Advanced Transit Signal Priority Control with Online Microsimulation-Based Transit Prediction Model

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.

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.

This paper examines quantitatively the effect of modeling assumptions on transit signal priority (TSP) control in mixed mode operation. The primary sources of delays to buses at intersections are queues formed at intersections (queuing delay) and traffic signals (signal delay). We show that models based on point queues and models based on spatial queues would lead to different control strategies and yield different results for the benefit to buses in terms of delay reduction. Situations in which the spatial content of queues is crucial to modeling TSP systems are identified in the paper. We conclude that given that models with the spatial queue representation are more realistic in capturing the queuing dynamics at intersections they should be incorporated in the decision‐making process for initiating priority calls in a TSP system.

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

Transit signal priority (TSP) is the most effective method to improve the operational efficiency of public transports. This paper presents an adaptive dynamic TSP control method of public traffic, by adjusting the signal time to give buses passing priority at intersections, which can take into account not only the dynamic time of bus's reaching the intersections according to the actual road conditions, but also the benefit balance of priority and non-priority vehicles. This control method has been successfully piloted in BRT Line 1 in Guangzhou, China. The average BRT vehicles' travel time under each signal priority operation is reduced by about 34 seconds, and the average delay time is decreased about 10% while it causes only acceptable negative impact on non-priority social vehicles. These pilot results demonstrate the validity and effectiveness of the control algorithm.

Giving priority to public transport vehicles at traffic signals is one of the traffic management strategies deployed at emerging smart cities to increase the quality of service for public transit users. It is a key to breaking the vicious cycle of congestion that threatens to bring cities into gridlock. In that cycle, increasing private traffic makes public transport become slower, less reliable, and less attractive. This results in deteriorated transit speed and reliability and induces more people to leave public transit in favor of the private cars, which create more traffic congestion, generate emissions, and increase energy consumption. Prioritizing public transit would break the vicious cycle and make it a more attractive mode as traffic demand and urban networks grow. A traditional way of protecting public transit from congestion is to move it either underground or above ground, as in the form of a metro/subway or air rail or create a dedicated lane as in the form of bus lane or light rail transit (LRT). However, due to the enormous capital expense involved or the lack of right-of-way, these solutions are often limited to few travel corridors or where money is not an issue. An alternative to prioritizing space to transit is to prioritize transit through time in the form of Transit Signal Priority (TSP). Noteworthy, transit and specifically bus schedules are known to be unstable and can be thrown off their schedule with even small changes in traffic or dwell time. At the same time, transit service reliability is an important factor for passengers and transit agencies. Less variability in transit travel time will need less slack or layover time. Thus, transit schedulers are interested in reducing transit travel time and its variability. One way to reach this goal is through an active intervention like TSP. In this chapter a comprehensive review of transit signal priority models is presented. The studies are classified into different categories which are: signal priority and different control systems, passive versus active priority, predictive transit signal priority, priority with connected vehicles, multi-modal signal priority models, and other practical considerations.

Global positioning system (GPS)-based transit signal priority (TSP) has advantages in providing flexible and conditional signal priority to transit vehicles. This study compared the effectiveness of different combinations of operational patterns and TSP strategies and assessed their impacts on side-street traffic operations. Micro-simulation analytical tools were applied to two testbeds of Utah Transit Authority’s Bus Route 33 and bus rapid transit (BRT) Line 35 MAX. Eight scenarios were created for Route 33 that included the existing conditions and combinations of regular/BRT operational patterns with traditional TSP, GPS-based TSP, conditional TSP implementations. Three scenarios were created for 35 MAX, also considering the existing traditional TSP condition, GPS-based TSP, and conditional TSP implementations. Results confirmed similar operational effectiveness of GPS-based TSP when compared to traditional TSP. Conditional TSP had smaller impacts on side-street traffic while providing transit vehicles considerable amounts of delay reduction and travel time saving.

Transit signal priority (TSP), which has been deployed in many cities in North America and Europe, is a traffic signal enhancement strategy that facilitates efficient movement of transit vehicles through signalized intersections. Most TSP systems, however, do not work well in transit networks with nearside bus stops because of the uncertainty in bus dwell time. Unfortunately, most bus stops on U.S. arterial roadways are nearside ones. In this research, weighted-least-squares regression modeling was used to estimate bus stop dwell time and, more important, the associated prediction interval. An improved TSP algorithm that explicitly considers the prediction interval was developed to reduce the negative impacts of nearside bus stops. The proposed TSP algorithm was tested on a VISSIM model of an urban arterial section of Bellaire Boulevard in Houston, Texas. In general, it was found that the proposed TSP algorithm was more effective than other algorithms because it improved bus operations without statistically significant impacts on signal operations.

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.

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.

Multi-modal traffic signal control with priority, signal actuation and coordination

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.

Urban traffic congestion remains a pressing challenge, requiring integrated signal control and lane management. This study proposes a strategy that combines a rule-based dynamic shared bus lane (DSBL) controller with modified NEMA signal control featuring transit signal priority. DSBL permits general vehicles to use the bus lane based on demand, while the signal controller adjusts phase timings to serve buses and maintain intersection efficiency. An enhanced cell transmission model captures intersection flow dynamics and supports coordinated operation. Simulations at a single intersection under eight saturation levels show that the proposed strategy reduces delay and congestion, with the largest gains under oversaturated conditions, compared with intermittent bus-priority lanes, DSBL with fixed-time signals, and dedicated bus lanes with fixed-time signals. Dynamic lane sharing consistently outperforms non-sharing schemes. Sensitivity tests further indicate that larger bus headways and longer approach links can reduce total passenger delay.

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.

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.