute is presented as an
integer), but in other cases the reference speed was much lower than the median speed. This
might be due to the use of more data in calculating the reference speed, but it still means that,
for those specific TMCs, using the median speed as a threshold for calculating delay would result
in more delay than that using the reference speed as a threshold, which is counter-intuitive.
The most extreme example of this issue is TMC 120N04417 (I-78 westbound near exit 58). The
reported INRIX reference speed for this link is 37 mph. However, the median speed for all hourly
data during 2012 on this link was 52 mph.
Because of these apparent discrepancies, we choose to use a free-flow travel time calculated
from the 15th percentile travel time (85th percentile speed). We calculated the 15th percentile
travel time using both the 1-hour and 5-minute data. For all TMCs on I-78, the 85th percentile
speed using the 5-minute data was higher (or exactly the same for two TMCs) than that from the
1-hour data. Moreover, in almost all cases (all but 5 for the 1-hour data and all but 3 for the 5minute data), the calculated 85th percentile speeds were higher than INRIX’s reference speed
(although some 85th percentile speeds were above INRIX’s 65 mph cap). For the example link
cited above (TMC 120N04417), the 15th percentile travel time (85th percentile speed) equates to
56 mph (compared to the 37 mph “reference speed”).
2. Retrieve CSV files from VPP Suite
3. Unzip into two files (Readings.csv, TMC Identifiation.csv)
C-6
4. Import CSV files into Access
a. Create new “RAW” database (filenames: RAW_I78_2012_5min.accdb,
RAW_I78_2012_1hr.accdb, RAW_NJ18_2012_5min.accdb, and RAW_NJ18_2012_1hr.accdb)
b. Import Readings.CSV file into the new “RAW” Access database
i. On the “External Data” ribbon, in the “Import & Link” group, click on “Text File”.
ii. ”Browse” to the appropriate CSV file and choose to “Import”.
C-7
iii. Select “Delimited”.
iv. Choose “Comma” delimited, the “First Row Contains Field Names” option. On subsequent
screens, note the data type options used, to minimize the database size, and no fields are
indexed at this point.
C-8
v. tmc_code: text
vi. measurement_tstamp: has to be imported as a text field (Date/Time does not work!)
C-9
vii. average_speed: integer is sufficient
viii. reference_speed: even though it is shown with decimal points, it appears as if they have
been rounded to the nearest integer in all cases.
C-10
ix. ”travel_time_minutes”: single is sufficient
x. confidence_score: again, it appears as if they’ve all been rounded to integers
C-11
xi. Because this is just the “raw” database, no indexes or keys are needed.
xii. Give the table an appropriate name.
C-12
xiii. Saving the import is not necessary, but can make it easier if you need to repeat the import
for some reason.
c. Convert the “RAW” database into “DB” database
i. Create a new blank database (named “DB” instead of “RAW”)
C-13
ii. Create a new table and save it with an appropriate name.
C-14
iii. Add fields as shown below.
1. tmc_code: type=text, size=9
2. average_speed: type=integer
C-15
3. travel_time_minutes: type=single
4. confidence_score: type=integer
C-16
5. Date_Time: type=Date/Time
iv. Select both tmc_code and Date_Time fields and click “Primary Key”
C-17
v. On the “External Data” ribbon, in the “Import & Link” group, click on “Access”
vi. ”Browse” to the RAW database file, and choose to “Link”.
C-18
vii. Select the RAW table and click “OK”
viii. Create a new query (used to copy records from the “RAW” table into the new, properly
formatted table)
1. In the Create ribbon, select “Query Design”, and add the “RAW” table to the design.
C-19
2. Make the query an “Append” action query, and select the table created above.
3. Set up the query as shown below (appending the fields “tmc_code”,
“average_apeed”, “travel_time_minutes”, and “confidence_score” into the samenamed fields in the destination table, and creating a date/time field out of the
“measurement_tstamp” field using the equation
C-20
“CDate(Left([measurement_tstamp],19))”
4. Save the query.
C-21
5. Run the query
6. Note that there will be “key violations” because of the primary key that was set up
(on the “tmc_code” and “Date_Time” fields) This is because of the return from
daylight savings time to standard time, where there are technically two times that
“2:00AM” (and “2:05AM” and so on) occurs. The second occurrence will simply be
ignored.
5. Repeat above steps (importing “RAW” table, converting to “DB” table) for the hourly data, and for
other corridor(s).
6. Compact your database if you delete a large table, to avoid hitting the 2Gig file size limit. (Note that
it is good practice to do the “Compact & Repair” step often, particularly when handling large
C-22
databases).
7. Create a new blank database (we called it “MasterDatabase.accdb”) and link each of your tables
from your “DB” databases. As a practice (in order to keep file sizes below the 2GB file size), this
“MasterDatabase” contains mostly links to tables stored in “child” database files, along with
queries to process those tables. When queries took a long time to process, we made them “make
table” queries and stored the resultant table either in the MasterDatabase file (when it was a
limited number of records) or in a “child” database file (when it contained many records). Some
queries (particularly those that calculated percentile values) took several hours (even up to days) to
run.
8. Import TMC Identification.csv file, using similar steps as above. Remember to limit the size of the
fields to keep file sizes down: “tmc” should be 9 characters, “state” should be 2, “zip” should be 5,
road_order should be “long integer”, and the lat/long and miles can be single or double precision.
We examined the remaining text fields and used appropriate number of characters: “road” was 25,
“direction” was 20, “intersection” was 50, and “county” was 20 characters.
9. In order to calculate percentile values in Access, you need to add a custom function. We
customized a “database” percentile function found on-line as follows:
Function DPercentile(PV As Single, expr As String, domain As String, Optional criteria As
String) As Double
'Uses linear interpolation method for percentile values.
Dim dbs As Database
Dim rst As Recordset
Dim lowIndex As Long
Dim numberOfRecords As Long
Set dbs = CurrentDb
If Len(criteria) <> 0 Then
C-23
_
Set rst = dbs.OpenRecordset("select " & expr & " from " & domain & " where " & criteria &
" order by " & expr)
Else
Set rst = dbs.OpenRecordset("select " & expr & " from " & domain & _
" order by " & expr)
'Make sure the spaces inside the quotes are preserved, otherwise your SQL will
'not be syntactically correct and Access will complain!
End If
If rst.BOF Then
numberOfRecords = 0
Else
rst.MoveLast
numberOfRecords = rst.RecordCount
'You need the Movelast to get the correct record count out of a recordset
End If
If numberOfRecords = 0 Then
DPercentile = 0
'We assume that the percentile is 0 when the number of records is zero
ElseIf numberOfRecords = 1 Then
DPercentile = rst(expr)
'If the number of records is 1, the value of the expression in that record is the percentile
Else
trueIndex = (PV / 100 * numberOfRecords) + 0.5
lowIndex = Int(trueIndex)
'lowIndex now points to the position below (or at) the correct percentile value.
rst.MoveFirst
rst.Move (lowIndex - 1)
DPercentile = rst(expr)
'if the number of records is odd, we are done
If lowIndex <> trueIndex Then
'the percentile value doesn't fall exactly on a record
rst.MoveNext
DPercentile = DPercentile + (trueIndex - lowIndex) * (rst(expr) - DPercentile)
'Do the linear interpolation between the two values.
End If
End If
rst.Close
Set rst = Nothing
dbs.Close
Set dbs = Nothing
'Cleanup everything before leaving the function
End Function
10.
We also created custom Maximum and Minimum functions as follows:
Public Function iMax(ParamArray p()) As Variant
' Idea from Trevor Best in Usenet MessageID
[email protected]
C-24
Dim i As Long
Dim v As Variant
v = p(LBound(p))
For i = LBound(p) + 1 To UBound(p)
If v < p(i) Then
v = p(i)
End If
Next
iMax = v
End Function
Public Function iMin(ParamArray p()) As Variant
' Idea from Trevor Best in Usenet MessageID
[email protected]
Dim i As Long
Dim v As Variant
v = p(LBound(p))
For i = LBound(p) + 1 To UBound(p)
If v > p(i) Then
v = p(i)
End If
Next
iMin = v
End Function
11. As an example of how we used the “DPercentile” function, below is a view of the design grid for
a query that calculated 80th, 50th, and 15th percentile values for each tmc, for each five-minute time
interval, by day of week (the TT50 and TT15 fields are calculated similar to the TT80 field, replacing
“80” with “50” or “15”).
C-25
Appendix D
Conflation
for Two Corridors
D-1
D-2
To calculate Annual Hours of Delay (AHD) it was necessary to acquire travel time data and integrate it
with corresponding volume data for the two travel corridors analyzed in this study. Travel time data and
volume data were available from two different datasets which were not completely in sync with each
other on the basis of feature representation, positional accuracy and feature segmentation. The primary
objective of this approach is to integrate these two different datasets so that the travel time data of a
road segment can be accurately associated with the corresponding volume data.
Data: The datasets required for this approach are as follows:
1. NJDOT Congestion Management System’s (CMS) road network: NJDOT’s CMS provides traffic
volume data of road segments. The system provides information such as morning peak period
traffic volume, evening peak period traffic volume, and 24 hour traffic volume, all of which was
used in this study. The system provides direction specific data, such as eastbound or westbound
and northbound or southbound. The data is associated with a GIS shapefile where road
segments are represented in the form of lines. These lines are also known as ‘centerlines’ and
represent the bi-directional traffic flow. They are identified on the basis of State Route
Identifiers (SRIs), beginning and ending mile posts and CMS number.
2. Vehicle Probe Project (VPP) Suite data: University of Maryland’s CATT Lab provides vehicular
travel time and vehicular travel speed data of the road segments through the VPP suite. This
dataset is also associated with a GIS shapefile in which the road segments are represented in the
form of lines. These lines are known as TMC links/paths. However, they represent the unidirectional flow of traffic. Hence more than one line represents one highway such as I-78 is
represented by I-78 East and I-78 West. In this dataset there are also separate lines for local lane
and express lane traffic. The TMC links are identified by codes that are very different from the
CMS numbers used to identify road segments in NJDOT’s CMS. The TMC links/paths also do not
have SRIs and mileposts associated with them.
3. NJDOT’s Straight Line Diagrams (SLD): According to NJDOT, SLD’s are a method of viewing the
road segments as lines. They are also associated with a GIS shapefile. In this case, again, there
are multiple lines representing one highway such as I-78 is represented by I-78 East and I-78
West. The SLD’s also have SRIs and beginning and ending mileposts associated with them. The
SLD data were used in this approach because it has common characteristics of both the above
mentioned datasets and also because it is a non-segmented data layer.
Study Area: In this study, two highways of northern New Jersey have been taken into
consideration—one interstate (I-78) and one arterial (NJ 18). Traffic volume and vehicular travel
time of both the directions of the highways were taken into consideration. However, since CMS
does not differentiate between the traffic volumes of the express lanes from the local lanes, the
express lanes were excluded from the TMC dataset.
Problems of integrating TMC links with CMS links: The task of integrating the data associated with
the TMC links with the data associated with the CMS links was not simple because of the following
reasons:
D-3

Feature representation: One CMS link represents bi-directional traffic flow. For instance, one
CMS link represents both I-78 East and I-78 West (Figure 1). On the other hand, one TMC link
represents uni-directional traffic flow. For instance, one TMC link represents I-78 East and
another one represents I-78 West (Figure 1). The CMS links are identified by CMS number, SRIs
and mileposts whereas TMC links are identified by TMC codes which are different from the CMS
numbers.
Figure 1: Differences between the TMC links and the CMS links


Positional accuracy: None of the CMS links overlaps the TMC links. If precisely positioned, I-78
West TMC link should overlap I-78 CMS link.
Segmentation: The CMS links and the TMC links are segmented differently. Hence, the length of
the CMS links are not equal to the TMC links. The CMS links are usually longer than the TMC
links and hence there are multiple TMC links corresponding to one CMS link which arises the
problem of joining one traffic volume data to multiple travel time data.
The Suggested Approach: On the basis of an extensive literature review, an approach to accurately
integrate the two different datasets is suggested in the following narration. It should be mentioned that
none of the methodologies suggested in the literature were applied as is to integrate the two datasets.
Instead, appropriate sections of the methodologies were selected, arranged and applied. It also requires
mention that the use of a third dataset—SLD—is unique in this approach. As mentioned before, the SLDs
were used because they share common characteristics with the TMC links and the CMS links and also
they are non-segmented.
D-4
Step 1: Provide SRI and Mile post values to the TMC links
As mentioned before, while the CMS links are primarily identified by SRI and mile posts, the TMC links
are identified by codes. In order to have a common character/attribute between the two data sets, to
facilitate the process of integration, it was decided to provide the TMC links with SRIs and mile post
values through the SLDs. The SLDs were used for the purpose for two reasons. First, like the TMC links,
the SLDs represent the uni - directional flow of traffic such as I-78 is represented by I-78 East and I-78
West. Second, the SLDs are not segmented. For instance, NJ 18 North is represented by a line which
begins at mile post 5.14 and ends at mile post 45.30. As a result, the problem of two datasets being
segmented differently does not exist. This step has several sub-steps as mentioned below:
Step 1a: Convert TMC links into point features
The TMC links that are represented by line features in ArcGIS are converted to point features by using
the tool ‘Feature Vertices to points’ in ArcGIS 10.0 (ArcToolbox>Data Management
Tools>Features>Feature Vertices To Points). For the purpose of simplicity, I-78 East and West and NJ 18
North and South were processed separately. The process was repeated four times and the final product
was four separate point feature files as shown in the figure 2.
End product: 4 point
feature shapefiles
Figure 2: Final product of Step 1a.
Points to Remember:
1. As a pre-requisite of step 1a, it is necessary to isolate the links representing the different lanes
of a roadway. For instance, in this case, four shapefiles were created representing I-78 West, I78 East, NJ 18 North and NJ 18 South.
2. While converting the line features to points features it is necessary to insert the parameters as
shown in the snapshot below:
D-5
Remember: Point Type
is BOTH_ENDS
3. The products will be saved in a geodatabase and hence the file name should not contain any
space or characters.
Step 1b: Snap the point features to the respective SLD line features
The point features created from the TMC links did not overlap the corresponding SLD line features. For
instance, the point features created from NJ 18 North did not overlap the NJ 18 North SLD line feature.
Hence, it was necessary to snap the point features to the corresponding SLD line feature. This task was
completed with ‘Snap points to the lines’ tool in Hawths Toolbox—an add-in for ArcGIS 10. In this step
as well, for the purpose of simplicity, the four point features and the corresponding line features were
processed separately. Hence the process was repeated four times and as the final product there were
four output features. However, after this step, the four output files were merged to create one file for
use in the later steps.
Step 1c: Split the SLDs on the basis of the point features created from the TMC links
The point features representing the extent of the TMC links were then used to split the non-segmented
SLDs with the tool ‘Split Line at Points’ in ArcGIS 10.0 (ArcToolbox>Data Management
Tools>Features>Split Line at points). As a result, SLD links similar in extent to the TMC links were created
from the non-segmented SLDs and these links had SRIs and mile posts associated with them. However,
the mile post values were incorrect. Hence, in a later step correct mile post values were calculated for
each link created by splitting the SLDs.
Points to remember:
1. Among the parameters to be included in Step 1b, the one to be most careful about is ‘Search
tolerance distance’ which is determined on the basis of the largest distance between the point
features, created from TMC links, and the SLD line feature. For instance, if the largest distance
between the point features (TMC) and the line feature (SLD) is 73 feet, then the search radius
can be 73 feet or can be rounded to 75 feet. After Step 1b, it is not necessary to be careful about
search radius/search tolerance. Using a minimal search radius/search tolerance is sufficient for
accomplishing the later steps.
D-6
2. In Step 1c, after one SLD is split into multiple segments, it is necessary to compare the number
of SLD links to the number of TMC links of the respective roadway. The number of SLD links
should be equal to the number of TMC links of the respective roadway.
3. The final products of Step 1b and 1c will be saved in a geodatabase and hence the file name
should not contain any space or character.
Step 1d: Spatially join selected attributes of the split SLD links and the TMC links
Before recalculating the mile post values, we joined the TMC codes of the TMC links to the SRIs and mile
post values of the split SLD links. This was done by spatially joining the TMC links, with the TMC codes in
their attribute table, to the split SLD links with the SRI and milepost values in their attribute table,
through the ‘Spatial Join’ tool of ArcGIS 10.0 (ArcToolbox>Analysis Tools>Overlay>Spatial Join). In the
attribute table of the final product (also called ‘Split SLD links’ henceforth) there were few duplicate
TMC codes which were manually identified and corrected. The duplication took place as some of the
very small split SLD links were not joined to the corresponding TMC links.
Step 1e: Provide correct mile post values to the split SLD links
In this step, the beginning and ending mile posts were recalculated for each split SLD link through the
tool ‘Locate Features Along Routes’ in ArcGIS 10.0 (ArcToolbox>Linear Referencing Tool>Locate features
along routes). In this step, the reference layer was the original non-segmented SLD link that was split on
the basis of the extent of the TMC links. The parameters included in this step are shown in figure 3,
along with some important notes to be remembered:
Split SLD links with
TMC codes
Original nonsegmented SLD link
file
Put a very minimal
search radius because
location of the files
are the same.
Figure 3: Parameters included while conducting ‘Locate features along routes’
D-7
The final product of this step was a table as shown in Figure 4. The table had the TMC codes, SRIs and
the correct mile post values of all the split SLD links.
Recalculated beginning and
ending milepost values
SRI
TMC Codes
Figure 4: Final product of Step 1e.
Step 2: Join the CMS links and the associated volume data to the split SLD links
In this step the traffic volume data of the CMS links, primarily identified by SRIs and beginning and
ending mile posts, were precisely joined to the split SLD links with TMC codes and correct SRIs and mile
posts. This was not a simple join because the length of the CMS links were different from the split SLD
links. Hence the traffic volume of one CMS link was attributable to multiple split SLD links.
For convenience, this step was conducted in MS Access with the help of SQL queries. As a pre-requisite
to this step it was necessary to create an Access database and link or import the attribute table of the
CMS links (containing SRIs, mile post values and traffic volume data) and the split SLD links (with SRIs,
correct milepost values and TMC codes).
Step 2a: Joining the CMS links to the corresponding split SLD links
In this step, the CMS links were joined to the corresponding split SLD links. Since the CMS links were not
of the same the length as the split SLD links, the following three relationships were observed between
them: Extension, Containment and Partial Overlap1. The relationships are clearly depicted in figure 5.
1
Safra et.al. 2006. Efficient Integration of Road Maps.
D-8
Figure 5: Three types of relationship between CMS links and split SLD links
To capture the above relationships the following queries were conducted:


To capture the first and second relationships, only those split SLD links were joined to the CMS
links whose beginning (MP_Start2) and ending (MP_End2) mile posts were between the CMS
link’s beginning (BEGIN_MP) and ending (END_MP) mileposts.
To capture the third relationship, only those split SLD links were joined to the CMS links who’s
either beginning milepost (BEGIN_MP) or ending milepost (END_MP) was between the CMS
link’s beginning (MP_Start2) and ending milepost (MP_End2).
After assigning the CMS links to the corresponding split SLD links which led to further fragmentation of
the split SLD links, it was necessary to assign the correct beginning and ending mileposts to the links. For
the purpose the queries shown in Figure 7 were used (queries in the example are for I-78East):
D-9


Start: IIf([BEGIN_MP]>[MP_START2],[BEGIN_MP],[MP_START2])
End: IIf([MP_END2]>[END_MP],[MP_END2],[END_MP])
Figure 7: Queries for calculating correct beginning and ending mileposts for the segments created after Step 2a.
Step 2b: Apportionment of traffic volumes associated with the CMS links to the split SLD links
As noticed in the third relationship of Step 2a, one split SLD link can be associated with multiple CMS
links. Hence, in this step, the traffic volumes of multiple CMS links were accurately allotted to one split
SLD link. The volumes of the CMS links were weighted by the length of the CMS links within the
corresponding split SLD link. Then the weighted volumes were summed up to provide one traffic volume
value to one split SLD link. The queries for the purpose are shown in Figure 8.
1.
2.
3.
D-10
Morning Volume or Vol_AM: Sum(([ABVOLAM]*([Final_End_MP]-[Final_Start_MP])/([MP_End2]-[MP_Start2])))
Evening Volume or Vol_PM: Sum(([ABVOLPM]*([Final_End_MP]-[Final_Start_MP])/([MP_End2]-[MP_Start2])))
Off-peak Volume: Sum(([ABVOL24]-[ABVOLAM]-[ABVOLPM]*([Final_End_MP]-[Final_Start_MP])/([MP_End2]-[MP_Start2])))
1
2
3
Figure 8: Queries for accurately apportioning the traffic volume data of multiple CMS links to one split SLD link.
The calculated volumes can finally be joined to the attribute table of the final product of Step1
if necessary.
D-11
Appendix E
Weigh-in-Motion Data Processing
for Two Corridors
E-1
E-2
In the pilot study, truck volume data were required for calculation of Annual Hours of Truck Delay
(AHTD) and Truck Reliability Index (TRI). Whereas, private bus volume data, later converted to private
bus passengers through the application of a loading factor, was required to precisely calculate Annual
Hours of Delay (AHD) and Reliability Index (RI80). These datasets were extracted from the data points
provided by Weigh-in-Motion (WIM) sites located on I-78 and NJ 18. From the WIM site data points it
was possible to extract truck and private bus volume data at the sub - corridor level by hour of the day
and day of the week.
Background: The WIM sites provide continuous traffic count data as a part of New Jersey Department of
Transportation’s (NJDOT) Traffic Monitoring System Program. In New Jersey, there are 90 permanent
Weigh-in-Motion sites, among which seven lies on I-78 and NJ 18. Hence, data from those seven sites
were used in this study. The locations of the seven WIM sites are as follows:







I-78 A in Union Township
I-78 D in Readington
I-78 W in Watchung Borough
NJ 18 in Marlboro
NJ 18 B in Colts Neck
NJ 18 C in New Brunswick
NJ 18 D in Piscataway
These WIM sites were the only sources for truck volume data. However, bus volume data were also
collected from general transit feed specifications (GTFS) feeds. The location of WIM sites in non-NJ
TRANSIT routes lead to the assumption that the bus volume data extracted from the WIM sites were
primarily private bus and university bus volumes. Hence this data when added to the GTFS data, which
provided the NJ TRANSIT bus volumes, provided a holistic view of bus volume along the study corridors.
Software Resources: A data analysis software called ‘iANALYZE’ was used to view and convert the WIM
sites data to excel file format. For this study, iANALYZE, processed traffic data files from 1060 series and
iSINC data gathering system.
Step-by-Step Process:
Step 1: Specify some important parameters
before running the software for viewing and
extracting data:


Select Classification Scheme: The vehicle
classification scheme used for this study is
‘FHWA_DOS’, Scheme Type is ‘DOS’ for
1060 series and ‘Unix/iSINC’ for ‘iSINC’
data and Units is ‘Pounds/Inches’
Site Editor: Identify the folder where the
raw data is being uploaded, add ‘-5’ hours to Vehicle Timestamps for iSINC data and ‘0’ hours to
E-3
Vehicle Timestamp for 1060 series data and identify the data source as ‘1060’ or ‘iSINC’ as is
applicable.
Step 2: Specify the filters used for extracting data for this study:


E-4
WIM Sites: such as Piscataway 18 D
Date and Time: such as Jan 01, 2012 00:00:00 to Jan 01, 2013 00:00:00



Vehicle Class such as 4 (Buses) to 13 (Seven or More Axle Multi-Trailer Trucks)
Direction such as northbound, southbound, eastbound and westbound
Time interval, such as Daily and by hour
E-5
Step 3: The data for each site were extracted in Excel format as shown in the snapshot below.
The data was formatted and exported to MS Access.
E-6
Step 4: Each WIM site data were associated with all the TMC links that lie within the sub – corridor
containing the WIM site. There were sub-corridors that contained multiple WIM sites. In that case, the
average of the WIM site data were considered as the truck and private bus volume of the sub-corridor
and the associated TMC links.
Final Product: The final product was a MS access file as shown below with the average number of
private buses and trucks by hour of the day and day of the week for each TMC link along I-78 and NJ 18.
E-7
E-8
Appendix F
NJ TRANSIT General Transit Feed Specification
(GTFS) Processing
for Two Corridors
F-1
F-2
In order to get an estimate of the number of transit (bus) passengers on each TMC segment, by hour of
day and day of week, we first needed to get the number of buses traveling on each segment each hour.
To get that, we turned to the general transit feed specification (GTFS) files published by NJ TRANSIT.
These files specify the (current) service pattern for NJ TRANSIT buses. While not entirely reflective of the
service that was provided throughout 2012, this is a decent surrogate, and does provide a proof of
concept for use of the GTFS files as part of performance measurement.
We downloaded GTFS files for this project from the NJ TRANSIT server on November 15, 2013. They
were downloaded as a compressed (zip) file (“bus_data.zip”) comprising seven distinct tables, as per
GTFS specifications:
Contents1
Table/File Name
Agency.txt
Calendar_dates.txt
Information about the transit agency(ies)
Dates associated with service patterns
Routes.txt
All unique bus routes/patterns. “A route is a group
of trips that are displayed to riders as a single
service.”
Lists sequence of latitude/longitude for bus
routes/trips
Shapes.txt
Stop_times.txt
Stops.txt
Trips.txt
Comments
Not used in this analysis
Used to identify dates for
“typical” weekday,
Saturday, and Sunday
service
Combined with shapes.txt
using ArcGIS tool “Display
GTFS Route Shapes”
Combined with routes.txt
using ArcGIS tool “Display
GTFS Route Shapes”
Arrival and departure times for each stop on each
trip
Individual stop locations
“Trips for each route. A trip is a sequence of two or
more stops that occurs at specific time.”
The first step in the process was to create an ArcGIS layer (“feature class”) of the GTFS data. To
accomplish this, we downloaded the “Display GTFS Route Shapes” tool2. This tool is an ArcGIS “toolbox,”
and has a simple interface, asking for the name of the input GTFS directory and the output feature class,
as follows:
1
Quotations are from General Transit Feed Specification Reference, Revised June 20, 2012
(https://developers.google.com/transit/gtfs/reference)
2
http://www.transit.melindamorang.com/analysis_DisplayGTFSRouteShapes.html, created by Melinda Morang at ESRI
F-3
This created a shape file containing a shape for each unique bus route alignment in the GTFS file. As
shown in the sample of the resulting shape attribute table below, the file has a record (shape) for each
unique combination of GTFS “shape” and GTFS “route”. Note that, while the “shape_id” and “route_id”
fields are somewhat arbitrary identifiers, the “rt_shrt_nm” field contains the NJ TRANSIT route number.
F-4
We converted the stops.txt file into an ArcGIS point shape file using ArcMap’s “Add XY Data”.
We then selected the routes and stops from the resultant shape files (named “NJTRANSITBusRoutes”
and “NJTRANSITBusStops”) where they traveled on either I78 or NJ18, creating “I78BusRoutes,”
“I78BusStops,” “NJ18BusRoutes,” and “NJ18BusStops.” We performed this selection step manually, but
it could easily be automated using ArcMap’s overlay tools.
The next step was to divide each roadway (I-78 and NJ 18) into “sections” divided by where buses enter
or leave the roadway. We did this by visual inspection, but some automation may be possible by using
GIS overlay techniques. Each bus route was tagged with the sections of roadway that it traveled on. The
“sections” used for this pilot project are presented in the following table and depicted on the following
maps.
F-5
Roadway
Section #
I-78
1
I-78
2
I-78
Intersecting Roads/Interchanges
(from and to)
Mileposts
(approx.)
Bus Routes
Garden State Parkway to Rts
1&9/21/22
Rts 1&9/21/22 to Port Newark
Interchange
53.4 – 57.4
113/114 Express, 117
57.4 – 58.3
3
Port Newark Interchange to I-95/NJ
Tpk
58.3 – 58.9
I-78
I-78
4
5
58.9 – 62.0
62.0 – 65.2
I-78
I-78
I-78
6
7
8
65.2 – 66.8
66.8 – 67.0
67.0 – 67.8
120
64 inbound, 120
120
NJ 18
NJ 18
1
2
31.8 – 33.6
33.6 – 34.4
138 (Hillsdale)
138 (Hillsdale), 818
NJ 18
3
34.4 – 35.6
68, 138 (Hillsdale), 818
NJ 18
4
I-95/NJ Tpk to Interchange 14A
Interchange 14A to Columbus Dr.
Exit
Columbus Dr. Exit to Jersey Ave
Jersey Ave to Rt 637/Henderson St
Rt 637/Henderson St to Holland
Tunnel/NYS Line
Ferry Rd to Southwood Dr
Southwood Dr to Rt 516/Old Bridge
Matawan Rd
Rt 516/Old Bridge Matawan Rd to Rt
617/Rues Ln
Rt 617/Rues Ln to Racetrack Rd
113/114 Express,
113/114 Local
(inbound), 117
107 Local, 113/114
Express, 113/114 Local
(inbound & outbound),
117
63, 64, 68
63, 64, 68, 81, 120
35.6 – 36.2
NJ 18
5
Racetrack Rd to Ferris St
36.2 – 37.9
NJ 18
6
Ferris St to I-95/NJ Tpk
37.9 – 39.6
NJ 18
7
I-95/NJ Tpk to Rt 172/George St
39.6 – 41.4
68, 138 (Hillsdale), 138
(Spotswood), 811, 818
68, 138 (Hillsdale), 138
(Spotswood), 818
68, 138 (Hillsdale), 138
(Spotswood), 815, 818
815, 818
7
8
6
1
2
3
4
F-6
5
7
6
5
4
3
2
1
For the next step, we examined each bus route and identified a “key stop” that could be used to
reference for determining when buses would be traveling on the subject highways. That stop could be
either before entering the highway, on the highway, or after exiting the highway. We did this by looking
at the stops for each route near or within the subject highways. In addition, we estimated how much
time (in minutes) it would take for the bus to arrive at each of the sections from that route’s “key stop”
(“offsets”). We used Google Maps to help with these estimates. Those offsets could be either positive (if
the key stop was before the bus traveled on the highway section), or negative (if the key stop was after
the bus traveled on the highway section).
For this pilot, we operationalized the above approach by adding fields to the GIS layer attribute tables
(I78BusRoutes and NJ18BusRoutes), called “SECTn,” “OFFSETn,” and “KEYSTOP_ID” (where “n” was 1
through the number of sections for that particular roadway).
F-7
While this approach does not follow database normalization rules, we found it easier to enter the
information into the database that way. We created subsequent Access queries to create normalized
tables, as discussed below.
The next step was to identify which TMCs were included in each of the highway sections. This had to be
a manual process because the TMC endpoints did not always coincide with the section endpoints, so we
had to use make some judgment calls to approximate the sections using the TMCs. In addition to the
“Section” identifier, a “Direction_ID” field was added (0 for East/Northbound and 1 for
West/Southbound). This yielded a table as shown below.
F-8
We next had to deal with the issue of which day(s) of the week to use. As noted above, the
“calendar_dates” file in the GTFS is used to identify which routes operate on which dates. NJ TRANSIT
bus services operate on a variety of different schedules. For example, some routes treat some holidays
like a Saturday, some like a Sunday, and some offer slightly different service on a holiday than any other
day. The GTFS file used during this pilot effort included service for November 7, 2013 through May 5,
2014, and included 30 different “Service_ID” patterns. To simplify matters, we decided to use the
service on Tuesday November 19, 2013 as a typical weekday, that on November 17 as a typical Sunday,
and November 23 as a typical Saturday. We performed this step using an Access query as follows.
F-9
SELECT Service_id,
Switch([date]=20131117,"Sunday",[date]=20131119,"Weekday",
[date]=20131123, "Saturday") AS DayType FROM
Calendar_dates
WHERE (((Switch([date]=20131117,"Sunday",
[date]=20131119,"Weekday",[date]=20131123,"Saturday")) Is
Not Null));
As discussed above, although the data on sections and offsets for each bus route were entered in a nonnormalized fashion for ease of data entry, we needed to normalize this data for use in subsequent
queries. We did this using a “union query” in Access, using the following SQL.
SELECT RTSHPNAME, IIf([SECT1],1,Null)
Where [SECT1] UNION ALL
Where [SECT8];
AS [Section], OFFSET1 AS Offset from I78BusRoutes
The “Display GTFS Route Shapes” tool created fields for “Shape_ID” and “Route_ID” fields in the GIS
layer tables (e.g., I78BusRoutes) as text fields, while the same fields in the “Trips” GTFS file were
imported into Access as numbers. In order to be able to have Access join these tables, we needed to
create a query that converted those fields back to numbers, as shown below.
F-10
SELECT RT_SHRT_NM, RTSHPNAME,
Val([SHAPE_ID]) AS Shape_ID2,
Val([ROUTE_ID]) AS Route_ID2,
KEYSTOP_ID FROM I78BusRoutes;
Now, we’re all set up to create a query that joins data from the Trips, Stops, and Stop_times tables along
with the queries discussed above (qryServiceDayType to get routes for typical
weekday/Saturday/Sunday, qryI78BusRouteSections for sections and offsets, and qryI78BusRoutes for
the key stop) to obtain the hour of day that each bus travels on each section of roadway.
The “KeyTime” field just converts the arrival_time field from the Stop_times table into a time field (note
that the arrival_time field can have values above 23:59 to indicate that the time is on the following day).
The “SectHr” field then adds the offset to the KeyTime and extracts just the hour of day.
F-11
SELECT qryI78BusRoutes.RTSHPNAME, qryI78BusRoutes.RT_SHRT_NM, Trips.trip_id, Trips.service_id,
Trips.trip_headsign, Trips.direction_id, qryI78BusRoutes.KEYSTOP_ID, Stops.stop_name,
Stop_times.arrival_time, qryI78BusRouteSections.Section,
TimeSerial(IIf(Left([arrival_time],2)>"23",Val(Left([arrival_time],2))24,Left([arrival_time],2)),Mid([arrival_time],4,2),Right([arrival_time],2)) AS KeyTime,
qryServiceDayType.DayType, IIf([Section],Hour(DateAdd("n",[Offset],[KeyTime])),Null) AS SectHr
FROM ((((qryI78BusRoutes INNER JOIN Trips ON (qryI78BusRoutes.Shape_ID2 = Trips.shape_id) AND
(qryI78BusRoutes.Route_ID2 = Trips.route_id)) INNER JOIN Stop_times ON
(qryI78BusRoutes.KEYSTOP_ID = Stop_times.stop_id) AND (Trips.trip_id = Stop_times.trip_id))
INNER JOIN Stops ON Stop_times.stop_id = Stops.stop_id) INNER JOIN qryServiceDayType ON
Trips.service_id = qryServiceDayType.service_id) INNER JOIN qryI78BusRouteSections ON
qryI78BusRoutes.RTSHPNAME = qryI78BusRouteSections.RTSHPNAME
ORDER BY qryI78BusRoutes.RT_SHRT_NM, Trips.trip_id, Trips.direction_id;
This results in a query that lists the hour that each bus is traveling over each highway section. There is
one record for each bus on each section. We can now get a count of the number of buses on each
section in each hour of each DayType by using a totals query.
SELECT DayType, Section,
direction_id, SectHr,
Count(RTSHPNAME) AS BusCount
FROM qryI78BusTripsSectHours
GROUP BY DayType, Section,
direction_id, SectHr;
We can then expand this out into all seven days of the week by using the following Union query
(qryI78BusCountBySectionDirectionHour_Days):
F-12
SELECT Section, direction_id, 1 AS DayOfWeek, SectHr AS HourOfDay, BusCount FROM
qryI78BusCountBySectionDirectionHour WHERE DayType="Sunday" UNION ALL
SELECT Section, direction_id, 2 AS DayOfWeek, SectHr, BusCount FROM
qryI78BusCountBySectionDirectionHour WHERE DayType="Weekday" UNION ALL
qryI78BusCountBySectionDirectionHour WHERE DayType="Saturday";
The final step in the process involves joining the table of TMCs with section numbers to the query
totaling the number of buses by day of week and hour of day.
SELECT tmc, tblI78TMCSections.Direction_id,
DayOfWeek, HourOfDay, BusCount INTO
tblI78TMCBusCount_Days FROM tblI78TMCSections INNER
JOIN qryI78BusCountBySectionDirectionHour_Days ON
(tblI78TMCSections.Direction_id =
qryI78BusCountBySectionDirectionHour_Days.direction
_id) AND (tblI78TMCSections.Section =
qryI78BusCountBySectionDirectionHour_Days.Section);
Note that this last query creates a table (tblI78TMCBusCount_Days). We then used this table in the
calculation of performance measures.
F-13
Appendix G
Plan4Safety Data Processing
for Two Corridors
G-1
G-2
In this study, vehicle occupancy data—number of people traveling in a vehicle—were collected from a
statewide accident/crash database and were then used to calculate the average vehicle occupancy
(AVO) rate at the sub-corridor level for I-78 and NJ 18. Federal Highway Administration in one of its
reports entitled ‘Improved Vehicle Occupancy Data Collection Methods’1 has identified the above
mentioned method as one of the most valid and low cost methods of collecting AVO data and
calculating AVO rate. The crash records often provide information about the number of people traveling
in vehicles involved in a crash. Assuming that vehicles involved in crashes are representative of all
vehicles in the region, the vehicle occupancy numbers were used to calculate the AVO rate along a
facility and/or region.
Data:
New Jersey has an extensive crash database known as Plan4Safety developed and maintained by Center
for Advanced Infrastructure and Transportation, Rutgers University on behalf of New Jersey Department
of Transportation. This database provides information about a crash through multiple tables. The
‘crashes table’ provides location information and the ‘occupants table’ provides traveler information
involved in the crash. Hence, these two tables were used in this study for collection of vehicle occupancy
data and calculation of AVO rate.
Study Area and Time period:
The focus of this study is on I-78 and NJ 18 and hence from the crash database, crashes occurring along
these two facilities were extracted on the basis of SRI. The mile post values were used to subdivide the
data to the sub-corridor level.
From the crash database, crashes of 2008, 2009 and 2010 were collected and averaged by the number
of years to calculate AVO rate of each sub-corridor for I-78 and NJ 18.
1Heidtman,K.,
Skarpness, B., Tornow C. 1997. Improved Vehicle Occupancy Data Collection Methods. Office of Highway
Information Management; Federal Highway Administration. Washington D.C.
G-3
Step-by-Step Process:
Step 1: The ‘crashes’ table and ‘occupants’ table was downloaded from the Plan4Safety website on the
basis of crash year and SRI as shown in Figure 1.
A
B
C
Select year and SRI as
the filter
D
Name and save the filter
E
Figure 1: Steps for downloading crash data from Plan4Safety website
G-4
Step 2: All the crashes tables and occupants tables were imported to one access database as shown in
figure 2.
Figure 2: All the tables imported to one access database.
Step 3: With the help of SQL queries, the crash table was linked to the occupant table on the
basis of ID number. The maximum number of vehicles and occupants involved in each crash
was extracted as shown in figure 3.
Figure 3: SQL queries
Step 4: The maximum number of vehicles and the maximum number of occupants were
summed at the sub-corridor level. The sub-corridors were identified on the basis of milepost
values associated with each crash record. The following formula was then used to calculate
G-5
vehicular occupancy rate for each corridor/sub-corridor for each year as shown in the table
below.
𝑂𝑐𝑐𝑢𝑝𝑎𝑛𝑐𝑦 𝑅𝑎𝑡𝑒𝐶𝑜𝑟𝑟𝑖𝑑𝑜𝑟/𝑆𝑢𝑏𝑐𝑜𝑟𝑟𝑖𝑑𝑜𝑟 =
∑ 𝑀𝑎𝑥 𝑛𝑜. 𝑜𝑓 𝑣𝑒ℎ𝑖𝑐𝑙𝑒𝑠
⁄∑ 𝑀𝑎𝑥. 𝑛𝑜. 𝑜𝑓 𝑜𝑐𝑐𝑢𝑝𝑎𝑛𝑡𝑠
Year
Vehicular occupancy
Corridor/Sub- No. of
No. of
No. of
Rate:
corridor
Crashes Vehicles Occupants Vehicles/Occupants
2008
I-78
I-78A
2795
4702
6526
1.39
PA to 287
934
1,495
2,144
1.43
I-78B
287 to GSP
887
1459
1907
1.31
I-78C
GSP to TPK
523
909
1,295
1.42
I-78D
TPK to NY
451
839
1,180
1.41
2009
I-78
3057
5194
7075
1.36
I-78A
PA to 287
950
1,535
2,156
1.40
I-78B
287 to GSP
1087
1876
2480
1.32
I-78C
GSP to TPK
648
1,079
1,476
1.37
I-78D
TPK to NY
372
704
963
1.37
2010
I-78
2613
4648
6345
1.37
I-78A
PA to 287
688
1,172
1,658
1.41
I-78B
287 to GSP
946
1652
2155
1.30
I-78C
GSP to TPK
602
1,087
1,477
1.36
I-78D
TPK to NY
377
737
1,055
1.43
Table 1: Occupancy Rate, 2008 to 2010, I-78.
Step 5: The vehicular occupancy rates of all the three years were then averaged by the number
of years to get the AVO rate of all the sub-corridors from 2008 to 2010 for I-78 and NJ 18.
G-6

NJ Pilot Report Appendics

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