Integrating Transit Signal Priority and
Adaptive Traffic Signal Control
A final report for the National ITS Implementation
Center
Kyoungho Ahn, Ph.D.
Senior Research Associate
Virginia Tech Transportation Institute
and
Hesham Rakha, Ph.D., P.Eng.
Professor, Civil & Environmental Engineering
Virginia Polytechnic Institute & State University
and
Director, Center for Sustainable Mobility
Virginia Tech Transportation Institute
INTRODUCTION
This study quantifies the impact of Transit Signal Priority (TSP) operations within the SCOOT (Split Cycle Offset Optimization Technique) adaptive traffic signal control system on transit and passenger vehicle operations using field-collected Global Positioning System (GPS) data. The SCOOT system optimizes the signal timing split, cycle, and offset in real-time to minimize vehicle stops and delays in response to fluctuations in traffic demand. The system is deployed and operated by transportation agencies in North America and worldwide.
TSP, which is defined as “an operational strategy that facilitates the movement of in-service transit vehicles, either buses or streetcars, through traffic-signal controlled intersections”, is recognized as an emerging technology that is capable of enhancing traditional transit services. TSP is deployed to improve transit operations and service quality and eventually promote more ridership, improve person mobility, reduce traffic congestion, and reduce mobile-source emissions and fuel consumption rates (Baker et al., 2005).
The SCOOT version 3.1 and later versions contain TSP functionality as an option. Researchers have attempted to evaluate TSP within SCOOT using either analytical and/or simulation tools (Dion and Rakha, 2005; Feng et al., 2003; Hounsell and Landles, 1995). However, there are very limited field studies that have been performed to demonstrate the benefits of integrating a TSP system within an adaptive traffic signal control system.
The Columbia Pike arterial, also known as VA 244, runs through the Arlington and Fairfax Counties in the Northern Virginia section of the Washington, D.C. metropolitan area. The section of the Columbia Pike (between Dinwiddie and Courthouse Rd.) is controlled by the SCOOT system, while other intersections are controlled using traditional time-of-day fixed-time control. This study quantifies the impact of integrating TSP operations within the SCOOT system on a number of measures of effectiveness (MOEs) using the 16G metro bus line along the Columbia Pike corridor by gathering field-collected GPS data. In particular, this study describes the findings of the field evaluation study of TSP operations on transit-vehicle travel time and intersection delay, fuel consumption, and emissions.
PURPOSE AND STUDY SCOPE
The purpose of this project is to quantify the impact of TSP operations within the SCOOT adaptive traffic signal control system on transit and passenger vehicles based on a field study evaluation. In particular, the objectives of this study are summarized as follows:
· To evaluate the benefits of TSP on transit and passenger vehicles in terms of travel time and intersection delay savings using field-collected GPS data.
· To evaluate the energy and environmental impact of TSP within a SCOOT system using a microscopic energy and emission model.
The scope of this study is limited to the field evaluation of TSP impacts using GPS data that are gathered with and without TSP operation along the Columbia Pike corridor during the morning, mid-day, and afternoon peak periods.
METHODS
In order to meet the objectives of this study, the following three tasks were performed.
1. Collect GPS data for transit vehicles with and without TSP along the Columbia Pike corridor study section.
2. Extract from the GPS data relevant data for the study section and estimate various measures of effectiveness from the GPS data.
3. Conduct a field evaluation of TSP impacts within the SCOOT system on transit and passenger car performances in terms of travel time and delays at critical intersections.
4. Investigate the energy and environmental impact of an integrated TSP and SCOOT adaptive traffic signal control system.
The following section describes the study corridor characteristics, the transit signal priority logic, and the GPS data collection procedures which include a description of the GPS equipment and experimental design of bus travel data collection. Finally, the GPS data reduction procedures and data analyses are discussed.
Study Corridor Characteristics
As shown in Figure 1, the study corridor, which is one of major urban arterials in the Northern Virginia section of the Washington, D.C. metropolitan area, extends over 3.4 mi (5.4 km) and covers 20 signalized intersections. Columbia Pike, which runs through Arlington County, VA, has two lanes per direction through the study section. The study section starts at Carlin Springs Rd. to the west and extends to the Joyce St. intersection to the east. The corridor serves residential and medium-density retail business neighborhoods as well as large federal agencies, such as the Pentagon and Navy Annex, at its eastern end. The corridor also connects congested interstate highway interchanges on I-395 and serves two closely located metro stations, Pentagon Station and Pentagon City Station.
Traffic flows along the corridor are typically directional. During the morning peak hours, traffic along the study corridor generally moves eastbound, towards downtown Washington, D.C. and the Pentagon City Station metro station. However, it should be noted that during the afternoon peak period the corridor also carries a significant traffic demand in the eastbound direction. The corridor serves approximately 26,000 vehicles per day. It should be noted that the western end of the study section, which has closely spaced signalized intersections, is typically more congested than other portions of the study section. Of the 20 signalized intersections, those with Carlin Springs, George Mason, Glebe, Walter Reed, and Washington Blvd. carry significant traffic demand from side streets.
Figure 1. Columbia Pike Study Corridor
The study corridor is controlled by the ACTRA traffic management system. Intersections between Dinwiddie and Quinn are controlled by the SCOOT system (16 intersections) and the other intersections are controlled by time-of-day fixed-time control. Among the 16 SCOOT controlled intersections, seven intersections were equipped with transit signal priority equipment and also three more intersections among the traditional time-of-day fixed-time controlled intersections were operated by the TSP system. Thus, a total of ten intersections are controlled by the TSP system, as demonstrated in Table 1. The average traffic signal spacing is 272 m (892 ft) and the section between Dinwiddie and George Mason, which is 990 m (3250 ft) in length, covers six signalized intersections with average traffic signal spacing of 165 m (541 ft). It should be noted that significant delay was caused due to these closely spaced traffic signals.
Table 1. Study Intersections in Columbia
Pike
|
Intersection Name |
Control |
Purpose |
|
Columbia Pike & Carlin
Springs |
ACTRA |
TSP/EVP |
|
Columbia Pike &
Jefferson |
ACTRA |
TSP/EVP |
|
Columbia Pike &
Greenbrier |
ACTRA |
TSP/EVP |
|
Columbia Pike &
Dinwiddie |
SCOOT |
TSP/EVP |
|
Columbia Pike & Four
Mile Run |
SCOOT |
EVP |
|
Columbia Pike &
Buchanan |
SCOOT |
TSP/EVP |
|
Columbia Pike &
Wakefield |
SCOOT |
EVP |
|
Columbia Pike & Thomas |
SCOOT |
EVP |
|
Columbia Pike & Taylor |
SCOOT |
EVP |
|
Columbia Pike & George
Mason |
SCOOT |
EVP |
|
Columbia Pike & Quincy |
SCOOT |
EVP |
|
Columbia Pike & Monroe |
SCOOT |
EVP |
|
Columbia Pike & Glebe |
SCOOT |
TSP/EVP |
|
Columbia Pike &
Highland |
SCOOT |
EVP |
|
Columbia Pike & Walter
Reed |
SCOOT |
TSP/EVP |
|
Columbia Pike & Barton |
SCOOT |
TSP/EVP |
|
Columbia Pike & Wayne |
SCOOT |
EVP |
|
Columbia Pike & Court House |
SCOOT |
TSP/EVP |
|
Columbia Pike & Quinn |
SCOOT |
TSP/EVP |
|
Columbia Pike & Joyce |
ACTRA |
EVP |
Ten different bus routes (16A, 16B, 16D, 16E, 16F, 16J, 16G, 16H, 16K, and 16W) operated by the Washington Metropolitan Area Transit Authority (WMATA) travel along the study corridor. All ten routes connect either Pentagon Station or Pentagon City Station, which is located in proximity to the Joyce St. intersection, and serves the residential areas east of the study corridor. It should be noted that the corridor serves 9,000 transit trips per day, which is the highest ridership of any bus corridor in Virginia. For purposes of this study, only the 16G route buses were utilized since this bus route extends over the entire study corridor.
As illustrated in Figure 2, bus route 16G departs from Pentagon City Metro Station and connects to Carlin Springs Rd. for westbound trips. For eastbound trips, the route departs from Dinwiddie St. to Pentagon City Metro Station, providing an access to the Washington Metro-rail Service. Thus, the trip distance (3.4 mi) of the westbound route is longer than the eastbound bus trips (2.87 mi). It should be noted that the large red circles indicate the eastern end and western end of the study corridor.
Figure 2.
Metrobus 16G Route
TSP Logic
The priority logic that was implemented along the study corridor within the SCOOT system involved green-extension and early green activation. Green extension was granted when a transit vehicle was detected or expected to arrive at a traffic signal a few seconds after the end of the green indication. Consequently, the transit vehicle utilized additional green time to allow it to clear the intersection before the traffic signal indication changed. This strategy was only provided when the signal was in a green indication and the approaching vehicle was equipped with a transit priority device; thus if the TSP-equipped vehicle arrived during a red indication, signal priority was not granted. The green-extension strategy is known to be one of the most effective approaches in granting priority to transit vehicles. An early green strategy was also utilized along Columbia Pike. The early green shortens the current green time and the succeeding phase is called to expedite the return to green to the transit vehicles (Baker et al., 2005).
The TSP system consists of emitters on the transit vehicles and optical detectors located at the traffic signals. The emitter is typically installed on the roof of transit vehicles while an optical detector and a confirmation light were set up on the traffic signal head. The emitter generates a series of pulses in the infrared and visible wavelengths. These pulses are sensed by the detector mounted at the intersection. The TSP system is processed when the optical detector receives a request from a transit vehicle if there is no ongoing pedestrian phase at the time and no emergency vehicle preemption call is being made simultaneously.
METHODS
In order to meet the objectives of this study, the following four tasks were performed.
1. Collect GPS data for transit and passenger vehicles with and without TSP along the study corridor study section.
2. Extract the GPS relevant data for the study section and estimate various measures of effectiveness from the collected GPS data.
3. Conduct a field evaluation of TSP impacts on performance in terms of travel time and delays at intersections.
4. Conduct an analysis of environmental and energy impacts on TSP operations
GPS Data Collection Procedures
GPS technology is increasingly being used for transportation-related applications. The study utilized portable Wide-Area Augmented System (WAAS)-enabled GPS receivers to gather second-by-second transit vehicle trajectories along the Columbia Pike study section. WAAS-enabled GPS receivers provide longitude and latitude data to an accuracy of 2 m, altitude data to an accuracy of 3 m, and speed measurements to an accuracy of 0.1 m/s. This section describes the experimental design for the study.
Transportation Data Collection using GPS
Reliable and accurate travel behavior data are difficult to obtain because traditional data collection is typically expensive, labor intensive, inflexible, time consuming, and error prone. Alternatively, laboratory simulation offers an economic means to gather data; however, minor behavioral differences can cause significant discrepancies between actual and measured behavior (Belliss, 2004; Marca et al., 2001). To address these problems, GPS technology integrated with in-vehicle data collection systems has emerged as a cost-effective data-gathering technology. GPS data collection systems provide a flexible data recording platform supporting a variety of in-vehicle data recording applications: GPS tracking of vehicle trajectories; real-time transmission of vehicle position and performance variables; tracking trip-making behavior (generation and routing) as a function of levels of congestion, anticipated travel time, and other route information (Marca et al., 2001).
A variety of studies have utilized GPS technology to evaluate transportation operational projects. For example, Rakha et al. demonstrated how GPS data can be utilized to evaluate the energy and environmental impacts of transportation operational projects (Rakha et al., 2001). The study demonstrated that appropriate data-smoothing techniques efficiently improved the speed profiles generated by GPS speed measurements. In addition, Marca et al. developed an extensible data collection unit (EDCU) which combines a standard GPS unit, a cellular data modem, and an embedded processor to serve the in-vehicle data collection needs of Intelligent Transportation System (ITS) researchers (Marca et al., 2001). Belliss utilized low-cost GPS equipment to measure detailed speed and travel-time data using commercial buses. The study shows that the collected GPS data allow valid calculations of speed, delay, and acceleration without the need for costly instrumentation and constant recalibration (Belliss, 2004). The GPS data collection is accurate, consistent, reliable, and automated. Because of these advantages, numerous publications have documented the use of GPS technology in transportation studies (Jeong and Rilett, 2004; Lin and Zeng, 1999; Oloufa et al., 2003; Oloufa, 2003; Quiroga and Bullock, 1997).
Experimental Design
and Bus Travel Data Collection
GPS technology offers a cost-effective means to conduct such field evaluation studies. GPS technology is increasingly being employed for ITS applications. This study utilizes portable GPS units to gather transit vehicle second-by-second trajectories to quantify the impact of TSP technology on transit-vehicle performance.
Two portable GPS units were utilized in the study: GD30L manufactured by LAIPAC Technology Inc. and a Virginia Tech Transportation Institute (VTTI) custom-built GPS unit. Both GPS units are designed to record the date, time, vehicle longitude, vehicle latitude, vehicle speed, vehicle heading, and the number of tracking satellites. They are small enough to be installed inside a glove compartment in any vehicle and powered by the cigarette-lighter power adapter. Both units are operated as a stand alone unit without the need for a PC or other equipment. Once the units are powered-up, the GPS unit collects the data automatically.
Before condition GPS data were collected on weekdays (Tuesday through Thursday) in March 2006 while after condition GPS data were collected on weekdays between October and November 2006. Unfortunately, construction on the roadway resulted in a large temporal lag between the before and after data collection efforts. The data were collected for three periods: the a.m. peak (7:30 a.m. – 9:30 a.m.), the midday peak (12:00 p.m. – 1:00 p.m.), and the p.m. peak (4:30 p.m. to 6:30 p.m.). For bus data collection, four portable GPS units were utilized on the TSP-equipped buses for transit priority detection. The bus travel data were recorded at a 1 s resolution and downloaded to a personal computer every night. For car data collection, two probe vehicles equipped with GPS units were utilized. In order to reflect the aggregate characteristics of traffic flow, the probe vehicles maintained the average speed of the traffic stream. The portion of the trips that covered the study section was extracted from the entire trip for analysis purposes using a MATLAB code that was developed for this purpose. The software automatically identified the first and last GPS points within the study corridor using the coordinates of the boundary intersections. Following the data reduction, a unique trip number was assigned to each trip.
Tables 2 and 3 show the required sample sizes for the evaluation of TSP and the number of valid GPS trip data. The minimum sample size (N) was calculated to satisfy the 95% and 90% confidence limits (Z value, 1.96 or 1.645) using the standard deviation (σ) value and travel time error (δ). As shown in the table, the GPS data that were gathered exceeded the required minimum sample size for bus and car trips. In total, 336 valid bus trips and 380 valid car trips were recorded for this study.
[1]
Table 2. Bus Sample Size Requirements
|
Direction |
|
Valid Trips |
Required Sample Size |
||
|
TSP Off |
TSP On |
95% Confidence |
90% Confidence |
||
|
EB |
AM |
36 |
31 |
32 |
23 |
|
Mid |
19 |
24 |
19 |
13 |
|
|
PM |
27 |
28 |
23 |
16 |
|
|
EB Total |
|
82 |
83 |
72 |
57 |
|
WB |
AM |
33 |
35 |
27 |
19 |
|
Mid |
20 |
23 |
21 |
15 |
|
|
PM |
33 |
27 |
24 |
||