CALIFORNIA PATH PROGRAM
INSTITUTE OF TRANSPORTATION STUDIES
UNIVERSITY OF CALIFORNIA, BERKELEY
This work was performed as part of the California PATH Program of the University of California, in cooperation with the State of California Business, Transportation, and Housing Agency, Department of Trans­portation,
and the United States Department Transportation, Federal Highway Administration.
The contents of this report reflect the views of the authors who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the State of California. This report does not constitute a standard, spec­ification,
or regulation.
ISSN 1055- 1417
October 2008
Field Evaluation of San Pablo Corridor
Transit Signal Priority ( TSP) System
California PATH Working Paper
UCB- ITS- PWP- 2008- 7
CALIFORNIA PARTNERS FOR ADVANCED TRANSIT AND HIGHWAYS
Kun Zhou et al.
Report for Task Order CA- 26- 7049- 00 Field Evaluation of San Pablo Corridor Transit Signal Priority ( TSP) System Final Report Prepared by: California PATH University of California, Berkeley And California Department of Transportation In collaboration with AC Transit June, 2007 Acknowledgements This work was performed by the California PATH Program at the University of California at Berkeley in cooperation with the State of California Business, Transportation and Housing Agency, Department of Transportation ( Caltrans). The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the State of California. The authors thank Don Dean of Caltrans’ Division of Research and Innovation, Jim Cunradi and John Twichel of AC Transit District for their support. Author List University of California, Berkeley Kun Zhou Meng Li Scott Johnston Wei- Bin Zhang Caltrans Sonja Sun Kai Leung James Lau Paul Chiu STATE OF CALIFORNIA DEPARTMENT OF TRANSPORTATION
TECHNICAL REPORT DOCUMENTATION PAGE
TR0003 ( REV. 10/ 98)
1. REPORT NUMBER CA06-(?)
2. GOVERNMENT ASSOCIATION NUMBER
3. RECIPIENT’S CATALOG NUMBER
4. TITLE AND SUBTITLE Field Evaluation of San Pablo Corridor Transit Signal Priority ( TSP) System
5. REPORT DATE June 2008
6. PERFORMING ORGANIZATION CODE
7. AUTHOR( S) Kun Zhou, Meng Li, Scott Johnston, Wei- Bin Zhang, Sonja Sun Kai Leung, James Lau, Paul Chiu
8. PERFORMING ORGANIZATION REPORT NO. UCB- ITS- PRR- 200?-?
9. PERFORMING ORGANIZATION NAME AND ADDRESS California PATH Program University of California, Berkeley 1357 .46th St. Richmond, CA 94804
10. WORK UNIT NUMBER
11. CONTRACT OR GRANT NUMBER CA- 26- 7049- 00
12. SPONSORING AGENCY AND ADDRESS California Department of Transportation Division of Research and Innovation P. O. Box 942873, MS 83 Sacramento, CA 94273- 0001
13. TYPE OF REPORT AND PERIOD COVERED
14. SPONSORING AGENCY CODE
15. SUPPLEMENTAL NOTES
16. ABSTRACT This document reports the results of the evaluation of the Transit Signal Priority currently under operation at AC Transit. The paper discusses about the Measure of Effectiveness and a quantitative evaluation method for TSP. It reports the data collected and analysis conducted of the concerned TSP system and presented the evaluation results.
17. KEY WORDS Transit Signal Priority, ITS Evaluation, TSP MOE
18. DISTRIBUTION STATEMENT No restrictions. This document is available to the public through the National Technical Information Service, Springfield, VA 22161
19. SECURITY CLASSIFICATION ( of this report) Unclassified
20. NUMBER OF PAGES
21. PRICE
Reproduction of completed page authorized Field Evaluation of San Pablo Corridor Transit Signal Priority ( TSP) System
1 Overview of AC Transit Rapid 72R Corridor and TSP System
1.1 AC Transit Rapid 72R Corridor
The Alameda- Contra Costa Transit District ( AC transit) began BRT ( Bus Rapid Transit) service on its San Pablo Rapid line, Line 72R, on June 30, 2003. This rapid bus corridor is 13.5 miles long. The southern terminus is Jack London Square located in downtown Oakland, and the northern terminus is located at Contra Costa College in San Pablo. It runs through seven cities, Oakland, Emeryville, Berkeley, Albany, El Cerrito, Richmond, and San Pablo, and two counties, Alameda and Contra Costa, as illustrated in Figure 1- 1. The Rapid operates every day from 6 am to 7 pm on a headway- based schedule of 12 minutes.
Figure 1- 1 The AC Transit Rapid 72R Corridor1
1.2 Bus- Stops
This Rapid operates in mixed traffic and was developed with 27 stops located at major intersections. These stops are spaced 0.5 miles apart on average along the length of the corridor, with the minimum and the maximum distance between adjacent bus- stops at 63 meters and 1,505 meters, respectively. Most bus- stops are located at the far- side of intersections to decrease the overall travel time. Figure 1- 2 illustrates the geographic layout of bus- stops along the Rapid line 72R.
1 Source: http:// www. actransit. org Figure 1- 2 The AC Transit Rapid 72R Corridor2
1.3 Signalized Intersections
Rapid line 72R runs through 82 signalized intersections. The average distance between adjacent signalized intersections is 203 meters, with the minimum and maximum distance at 47 meter and 810 meters, respectively. Table 1- 1 lists the locations of signalized intersections along the length of the corridor. GPS location of each signalized intersections is measured at the middle of the intersection box.
2 Source: The San Pablo Rapid BRT Project Evaluation, Final Report, Federal Transit Administration, June, 2006
Van Ness St Table 1- 1 List of Signalized Intersections along Rapid Line 72R
INT ID
Cross Street
City
Latitude ( degree)
Longitude ( degree)
INT ID
Cross Street
City
Latitude ( degree)
Longitude ( degree)
1
3rd St
Oakland
37.7968
122.2759
42
W Addison St
Emeryville
37.8678
122.2917
2
5th St
Oakland
37.7981
122.2751
43
University Ave
Berkeley
37.8692
122.2921
3
6th St
Oakland
37.7988
122.2746
44
Delaware Ave
Berkeley
37.8717
122.2930
4
7th St
Oakland
37.7995
122.2742
45
Dedar Ave
Berkeley
37.8752
122.2941
5
8th St
Oakland
37.8002
122.2738
46
Gilman St
Berkeley
37.8805
122.2958
6
9th St
Oakland
37.8009
122.2733
47
Monroe St
Berkeley
37.8846
122.2971
7
10th St
Oakland
37.8016
122.2729
48
Marin Ave
Berkeley
37.8868
122.2978
8
11th St
Oakland
37.8022
122.2725
49
Bachnanan St
Berkeley
37.8878
122.2982
9
12th St
Oakland
37.8029
122.2720
50
Solano Ave
Albany
37.8903
122.2990
10
13th St
Oakland
37.8036
122.2716
51
Washington Ave
Albany
37.8923
122.2996
11
14th St
Oakland
37.8043
122.2712
52
Clay St
Albany
37.8961
122.3008
12
15th St
Oakland
37.8053
122.2706
53
Brighton Ave
Albany
37.8967
122.3010
13
16th St
Oakland
37.8058
122.2703
54
Carlson Blvd
El Cerrito
37.8990
122.3019
14
17th St
Oakland
37.8066
122.2697
55
Fairmount Ave
El Cerrito
37.9006
122.3026
15
19th St
Oakland
37.8078
122.2690
56
Central Ave
El Cerrito
37.9022
122.3034
16
20th St
Oakland
37.8091
122.2682
57
Stockton Ave
El Cerrito
37.9077
122.3062
17
20th Ave at Telegraph Ave.
Oakland
37.8095
122.2696
58
Moeser Ln
El Cerrito
37.9113
122.3086
18
20th Ave
Oakland
37.8102
122.2732
59
Schmidt Ln
El Cerrito
37.9141
122.3105
19
Castro Ave
Oakland
37.8115
122.2737
60
Manila Ave
El Cerrito
37.9157
122.3116
20
W Grand Ave
Oakland
37.8127
122.2740
61
Potrero Ave
El Cerrito
37.9207
122.3151
21
Brush Ave
Oakland
37.8135
122.2743
62
Hill St
El Cerrito
37.9236
122.3172
22
25th Ave
Oakland
37.8154
122.2748
63
Cutting Blvd
El Cerrito
37.9254
122.3185
23
27th Ave
Oakland
37.8178
122.2756
64
Knott Ave
El Cerrito
37.9266
122.3193
24
Market St
Oakland
37.8202
122.2764
65
Conlon Ave
El Cerrito
37.9296
122.3214
25
31st Ave
Oakland
37.8215
122.2768
66
Macdonald Ave
El Cerrito
37.9323
122.3234
26
35th Ave
Oakland
37.8264
122.2784
67
Barrett Ave
Richmond
37.9353
122.3251
27
36th Ave
Oakland
37.8270
122.2786
68
Roosevelt Ave
Richmond
37.9370
122.3258
28
Adeline St
Oakland
37.8287
122.2792
69
?
Richmond
37.9387
122.3266
29
40th Ave
Oakland
37.8310
122.2799
70
Clinton Ave
Richmond
37.9404
122.3275
30
Park Ave
Oakland
37.8324
122.2802
71
Solano Ave
Richmond
37.9421
122.3283
31
45th Ave
Oakland
37.8342
122.2809
72
Garvin Ave
Richmond
37.9437
122.3291
32
47th Ave
Oakland
37.8356
122.2814
73
Esmond Ave
Richmond
37.9452
122.3298
33
53rd Ave
Oakland
37.8372
122.2818
74
McBryde Ave
Richmond
37.9473
122.3308
34
Stanford Ave
Oakland
37.8409
122.2831
75
Rheem Ave
Richmond
37.9498
122.3320
35
E 63rd Ave
Oakland
37.8457
122.2846
76
FoodMax Plz
Richmond
37.9518
122.3329
36
W 63rd Ave
Oakland
37.8461
122.2847
77
San Pablo Dam Rd
Richmond
37.9533
122.3336
37
Alcatraz Ave
Oakland
37.8470
122.2850
78
Vale Rd
Richmond
37.9556
122.3357
38
Ashby Ave
Emeryville
37.8521
122.2867
79
Van Ness St
Richmond
37.9605
122.3426
39
Grayson St
Emeryville
37.8562
122.2880
80
23rd Ave
San Pablo
37.9627
122.3454
40
Dwight Way
Emeryville
37.8611
122.2896
81
International Market Plz
San Pablo
37.9650
122.3451
41
Allston Way
Emeryville
37.8664
122.2913
82
El Portal Dr
San Pablo
37.9674
122.3442
1.4 San Pablo Corridor TSP System
The Rapid employs several forms of Intelligent Transportation Systems ( ITS) to help in the operations, including the use of Transit Signal Priority ( TSP), the Automated Vehicle Locator ( AVL), Automated Passenger Counters ( APC), and real- time bus arrival information displays that are located inside the shelters at most bus- stops. The San Pablo corridor TSP system utilizes 3M’s Opticom TSP system to detect the presence of transit buses, to generate TSP calls and uses enhanced traffic signal control software developed by Caltrans to control TSP operations. The Opticom system uses infrared based communications between the buses and the signalized intersections to detect the approach of a transit bus. The primary components of the system are emitters mounted on the front of buses, an optical detector mounted on the signal head, and a phase selectors installed in the roadside controller cabinet. When activated, the bus emitter sends a frequency coded optical message that identifies the bus to the detector. The detector then sends a signal to the signal controller via the phase selector to request TSP operations. The signal is dropped when the bus has cleared the intersection, and a check- out call is placed to terminate an on- going TSP execution. The signal control system in place along San Pablo corridor is a closed- loop distributed control system with Model 170E controllers operating Caltrans C- 8 local traffic signal controller software. The enhancements for TSP have been incorporated by updating the C- 8 software to provide two types of TSP operations, Early Green which returns green earlier to the bus approach, and Green Extension which hold the green longer to allow the bus clear the intersection before the signal indication changes. Upon receiving a TSP call via the phase selector, the TSP control logic is as follows:
• If the green on the bus approach is on, the green is extended until either the maximum allowable green extension time ( ten seconds) is reached or a check- out call is received;
• If the green is off, force- off points for all minor phases are moved forward by 20% until the signal returns green to the bus approach.
Optical detectors have been installed on the worst congested roadway segment along line 72R corridor to provide TSP operations. The 7.5 mile long TSP segment consists of 37 signalized intersections between 35th Ave in Oakland and Hill St in El Cerrito.
2 Measures of Effectiveness
Measures of effectiveness ( MOEs) provide the basis for any TSP evaluation study. The California PATH program has developed a set of comprehensive MOEs that can be used for site- to- site comparison of TSP systems. These MOEs are classified into the following three categories to quantify the impacts of TSP operations on different stakeholders. 2.1 TSP System Performance
Any benefits of TSP operations depend on whether the TSP system fulfills its designed functionality. The MOEs that fall into this category capture the performance of the TSP system itself, identify operations problems and provide suggestions for improvement. Intersection- based MOEs
• Transit vehicle detection rate at TSP enabled intersections
• Successful priority execution rate at TSP enabled intersections
Trip- based MOEs
• Number of detections per bus per one- way trip
• Number of successful executions per bus per one- way trip
2.2 Benefits of TSP Operations on Transit Vehicles
The MOEs that fall into this category quantify the benefits of TSP operations to the quality of transit service. Intersection- based MOEs
• Transit vehicle stop rate due to red signal phase
• Transit vehicle stopped time due to red signal phase
Trip- based MOEs
• Trip travel time, in terms of dwell time, stopped time due to red signal phases and running time
2.3 Impacts of TSP Operations to Traffic
MOEs in this category quantify the impact of TSP operations on non- transit traffic. In particular, these measures describe impacts on cross- street and mainline left- turn traffic ( i. e., minor- phase traffic). Traffic Impacts of TSP Operations
• Traffic delay at prioritized intersections.
3 Field Data Collection
3.1 Architecture of Data Collection System
Three types of field data need to be collected to evaluate TSP system performance: transit operations data, traffic operations data and TSP operations data. Figure 3- 1 illustrates the physical architecture of the data collection system used for this study. Figure 3- 1Physical Architecture of Data Collection System The signal control system in place is a closed- loop system with control logic distributed among three levels: the local controller, the on- street master and the super master. Typically, the local controller receives information from loop detectors and Opticom phase selectors, the local master controller receives information from the local controller, and the super master enables the system operator to monitor and control the system’s operation based on data collected from the field. The data available from the signal control system include time- of- day timing plans, traffic counts, occupancy, and signal status. The resolution is every one to two seconds. The database also logs every TSP event data, including the type of request ( early green/ green extension), requested time and execution condition. The data are polled and stored in super- master computers that are located inside traffic cabinets together with field masters. The data are retrieved regularly from the field via a frame relay connection. Cell phone based GPS/ GPRS communication devices have been installed on 12 buses serving Line 72R for the purpose of collecting transit operations data. The wireless devices automatically forward bus GPS data ( UTC time, latitude, longitude, and speed over ground) to the computer located at PATH’s traffic lab. The sampling rate of GPS data updates is 1 second.
3.2 Description of Data Collection Procedures
The data used in this report were collected from March 5, 2007 to May 25, 2007. Data collection was conducted in the following two periods:
Local Master
Local Controller
Local Controller
Local Controller
..
..
..
..
Field
GPS
Detectors Phase selector
Bus
Bus
..
Super Master
• UTC time
• Vehicle count
• Signal status
• TSP events
• Transit operations
Database
Frame Relay
PATH
Real- time
Fleet Vehicles
GPRS
GPS
GPRS
GPS
Detectors Phase selector
Detectors Phase selector • Period 1 ( 03/ 05/ 2007 to 04/ 27/ 2007): “ after” ( with TSP scenario) data collection with the emitters activated for the 12 buses, so they could request priority, and
• Period 2 ( 05/ 02/ 2007 to 05/ 25/ 2007): “ before” ( without TSP scenario) data collection, with the emitters being deactivated for the 12 buses, so they could not request priority.
Within each period, data were collected at the following three different times of day:
• Morning Peak ( AM): 7 am to 11 am;
• Mid- day Peak ( MD): 11 am to 3 pm; and
• Afternoon Peak ( PM): 3 pm to 7 pm
The peaks were selected based on the traffic profiles along the corridor, and are consistent with the signal timing plans in use. The traffic, signal status, and TSP event data were independently collected from transit operations data but within the same time periods, thus both types of data can be synchronized.
3.3 Summary of Collected Bus One- Way Trips
Table 3- 1 summarizes the collected effective bus one- way trips for the “ before” scenario and “ after” scenario, in terms of time- of- day and travel direction. A total of 375 and 774 bus one- way trips were collected for the “ before” and “ after” scenario, respectively. Note that there are more bus trips being collected during the 3- month data collection period. However, some of the trip data have large GPS data noise and blockages. Those data are excluded from this study.
Table 3- 1 Number of Collected Effective Bus One- Way Trips
Northbound
Southbound
“ Before” Scenario
“ After” Scenario
“ Before” Scenario
“ After” Scenario
Morning Peak
56
104
99
137
Mid- day Peak
49
123
71
155
Afternoon Peak
30
119
70
136
Total
135
346
240
428
3.4 Summary of Collected TSP Event Log Data
Although the 37 signalized intersections, between 35th Ave in Oakland and Hill St in El Cerrito, all have TSP functionality enabled, TSP event log data were only available at 16 intersections. The reason for missing TSP event log data is that either the intersection belongs to a local city that does not record TSP events or there were communication issues between the local controller and the master controller. Table 3- 2 lists the 16 intersections that have TSP event log data available for this study. Table 3- 2 List of Intersections with TSP Event Log Data Available
Intersection ID
Cross Street
City
28
Adeline St
Oakland
29
40th Ave
Oakland
30
Park Ave
Oakland
31
45th Ave
Oakland
32
47th Ave
Oakland
33
53rd Ave
Oakland
48
Marin Ave
Berkeley
49
Bachnanan St
Berkeley
50
Solano Ave
Albany
51
Washington Ave
Albany
52
Clay St
Albany
53
Brighton Ave
Albany
56
Central Ave
El Cerrito
58
Moeser Ln
El Cerrito
59
Schmidt Ln
El Cerrito
61
Potrero Ave
El Cerrito
4 Data Analysis
The goal of this evaluation study was to assess the effectiveness of the San Pablo corridor TSP system. The study aims to provide AC Transit and Caltrans with a quantitative description of the benefits and impacts, if any, associated with implementing signal priority technology on transit and non- transit vehicle traffic. Data analysis was carried out to quantify the MOEs described in section 2 of this report.
4.1 TSP System Performance
Collected bus one- way trip data and TSP event log data at the 16 intersections listed in Table 3- 2 were used to evaluate the functionalities of the TSP system.
4.1.1 The Need for Priority
Prior to evaluating the performance of the TSP system itself, it is necessarily to understand the need for TSP operations. The need for priority varies from intersection to intersection, because the characteristics ( e. g., traffic volume, signal timing, and bus arrival time) are intersection specific. At the intersection level, two questions need to be answered: how frequently a bus would stop due to a red signal phase, i. e. the intersection stop rate and how long it would remain stationary, i. e. the average actual stopped time. At each intersection, the product of intersection stop rate and the average actual stopped time, i. e. the average stopped time due to the signal, provides a measure of the need for priority. The greater the average stopped time, the greater the need for priority. In this study, a speed threshold of 5 MPH was used to differentiate a stopped status from a moving status. This threshold was selected based on the histogram of bus speed as recorded by GPS. Figure 4- 1 and Figure 4- 2 illustrate the average intersection stopped time for northbound and southbound trips, respectively. Average Stopped Time ( Northbound, sec) 051015202530354045Adeline40thPark45th47th53rdMarinBachnananSolanoWashintonClayBrightonCentralMoeserSchmidtPotreroAMMDPMAll- day
Figure 4- 1 Average Intersection Stopped Time ( Northbound, without TSP) Average Stopped Time ( Southbound, sec) 05101520253035Adeline40thPark45th47th53rdMarinBachnananSolanoWashintonClayBrightonCentralMoeserSchmidtPotreroAMMDPMAll- day
Figure 4- 2 Average Intersection Stopped Time ( Southbound, without TSP) The top five intersections that need priority in the northbound direction are Solano Ave, Marin Ave, Adeline St, Schmidt Lane, and 40th Ave. The top five on southbound are 40th Ave, Central Ave, Solano Ave, Park Ave, and Adeline St, followed closely by Marin Ave. Based on the recorded stopped time, TSP should perform well at these intersections.
4.1.2 Transit Vehicle Detection Rate
In order to obtain priority, a transit vehicle needs to be detected first by the Opticom system and be registered to the signal controller to request TSP operations. Ideally, the detection rate should be 100 percent, as whenever a bus is within the field of view of the detector it should be detected. In reality, the detection rate is always lower than the ideal rate due to a number of reasons such as the line- of- sight between the emitter and the detector being blocked, a dirty lens on the emitter and/ or receiver or communication errors.
Figure 4- 3 and Figure 4- 4 illustrate the bus detection rate at northbound and southbound intersections, respectively. At each intersection, the detection rates are very consistent in terms of time- of- day. Among the top intersections that need priority, several intersections have detection rates lower than 50 percent. Two intersections, northbound at Solano Ave and southbound at Park Ave, have extremely low detection rates ( less than 5%). The causes of the failure in detecting transit vehicles at these intersections need to be identified and resolved to further improve the TSP system performance. Transit Vehicle Detection Rate ( Northbound) 0% 10% 20% 30% 40% 50% 60% 70% 80% Adeline40thPark45th47th53rdMarinBachnananSolanoWashintonClayBrightonCentralMoeserSchmidtPotreroAMMDPMAll- day
Figure 4- 3 Transit Vehicle Detection Rate on Northbound Transit Vehicle Detection Rate ( Southbound) 0% 10% 20% 30% 40% 50% 60% 70% 80% Adeline40thPark45th47th53rdMarinBachnananSolanoWashintonClayBrightonCentralMoeserSchmidtPotreroAMMDPMAll- day
Figure 4- 4 Transit Vehicle Detection Rate on Southbound
4.1.3 TSP Execution Rate
When a local controller receives a priority request, it grants the priority based on pre- determined criteria. Not all executions can benefit a bus. For example, if an Early Green call was placed during the yellow or all- red interval of the phase just prior to the bus phase, the bus gains no benefit from the execution as the yellow and all- red interval can not be reduced. Another example is if a Green Extension call was placed, but the bus passes the intersection during the normal green interval. Therefore, we defined the execution rate as the rate of successful executions. The result is presented in Table 4- 1. Priority execution rates are also consistent in terms of time- of- day. Among the top intersections that need priority, most intersections have higher priority execution rates. The exceptions are northbound at Solano Ave and southbound at Park Ave and Central Ave. Table 4- 1 Priority Execution Rate (%)
INT ID
Cross St
Direction
Morning Peak
Mid- day Peak
Afternoon Peak
EG
GE
Total
EG
GE
Total
EG
GE
Total
28
Adeline St
Northbound
19.2
6.7
26.0
19.5
7.3
26.8
26.1
5.9
31.9
Southbound
29.2
16.8
46.0
35.5
7.7
43.2
38.2
11.8
50.0
29
40th Ave
Northbound
19.2
5.8
25.0
13.0
9.8
22.8
9.2
8.4
17.6
Southbound
12.4
16.1
28.5
11.0
18.1
29.0
24.3
11.8
36.0
30
Park Ave
Northbound
2.9
46.2
49.0
10.6
37.4
48.0
12.6
46.2
58.8
Southbound
0.0
0.0
0.0
1.3
0.0
1.3
0.0
0.0
0.0
31
45th Ave
Northbound
4.8
4.8
9.6
18.7
4.1
22.8
20.2
3.4
23.5
Southbound
5.1
13.1
18.2
8.4
9.0
17.4
10.3
12.5
22.8
32
47th Ave
Northbound
9.6
4.8
14.4
9.8
4.1
13.8
16.8
3.4
20.2
Southbound
1.5
8.0
9.5
7.1
5.8
12.9
0.7
5.1
5.9
33
53rd Ave
Northbound
2.9
6.7
9.6
2.4
2.4
4.9
5.0
2.5
7.6
Southbound
10.9
19.7
30.7
13.5
21.3
34.8
10.3
19.1
29.4
48
Marin Ave
Northbound
26.0
5.8
31.7
22.8
4.9
27.6
20.2
8.4
28.6
Southbound
27.7
1.5
29.2
27.1
7.1
34.2
30.9
1.5
32.4
49
Bachnanan St
Northbound
0.0
0.0
0.0
0.0
0.8
0.8
5.0
8.4
13.4
Southbound
11.7
0.7
12.4
5.8
0.0
5.8
5.9
3.7
9.6
50
Solano Ave
Northbound
1.0
1.9
2.9
1.6
1.6
3.3
1.7
0.0
1.7
Southbound
14.6
8.0
22.6
14.8
3.2
18.1
16.9
2.9
19.9
51
Washington Ave
Northbound
5.8
0.0
5.8
4.9
3.3
8.1
8.4
4.2
12.6
Southbound
0.7
0.0
0.7
2.6
0.6
3.2
2.2
0.0
2.2
52
Clay St
Northbound
0.0
1.0
1.0
0.8
0.0
0.8
1.7
0.8
2.5
Southbound
1.5
0.7
2.2
2.6
1.9
4.5
6.6
1.5
8.1
53
Brighton Ave
Northbound
0.0
0.0
0.0
0.8
0.8
1.6
0.8
1.7
2.5
Southbound
0.0
0.0
0.0
0.0
0.6
0.6
0.0
0.0
0.0
56
Central Ave
Northbound
0.0
0.0
0.0
0.8
0.0
0.8
0.8
0.0
0.8
Southbound
0.7
0.0
0.7
0.6
0.6
1.3
0.7
0.7
1.5
58
Moeser Ln
Northbound
7.7
1.0
8.7
2.4
2.4
4.9
4.2
0.0
4.2
Southbound
5.8
0.0
5.8
3.9
0.0
3.9
2.9
0.7
3.7
59
Schmidt Ln
Northbound
13.5
1.9
15.4
10.6
3.3
13.8
9.2
2.5
11.8
Southbound
5.1
0.7
5.8
2.6
1.9
4.5
2.9
2.9
5.9
61
Potrero Ave
Northbound
7.7
1.0
8.7
4.1
3.3
7.3
6.7
4.2
10.9
Southbound
11.7
2.9
14.6
20.6
2.6
23.2
15.4
3.7
19.1
EG: successful Early Green execution GE: successful Green Extension execution
4.1.4 Number of Detections and Number of Executions per Bus One- Way Trip
Figure 4- 5 and Figure 4- 6 illustrate the number of detections and number of successful executions per bus one- way trip. For the purpose of comparison, the number of stops at the 16 signalized intersections is also included. On average, when traveling northbound, the bus stopped at 3 intersections and the system generated 9 priority requests. Of those, two were successfully executed. The numbers are 4, 7 and two, respectively, for southbound trips. Number of Detections and Executions per Bus One- WayTrip ( Northbound) 012345678910No. of detectionNo. of EGexecutionNo. of GEexecutionTotal No. ofexecutionNo. of stops at redAMMDPM
Figure 4- 5 Number of Detections and Executions per Trip on Northbound Number of Detections and Executions per Bus One- Way Trip ( Southbound) 02468No. of detectionNo. of EGexecutionNo. of GEexecutionTotal No. ofexecutionNo. of stops at redAMMDPM
Figure 4- 6 Number of Detections and Executions per Trip on Southbound
4.2 Benefits of TSP Operations on Transit Vehicles
Synchronized bus location data and signal status data make it possible to quantify bus intersection delay. Bus travel time can be broken down into three components as shown: Travel time = Dwell time ( at bus stops) + Stopped time ( at signals) + Running time Benefits of TSP operations on transit vehicles can be represented at the intersection level, in terms of changes in intersection stop rates and average stopped time, and at the trip level, in terms of total trip travel time, running time and stopped time at signals.
4.2.1 Intersection Stop Rate
Figure 4- 7 and Figure 4- 8 compare bus stop rates at the 16 intersections for the scenarios of “ with TSP” and “ without TSP.”. Comparison of Bus Stop Rate ( Northbound, %) 0% 10% 20% 30% 40% 50% 60% 70% Adeline40thPark45th47th53rdMarinBachnananSolanoWashintonClayBrightonCentralMoeserSchmidtPotreroWithout TSPWith TSP
Figure 4- 7 Comparison of Intersection Stop Rates on Northbound At the top five northbound intersections that were determined to need priority the most, TSP operations significantly reduced stop rate at Solano Ave (- 11%), Marin Ave (- 9%), Adeline St (- 22%) and Schmidt Lane (- 12%). However, TSP slightly increased the stop rate at 40th Ave (+ 5%). Comparison of Bus Stop Rate Southbound, %) 0% 10% 20% 30% 40% 50% 60% Adeline40thPark45th47th53rdMarinBachnananSolanoWashintonClayBrightonCentralMoeserSchmidtPotreroWithout TSPWith TSP
Figure 4- 8 Comparison of Intersection Stop Rates on Southbound At the top five southbound intersections that were determined to need priority the most, TSP operations reduced stop rates at all of the intersections, more specifically, a 6% reduction at 40th Ave, 2% at Central Ave, 1% at Solano Ave, 22% at Park Ave, and 1% at Adeline St.
4.2.2 Average Intersection Stopped Time
Figure 4- 9 and Figure 4- 10 compare the average intersection stopped time at the 16 intersections for the scenarios of “ with TSP” and “ without TSP.” The changes at the intersections marked with a yellow dot are statistically significant at the 5% level. Comparison of Average Intersection Stopped Time ( Northbound, sec) 0510152025Adeline40thPark45th47th53rdMarinBachnananSolanoWashintonClayBrightonCentralMoeserSchmidtPotrero0.00.1Without TSPWith TSPp- value
Figure 4- 9 Comparison of Average Intersection Stopped Time on Northbound On northbound trips, the significant changes occurred at Adeline St ( reduced 3 seconds), Marin Ave ( reduced 5 seconds), Central Ave ( increased 6 seconds), Schmidt St ( reduced 4 seconds), and Potrero Ave ( increased 2 seconds). Comparison of Average Intersection Stopped Time ( Southbound, sec) 0510152025Adeline40thPark45th47th53rdMarinBachnananSolanoWashintonClayBrightonCentralMoeserSchmidtPotrero0.00.1Without TSPWith TSPp- value
Figure 4- 10 Comparison of Average Intersection Stopped Time on Southbound On southbound trips, the significant changes occurred at 40th Ave ( reduced 7 seconds), Park Ave ( reduced 9 seconds), 45th Ave ( reduced 3 seconds), 47th Ave ( reduced 1 second), 53rd Ave ( reduced 2 seconds), Marin Ave ( increased 5 seconds), and Brighton Ave ( reduced 1 second).
4.2.3 Actual Stopped Time per Prioritized Intersection
At isolated intersections, TSP operations can reduce bus intersection delay. On the Rapid bus lines, there are normally several intersections between two consecutive bus- stops. The amount of delay savings at a prioritized intersection is affected by the TSP execution at a particular intersection as well as priority executions at upstream intersections. Upstream executions can change the chance of stopping and the actual stopped time at downstream intersections. This explains the situation that TSP operations can increase the bus stop rate and the average stopped time at some intersections, as described in the previous two sections. The actual stopped time per prioritized intersection per bus trip is used to quantify the TSP impacts on bus intersection delay. The comparison results are presented in Table 4- 2. In this comparison, all of the 37 prioritized intersections are considered. On average, TSP operations reduced the actual stopped time per prioritized intersection by 8.2% ( or 2.4 seconds) on northbound trips and by 10.1% ( or 3.1 seconds) on southbound trips.
Table 4- 2 Actual Stopped Time per Prioritized Intersection per Bus Trip
Without TSP ( second)
With TSP ( second)
Change
t- test ( p- value)
Value
Percentage
Northbound
29.1
26.7
- 2.4
- 8.2%
0.0053
Southbound
30.4
27.3
- 3.1
- 10.1%
0.0000
4.2.4 Trip Travel Time, Dwell Time, Running Time and Stopped Time
Table 4- 3 compares the trip- based MOEs for the scenarios of “ without TSP” and “ with TSP”. In most cases, TSP operations reduced bus travel time, total intersection delay and running time. TSP impacts on bus travel time have a strong relationship with the changes in the number of stops at red signals. In cases where the number of stops at red was reduced, transit vehicles gained benefits from TSP operations. The changes in travel time, total intersection delay and running time for these cases are statistically significant at 5% level of significance. For example, for the mid- day southbound bus trips, bus travel time was reduced by 7% ( 221 seconds), total intersection delay was reduced by 16% ( 104 seconds), total running time was reduced by 5% ( 118 seconds). Therefore bus average traveling speed was increased by 5%. In cases that the number of stops at red was increased, such as afternoon- peak northbound bus trips, the impacts are statistically insignificant. Table 4- 3 Comparison of Trip Time / Total Intersection Delay
Direction
MOE
Time- of- Day
Without TSP
With TSP
Change
t- test ( p- value)
Value
%
Northbound
Travel Time ( minutes)
Morning Peak
45.5
44.1
- 1.4*
- 3.1%*
0.0162*
Mid- Day Peak
50.2
48.9
- 1.3
- 2.5%
0.0570
Afternoon Peak
53.5
54.0
0.5
1.0%
0.6739
Running Time ( minutes)
Morning Peak
38.4
37.5
- 0.8*
- 2.2%*
0.0244*
Mid- Day Peak
40.6
40.0
- 0.6
- 1.6%
0.1103
Afternoon Peak
41.7
42.7
1.1
2.5%
0.0984
Total Intersection Stopped Time ( minutes)
Morning Peak
7.1
6.5
- 0.6
- 8.2%
0.0822
Mid- Day Peak
9.6
9.0
- 0.7
- 6.8%
0.1120
Afternoon Peak
11.8
11.3
- 0.5
- 4.4%
0.4853
Dwell Time ( minutes)
Morning Peak
4.9
5.7
0.8*
17.1%*
0.0224*
Mid- Day Peak
5.5
6.5
1.0*
18.1%*
0.0234*
Afternoon Peak
7.6
8.0
0.4
5.5%
0.5571
Number of Stops at Red Signal
Morning Peak
17.4
16.7
- 0.7
- 4.0%
0.2196
Mid- Day Peak
20.6
20.5
- 0.1
- 0.5%
0.8699
Afternoon Peak
22.7
23.6
0.9
4.0%
0.3231
Southbound
Travel Time ( minutes)
Morning Peak
48.6
47.3
- 1.3*
- 2.6%*
0.0138*
Mid- Day Peak
53.3
49.6
- 3.7*
- 6.9%*
0.0000*
Afternoon Peak
53.6
52.4
- 1.2
- 2.3%
0.1244
Running Time ( minutes)
Morning Peak
39.8
39.4
- 0.4
- 1.0%
0.2186
Mid- Day Peak
42.3
40.3
- 2.0*
- 4.6%*
0.0000*
Afternoon Peak
42.6
42.1
- 0.4
- 1.0%
0.3832
Total Intersection Stopped Time ( minutes)
Morning Peak
8.7
7.8
- 0.9*
- 9.9%*
0.0036*
Mid- Day Peak
11.0
9.3
- 1.7*
- 15.7%*
0.0001*
Afternoon Peak
11.1
10.3
- 0.8
- 7.1%
0.0839
Dwell Time ( minutes)
Morning Peak
7.7
7.3
- 0.4
- 5.4%
0.3406
Mid- Day Peak
7.3
7.0
- 0.3
- 4.2%
0.4797
Afternoon Peak
7.2
7.2
0.1
1.1%
0.8757
Number of Stops at Red Signal
Morning Peak
18.7
18.3
- 0.4
- 2.1%
0.3462
Mid- Day Peak
21.0
19.6
- 1.4*
- 6.7%*
0.0064*
Afternoon Peak
20.9
21.0
0.1
0.5%
0.9690
* Significant at 5% level of significance
4.3 Impacts of TSP Operations on Traffic
Traffic delay at prioritized intersection is the major measure of TSP impacts to traffic. To make a meaningful comparison, the delays of “ without TSP” are averaged across multiple cycles, corresponding to, in terms of time- of- day, the cases of “ with TSP”. The delays of “ with TSP” are averaged across only three cycles, including the cycle of TSP execution and the immediately preceding and following cycles. Note that the definition of average delay “ with TSP” here is different from the one used in many previous evaluations, such as the evaluation of LADOT/ LAMTA’s signal priority system. There, delay is averaged across a certain period of time encapsulating all cycles, whether impacted by TSP operation or not. With such a definition, if TSP operation is infrequent, the calculated average delay would not change when compared with the scenario of “ without TSP”. The prioritized intersection at Potrero Ave was selected for the evaluation of the TSP impacts on traffic. For each early green execution the average green time stolen from the minor phase traffic was 6 seconds; and that for each green extension execution was 5 seconds. Table 4- 4 compares the traffic delays for the scenario of “ without TSP” and “ with TSP”, in terms of the types of granted priority. TSP operations reduced major phase traffic delay as that traffic shares the right- of- way with the transit vehicles. The negative impacts on minor phase ( or cross street) traffic were minor, within 2 seconds per vehicle.
Table 4- 4 Comparison of Traffic Delays
Early Green Execution
Green Extension Execution
Major phase delay
Minor phase delay
Major phase delay
Minor phase delay
Without TSP ( sec/ veh)
17.5
36.3
17.2
38.1
With TSP ( sec/ veh)
16.3
38.1
16.4
38.2
Changes ( sec/ veh)
- 1.2
1.8
- 0.8
0.0
Changes in %
- 7.0%
5.0%
- 4.8%
0.1%
5 Summary and Recommendations for Further System Improvement
AC Transit began its BRT service, the Rapid 72R line, on June 30, 2003. This 13.5 mile long bus corridor, the San Pablo corridor, covers a total of 82 signalized intersections as well as 27 bus- stops. 37 out of the 82 intersections are TSP enabled to reduce the transit travel time along the corridor. The TSP system utilizes 3M’s Opticom TSP system to detect the presence of transit vehicles and to request TSP operations to the signal controller. The enhancements for TSP operations developed by Caltrans have been incorporated by updating the C- 8 software to provide early green and green extension treatments. The goal of this study was to assess the effectiveness of the San Pablo corridor TSP system. A data collection system was set up to collect synchronized traffic and transit operations data. The traffic data were collected through the signal control system and include traffic counts, occupancy, and signal status. The data resolution was every two seconds. TSP event log data such as priority requests, requested times and execution conditions were also collected. In addition, locations of transit vehicles were recorded second by second via a portable GPS/ GPRS device installed on 12 Rapid 72R buses operating in the corridor. Data were transmitted via those devices to a data server computer located at PATH traffic lab. Field operations data used in this study were collected from March 5, 2007 and May 25, 2007. The collections of “ after” survey data (“ with TSP”) and “ before” survey data (“ without TSP”) were conducted in sequence: from March 5, 2007 to April 27, 2007 for “ after” survey with emitters active on the 12 buses, and from May 2, 2007 to May 25, 2007 for “ before” survey, with the 23 buses having de- activated emitters. The collected data were then analyzed to evaluate the performance of the TSP system, the main concerns being fulfillment of system functionality and TSP impacts on transit vehicles and on traffic. TSP System Performance On average, the transit vehicle’s detection rate at prioritized intersection is about 50%. The detection rates at intersections that buses made most frequent stops were within a reasonable range. However, the detection rates at Solano Ave northbound and Park Ave southbound were extremely low, while transit bus stop rate at those locations is relatively high. Along the 16 sampling prioritized intersections, the bus stopped at 3 intersections when traveling on northbound and 4 while southbound. The TSP system generated 9 northbound priority requestsand 7 southbound requests. Of those requests, two northbound and two southbound requests were successfully executed. TSP Impacts on Transit Vehicles At intersections that transit vehicles made the most frequent stops, TSP operations were likely to reduce bus intersection stop rate and stopped time. The actual stopped time per prioritized intersection per trip was reduced by 8.2% ( 2.4 seconds) on northbound and reduced by 10.1% ( 3.1 seconds) on southbound trips. The reductions are statistically significant at the 5% level of significance. In most cases, TSP operations reduced bus travel time, total intersection delay and running time. It was found that TSP impacts on bus travel time have a strong relationship with the changes in number of stops at red signals. In cases where the number of stops at red was reduced, transit vehicles gained benefits from TSP operations. The changes in travel time, total intersection delay and running time are statistically significant at 5% level of significance for those cases. For example, on the mid- day southbound bus trips, bus travel time was reduced by 7% ( 221 seconds), total intersection delay was reduced by 16% ( 104 seconds), total running time was reduced by 5% ( 118 seconds), and therefore bus average traveling speed was increased by 5%. In cases that the number of stops at red was increased, such as afternoon- peak northbound bus trips, the impacts are statistically insignificant. TSP Impacts on Traffic When granting priority to transit vehicles the signal controller managed to steal green time from the minor phase ( or cross street) traffic. The average time stolen from the minor phase was 6 seconds for an early green execution and 5 seconds for a green extension.
Intersection delays were calculated for both the major phase traffic and the minor phase traffic to quantify the TSP impacts on traffic. For the case of “ with TSP”, the delay was averaged across three signal cycles, including the cycle of TSP execution and the immediately preceding and following cycles. With such a delay definition, the impacts are independent with the TSP execution rate. TSP operations reduced major phase traffic delay, as that traffic shares the right- of- way with the transit vehicles, and increased the minor phase traffic delay. The changes in traffic delay were minor, all within 2 seconds per vehicle. Recommendations for Further System Improvements
• Resolve the low detection rates at some intersections
• Enable TSP functionality at selected intersections that currently do not provide TSP
• Intelligently grant TSP taking into consideration the affects of the initial priority execution on the downstream intersections.