A Corridor-Level Evaluation of GPS-Based Transit Signal Priority

The continuous growth of automobile traffic on urban and suburban arterials in recent years has created a substantial problem for transit, especially when it operates in mixed traffic conditions. As a result, there has been a growing interest in deploying Transit Signal Priority (TSP) to improve the operational performance of arterial corridors. TSP is an operational strategy that facilitates the movement of transit vehicles (e.g., buses) through signalized intersections that helps transit service be more reliable, faster, and more cost-effective. The goal of this research was to quantify the mobility and safety benefits of TSP. A microscopic simulation approach was used to estimate the mobility benefits of TSP. Microscopic simulation models were developed in VISSIM and calibrated to represent field conditions. Implementing TSP provided significant savings in travel time and average vehicle delay. Under the TSP scenario, the study corridor also experienced significant reduction in travel time and average vehicle delay for buses and all other vehicles. The importance and benefits of calibration of VISSIM model with TSP integration were also studied as a part of the mobility benefits. Besides quantifying the mobility benefits, the potential safety benefits of the TSP strategy were also quantified. An observational before-after full Bayes (FB) approach with a comparison-group was adopted to estimate the crash modification factors (CMFs) for total crashes, fatal/injury (FI) crashes, property damage only (PDO) crashes, rear-end crashes, sideswipe crashes, and angle crashes. The analysis was based on 12 corridors equipped with the TSP system and their corresponding 29 comparison corridors without the TSP system. Overall, the results indicated that the deployment of TSP improved safety. Specifically, TSP was found to reduce total crashes by 7.2% (CMF = 0.928), FI crashes by 14% (CMF = 0.860), PDO crashes by 8% (CMF = 0.920), rear-end crashes by 5.2% (CMF = 0.948), and angle crashes by 21.9% (CMF = 0.781). Alternatively, sideswipe crashes increased by 6% (CMF = 1.060), although the increase was not significant at a 95% Bayesian credible interval (BCI). These results may present key considerations for transportation agencies and practitioners when planning future TSP deployments.

Driven by the growing demand for transportation, research on how to alleviate urban traffic congestion has received increasing attention, while public transportation is an effective way to solve the problem of urban congestion. As an important means to improve the speed and reliability of the public transportation system, the transit signal priority (TSP) control system has been widely deployed for the last few decades. With the emergence and development of V2X technology, it provides a wider range and more accurate data for the real-time TSP strategy, which has made the TSP control strategy have a new development. In this paper, a V2X (Vehicle to Everything) based TSP control strategy was proposed for a single intersection. Enabled by V2X communication, bus can send a priority request before entering the intersection, activating the real-time signal priority control of the front intersection before the bus reaches the intersection. In this TSP strategy, the travel time was estimated based on the shock wave theory. Finally, the field test was designed and implemented to test the performance of V2X communication and analyze and verify the proposed TSP strategy.

Bus rapid transit (BRT) as a valid means of public transportation for overpopulated country of China is an especial mode of travel to relieve traffic congestion. Despite utilizing exclusive bus line, BRT constantly experiences delay at intersections when encountering a red signal. Transit signal priority (TSP) considered as a promising control strategy to improve the operation efficiency of BRT has been widely used at isolated intersections or arterials to reduce travel delay. However, as a transport with schedule, the unexpected arrival time of transit vehicle at stop lines of consecutive intersections inevitably affects the operation reliability of the BRT system. To solve the problem, this study develops an optimization model of conditional TSP for bus rapid transit with the objective of improving the on-time performance of the BRT system while mitigating adverse impacts on private vehicles. A stop-to-stop segment is established with both road sections and intersections for the consideration of the transit operation reliability. The constraints on the degree of saturation and queue overflow were considered for mitigating adverse impacts of TSP. A mixed-integer non-linear programming procedure is adopted to formulate the optimization model. The mathematical model is linearized and solved by the branch-and-bound method. Extensive numerical analyses are conducted, and the proposed conditional TSP strategy is compared with unconditional TSP and no-TSP control to evaluate its performance under various traffic and signal conditions. A case study of a BRT line in Shanghai, China, was selected to illustrate the effectiveness of the proposed model. For the tested scenarios, the transit on-time performance was improved by 21%, with an incurred cost of 3.4% increase in the delay of private vehicles. This indicates that the proposed model performs well in maintaining the reliability of the transit system with the least impact on general traffic.

Transit signal priority (TSP) is a signal timing strategy to give priority to transit by adjusting the signal operation with the goal of reducing transit delay and improving reliability. While TSP can be a powerful tool, TSP deployments in the U.S. have often resulted in marginal improvements. The primary reasons for limited TSP effectiveness are short detection horizons for TSP requests (e.g., 10 s), near-side bus stops (i.e., located before crossing an intersection) that influence arrival times at the downstream traffic signal, and restrictive signal timing strategies (e.g., lock-out policies that inhibit TSP for a specified amount of time, coordinated control that offers little flexibility for TSP). This paper documents the impacts of a “next-generation” TSP system that couples with custom signal control logic for TSP through a field deployment in Portland, Oregon, U.S., using emerging data sources. The system uses cloud-based, predictive logic for estimating time of arrival, with predictions of bus arrivals available up to 2 min ahead of each intersection and updated continuously every 1 s. The custom signal control logic includes advanced TSP strategies that can take advantage of early prediction. Using data from high-resolution automatic vehicle location, analysis results show the custom signal controller logic with advanced prediction resulted in an average bus delay reduction of 29 s per intersection at major intersections (a reduction of 69% compared with baseline). Analyses using automated traffic signal performance measures and vehicle probe data showed these bus delay improvements were achieved with marginal impacts on motorists and without additional delay to pedestrians and bicycles.

Transit signal priority (TSP) has become popular among cities in the United States as a transit preferential treatment that improves transit operational performance through adjusting signal timing when transit vehicles are approaching signalized intersections. The majority of attention has been given to the operational effects of TSP. A small number of prior studies have discussed such a system’s effects on traffic safety, with few studies investigating North American implementations. Prior assessment results were mixed, leaving the understanding of TSP’s safety effects unclear. The purpose of this study was to add to the existing research on this topic, and to get a better understanding of TSP’s safety effects. An empirical Bayes (EB) before–after analysis was carried out using TSP implementation data from King County, Washington. Eleven transit corridors having had TSP activated in 2010–2014 were assessed, and 75 street sections without TSP treatment were selected to form a reference group. The EB analysis results showed a 13% reduction in total crashes with the implementation of TSP, a 16% reduction in property-damage-only crashes, and a 5% reduction in fatal and injury crashes. These results are consistent with most existing TSP safety studies. The study implies that TSP brings road safety benefits to transit corridors in a U.S. metropolitan setting, and suggests that transportation and transit agencies add safety assessment into future TSP practices.

The design of transit signal priority (TSP) systems requires knowledge of dwell-time distributions at bus stops within the block. Dwell-time trends are not well established in the literature despite the ubiquity of large sets of automated passenger count (APC) data. Additionally, the impact of dwell-time variability on TSP performance is not well studied, particularly with field-collected dwell-time data. This study first analyzes trends and distributions inherent in dwell-time data deduced from APC data. Dwell times vary from stop to stop and for each stop by time of day (TOD). Stops with the highest proportions of non-zero dwell time also had the highest dwell-time magnitudes and variability. For most stops, dwell-time data was closely fitted by inverse Gaussian, log-normal, power log-normal, Fisk (log-logistic), and Johnson’s SU distributions. The second part of the study used a simulation environment to evaluate the impact of dwell-time magnitude and variability on TSP performance at both far-side and near-side bus stops. For far-side bus stops, dwell time significantly impacted the bus arrival profile at the check-in detector and thus the selected TSP strategy. Higher dwell-time magnitudes and variability led to a higher share of an Early Green Phase (EG) which is not as effective as a Green Phase Extension (GE). At near-side bus stops, dwell-time variability induced more uncertainty in an estimated time of arrival (ETA) and significantly reduced TSP effectiveness especially for GE. TSP performance in terms of GE success, bus travel time, and side-street traffic delay was significantly better at far-side stops compared to near-side stops.

Transit Signal Priority (TSP) is an advanced control mechanism to facilitate transit vehicle operations along signalized arterials. In practical application, TSP systems are integrated with coordinated signal control strategies. People take for granted the reduced transit delays through TSP treatments. However, due to conflicts between TSP control schemes and coordinated signal control strategies along signalized arterials, the benefits achieved by transit vehicles may be washed out to some extent. Our recent study for optimizing the South Snohomish Regional Transit Signal Priority (SS-RTSP) system operations found that transit vehicle delays over a signal-coordinated corridor can be lengthened by TSP treatments in some scenarios. A VISSIM-based simulation model is developed to emulate TSP system operations along the SR-99 arterial covering 13 intersections, in the City of Lynnwood, Washington. Various transit operation scenarios under diverse coordinated signal control plans are designed. Theoretical analysis is also provided to formulate the transit travel process. The research findings indicated that to achieve the best operational efficiency, the compatibility between TSP control schemes and signal control coordination should be strengthened to minimize transit disruption to signal coordination.

Analytical and simulation approaches to understand combined effects of transit signal priority and road-space priority measures

This report is a limited scope evaluation of the MetroRapid's first two years of operation. The MetroRapid represents Hillsborough Area Regional Transit's first foray into bus rapid transit. Built at a total cost of $34.75 million, or $1.98 million per mile, it is at the low end of the cost spectrum for BRT projects. MetroRapid includes several key BRT features such as branding of the stations and buses, increased station spacing, ticket vending machines at 12 of the 59 stations, and transit signal priority (TSP). The MetroRapid averaged 48,666 riders per month in its first two years. Total annual ridership grew 3 percent between the first and second year, which was the same growth rate as the rest of HART's fixed route system. Most passengers board and alight between Marion Transit Center and University Area Transit Center. Ridership drops off significantly between UATC and the north end of the line at Hidden River Park and Ride Lot. The MetroRapid is 10 minutes faster than the parallel Route 2. Consequently, many Route 2 customers have switched to the MetroRapid. Despite that, there has been a 10 percent net gain in transit riders on the Nebraska Avenue corridor since the MetroRapid began. Even with the TSP, traffic signal delay accounts for 21 to 24 percent of its end to end travel time. Much of the signal delay is occurring at intersections where the TSP is not activated. 17.

Arterial signal coordinated control system and transit signal priority strategy are widely used for decreasing delay and improving the level of service in the urban areas, therefore, transit signal priority (TSP) optimization under arterial coordinated control condition needs to be focused on for improving urban traffic efficiency. This paper develops an arterial signal coordinated control method regarding TSP scenarios considering passenger delay and transit schedule adherence simultaneously, and a passenger-based MAXBAND graphic method is proposed to a generate coordinated control plan. Consequently, a test arterial segment of Nanjing (in China) is simulated by the VISSIM software platform to evaluate and analyze the performance of the proposed optimization method in decreasing delay and improving system reliability. The results illustrate that the proposed optimization model presents promising outcomes in transit signal priority under signal coordinated control conditions compared with traditional coordinated optimization methods.

Transit signal priority (TSP) is an effective strategy to reduce transit delays and improve intersection efficiency. This paper introduces a Cooperative TSP strategy of Variable phase (CTSPV) using multi-agent reinforcement learning (MARL) to minimize transit schedule delays on arterial roads. The agents adjust phase sequences and durations based on real-time traffic, balancing transit and non-transit vehicle needs, resolving conflicting bus requests, and ensuring agent cooperation. Invalid action masking ensures compliance with green time and phase-skipping rules. Simulation results show CTSPV reduces person delay, queue lengths, and lateness by 8.7%, 31.6%, and 17.0%, respectively, compared to fixed-time signals. Testing different green time constraints highlights the importance of proper restrictions for efficient learning. Analysis of CTSPV's signal timing reveals agents prioritize phases with high traffic demand and bus priority, skipping phases with lower demand. Evaluation results of generalized rule-based strategies based on those RL-derived patterns demonstrate the good performance of RL-learned knowledge.

Chapter 11 - Implementation of Transit Signal Priority and Predictive Priority Strategies in ASC/3 Software-in-the-Loop Simulation

Transit Signal Priority (TSP) makes Public Transport (PT) more competitive to the private car by facilitating the movement of PT vehicles across signalised intersections. However, the acceptance of TSP is limited, as its application can result in delays to the general traffic. In the future traffic signals will broadcast Signal Phase and Timing (SPaT) status information to nearby vehicles. Thanks to this, TSP could be complemented with in-vehicle systems such as Green Light Optimal Speed Advisory (GLOSA) and Green Light Optimal Dwell Time Advisory (GLODTA). They allow a bus to pass through signalised intersections during a green phase, without modifying signal timings. Therefore they do not create delays to the general traffic. The objective of this paper is to analyse how the two systems can complement TSP and to which extend they can be used instead of TSP. Using computational experiments the authors demonstrate that although signal priority provides greater travel time improvement, the two advisory systems offer similar advantages in terms of reducing the number of stops and delay time at traffic signals.

Intersections are considered as the most critical parts of the bus rapid transit (BRT) system. Transit signal priority is one of the efficient solutions to reduce BRT fleet delays at intersections. The aim of this study is to propose a new algorithm to decrease the BRT fleet delays at actuated intersections, while reducing the negative impacts on different approaches. The adaptive strategy is applied in this study. In the proposed algorithm, named TSPAT (Transit Signal Priority for Actuated Timing), intersection phasing is rescheduled, based on traffic conditions such as phase conditions at the time of bus arrival, the queue length of other approaches, and prioritization record in a specific time length. To assess the merits of the proposed algorithm, a before-after study is executed by applying VISSIM traffic simulation software for an actuated intersection in Isfahan city, Iran. The simulation results show that by applying the algorithm, the average delay of BRT fleets is declined by 21 % and 51% in peak and off-peak hours, respectively. Furthermore, the average speed of BRT fleets is increased by 26% and 78%, during peak and off-peak hours, respectively. The utilization of TSPAT algorithm can improve the desirability of the public transportation system along the BRT routes.

During an urban evacuation, is it advisable for regional planners to allow transit buses signal priority in cases where police-assisted traffic controls are not an option? With only a finite number of available units, buses will be required to make multiple trips in and out of evacuation zones. Therefore, it is within reason that some regional municipalities would want to allow transit priority to hasten trips made by buses. However, studies in the past have shown that, during times of high roadway demand, transit priority causes major delays for vehicular traffic. The goal of this paper is to examine and quantify the benefit of transit signal priority will have on transit vehicles and any hindrance this priority has on nontransit evacuees. By applying state-of-the-art tools in evacuation modeling and microscopic traffic simulation, the aspects of a multimodel evacuation and transit signal priority impact study are merged within a single study of Washington, DC. On the basis of simulation results, transit signal priority has the potential to save lives by reducing bus travel time by 26%. This signifies that three prioritized buses can handle the workload of four nonprioritized buses. Furthermore, this priority has no adverse effect on the evacuation clearance time of nontransit evacuees. On the basis of this study, transit priority in a multimodal evacuation should be used.