Conventional Transit Signal Priority Research Articles

Transit Signal Priority under Connected Vehicle Environment: Deep Reinforcement Learning Approach

Transit Signal Priority (TSP) is a traffic signal control strategy that can provide priority to transit vehicles and thus improve transit service and enhance transportation equity. Conventional TSP strategies often ignore the fluctuation of passenger occupancy in transit vehicles, leading to sub-optimal solutions for the entire system. The use of Connected Vehicle (CV) technology enables the adoption of a more fine-grained objective in optimizing traffic signals, such as person delay, by allowing real-time information on passenger occupancy to be obtained. In this study, a deep reinforcement learning algorithm, deep Q-network (DQN), is applied to develop a traffic signal controller that minimizes the average person delay. The proposed DQN controller is tested in a simulation environment modeled after a real-world intersection and compared with pretimed and actuated controllers. Results show that the proposed DQN controller has the best performance in terms of average person delay. Compared to the baseline, it reduces the average person delay by 18.77% in peak hours and 23.37% in off-peak hours. Furthermore, it also results in decreased average delays for both buses and cars. The sensitivity analysis results indicate that the proposed controller has the potential for practical applications, as it can effectively handle some dynamic changes.

A Collaborative Transit Signal Priority Method Considering Gaming among Traffic Participants

How to realize the game equilibrium between bus and nontransit vehicle is a hot topic in the field of transit signal priority (TSP). To this end, a collaborative transit signal priority (Co-TSP) method is proposed. The core of Co-TSP is a two-objective optimization problem which takes the expected delays of buses and the average delays of nontransit vehicles as the objectives. Different from previous studies, Co-TSP uses game theory to realize collaborative optimization, instead of transforming the problem into a single objective optimization problem by weighting. A finite state machine-based algorithm is developed to estimate the average delays of nontransit vehicles. The stochasticity of bus arrival time is also considered in the estimation of bus delays to improve the robustness. Candidate timing plans obtained by the nondominated sorting genetic algorithm (NSGA) are divided into three priority levels based on the delays of buses. The final timing plans can be picked intuitively from the candidates by rules representing expert knowledge and demands to control the priority level. Co-TSP guarantees theoretically by preliminary screening that the expected delays of bus after optimization must be no higher than that before optimization. Simulation experiments are conducted in Shanghai, China, to verify the performance. Results show that Co-TSP reduces the delays of buses by 27.7%∼41.0% and still performs well under low and high congestion levels, while the conventional TSP (CTSP) fails in some cases. Priority control proves to be effective at last. The research provides a new idea for the benefit allocation among participants at intersections.

New signal priority strategies to improve public transit operations in an urban corridor

By facilitating public busesʼ movement through traffic signal-controlled intersections, a transit signal priority (TSP) strategy can contribute to the reduction of queuing time at intersections. However, the traditional TSP also has a significant downside; it is detrimental to non-prioritized movements and other transport modes. This research proposes new TSP strategies that account for optimization for the person delay at an intersection along a corridor. This research focused both on major arterial roads and minor arterial roads, whereas the majority of the publications only addressed the major arterial approach. As part of this research, the bus schedule was also taken into consideration, whereas in other studies, the measure of giving priority was only the approach to the intersection. The proposed method is described in detail and also is implemented on arterial corridor in VISSIM. The study area simulation results indicated that the proposed TSP methods performed better than the conventional TSP.

Evaluation of transit signal priority at signalized intersections under connected vehicle environment

ABSTRACT Conventional transit signal priority (TSP) control strategies generally have negative impacts on other traffic. In this study, two typical signal control strategies under connected vehicle (CV) environment that give priority to transit vehicles at signalized intersections are investigated, i.e. actuated TSP with CV and optimized TSP with CV. The optimization algorithm used in this study is genetic algorithm (GA) and the goal of the optimization is to minimize the total person delay. A real-world intersection is modeled in a microsimulation environment to evaluate the performance. The results are compared with fully actuated signal control strategies with and without TSP. Results show that the proposed optimization control strategy can reduce the average bus delay by 24.5% during peak hours while minimizing the negative impacts on conflicting traffic. Under the low traffic demand condition, the actuated controller with TSP using CV has a better performance in terms of average delay.

Transit Signal Priority Method Based on Speed Guidance and Coordination Among Consecutive Intersections

Speed and punctuality are essential to the quality of bus services. To reduce bus delays and increase bus speed, a transit signal priority (TSP) method based on speed guidance and coordination among consecutive intersections is proposed. The TSP problem is formulated as a binary mixed integer nonlinear program. Impacts of TSP on the current intersection and adjacent intersection downstream are measured by deviations of split time from background timing plans and non-overlapping degrees, respectively. The weighted sum of the two measurements and bus travel time is taken as the objective function. The method does not change the original cycle length, and it is adaptive to timing plans with an arbitrary number of phases. Exclusive bus lanes are required to provide good conditions for speed guidance. A simulation case study of three consecutive intersections in Shanghai, China, is conducted. In the experiments, no priority method, the conventional TSP method, and the proposed method are applied. The results indicate that the proposed method performs the best. Compared to no priority method, the average travel time of buses, delays of bus, and delay per capita are reduced by 26.3%, 91.3%, and 14.5%, respectively. In addition, no serious deterioration is observed in the experience of other road users as the congestion level rises. The study illustrates the possibility of giving high priority to buses without significant negative impacts on other road users, and it can help traffic managers to alleviate traffic congestion in densely populated cities.

Regional Coordinated Bus Priority Signal Control Considering Pedestrian and Vehicle Delays at Urban Intersections

Bus priority signal control can reduce bus delay at intersections and improve bus operation efficiency. The existing research focus mainly on bus priority control methods at isolated intersections, as well as urban arterial corridors. Rarely existing works consider a network-level bus priority control. Treating regional-wide intersections can be challenging, given that more traffic directions and large multi-modal traffic volume should be considered. The conventional transit signal priority (TSP) research focus on the delay of buses and social vehicles and neglect the delay of pedestrians. To fill these gaps, this paper proposes a regional coordinated bus priority signal control (RCBPSC) method, which is a network-level bus priority control method considering pedestrian and passenger delays. This method is divided into two stages. The first stage is the regional coordinated signal control to obtain the basic signal timing schemes. The second stage is the bus priority signal control. At this stage, the timing schemes at each intersection will be adjusted according to the bus arrival time and the delays. In order to verify the effectiveness of this method, we choose four intersections in Chengdu to study. The results show that the total delay of the proposed method at the first stage (control case 1) can be reduced by 602s (2.3%) and 606s (3.7%) comparing with the conventional timing method in the peak and non-peak period. At the second stage, the proposed method can reduce more pedestrian delay than the conventional TSP method in both <xref ref-type="other" rid="other1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scenario 1</xref> and <xref ref-type="other" rid="other2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scenario 2</xref> .

Design of Traffic-Signal Progression for the Modern Streetcar: Case Study of Qilin Line One, Nanjing, China

Mass streetcars operating record data used in a proper way can reveal the operational characteristics of streetcars to the full extent. This paper aims to design the traffic-signal progression for the modern streetcar by mining and analyzing these data. Each unit in the study consists of two adjacent signalized intersections. The k-means clustering algorithm is used to cluster the streetcar travel time data between two intersections and to determine whether the streetcar has stopped waiting for a red light at an intersection or not. An offset calculation model for streetcars is proposed to lower the impact of the conventional transit signal priority (TSP) for streetcars on motor vehicles. A real-world case study of Line One of the Nanjing, China, Qilin modern streetcar shows that the traffic-signal progression design proposed in this paper can reduce the conventional TSP’s negative impacts on motor vehicles by about 20%.

Coordinated Transit Signal Priority Model Considering Stochastic Bus Arrival Time

This paper presents an advanced transit signal priority (ATSP) control model that considers bus progression at downstream intersections when giving priority at upstream intersections and stochastic bus arrival times. The ATSP control model is applicable to arterials with bus lanes. At the center of the ATSP control model is a stochastic programming model formulated to find the optimum priority strategies at each intersection of interest, which minimize bus delays while having the least negative impact on general traffic. The uncertainty in bus arrivals is taken into account by considering stochastic bus dwell times. The ATSP control is implemented in a traffic micro-simulation environment and compared with conventional transit signal priority (CTSP) control. Extensive simulation experiments are conducted with different traffic congestion levels, bus headway levels, and dwell-time distributions. Results show that the ATSP control generates an additional reduction in bus delay of around 10% when compared to the CTSP control.

Effect of transit signal priority on bus service reliability

As every user knows buses tend to bunch. To alleviate this problem, transit agencies introduce slack into their schedules and then hold buses back to schedule at pre-established control points along their routes. Unfortunately, this practice retards buses and only works with low frequency systems; i.e., when the headways are long. For higher frequency systems, which effectively operate without a schedule, headway-based control strategies show promise but unfortunately, they also retard buses. To alleviate bus retardation in all its forms, transit signal priority (TSP) is commonly used. Curiously however, the potential of TSP to enhance bus control practices has not been explored in detail.This paper evaluates a form of TSP designed for this purpose. In this version of TSP, which we call conditional signal priority (CSP), buses send priority requests only when the requests improve reliability. The evaluation includes both scheduled (low frequency) systems, and unscheduled (high frequency) systems operated by headways. A mathematical model based on Brownian motion is proposed for the former. This model allows us to optimize the CSP strategy and to develop formulas for the expectation and variance of the steady state deviations from the schedule–with CSP, with conventional TSP and with neither. Simulations confirm the accuracy of these findings. It is found that CSP improves reliability considerably and that it is better for traffic than TSP because it reduces the number of priority requests. For high frequency systems operated with a fixed number of buses CSP is shown to not just improve reliability and reduce the number of priority requests of TSP (by about 50%) but also to reduce the average headway by speeding up the buses.

Effect of Transit Signal Priority on Bus Service Reliability

Abstract As every user knows buses tend to bunch. To alleviate this problem, transit agencies introduce slack into their schedules and then hold buses back to schedule at pre-established control points along their routes. Unfortunately, this practice retards buses and only works with low frequency systems; i.e., when the headways are long. For higher frequency systems, which effectively operate without a schedule, headway-based control strategies show promise but unfortunately, they also retard buses. To alleviate bus retardation in all its forms, transit signal priority (TSP) is commonly used. Curiously however, the potential of TSP to enhance bus control practices has not been explored in detail. This paper evaluates a form of TSP designed for this purpose. In this version of TSP, which we call conditional signal priority (CSP), buses send priority requests only when the requests improve reliability. The evaluation includes both scheduled (low frequency) systems, and unscheduled (high frequency) systems operated by headways. A mathematical model based on Brownian motion is proposed for the former. This model allows us to optimize the CSP strategy and to develop formulas for the expectation and variance of the steady state deviations from the schedule-with CSP, with conventional TSP and with neither. Simulations confirm the accuracy of these findings. It is found that CSP improves reliability considerably and that it is better for traffic than TSP because it reduces the number of priority requests. For high frequency systems operated with a fixed number of buses CSP is shown to not just improve reliability and reduce the number of priority requests of TSP (by about 50%) but also to reduce the average headway by speeding up the buses. This paper has been accepted for a podium presentation at the 23rd International Symposium on Transportation and Traffic Theory (ISTTT23) July 24-26, 2019 in Lausanne, CH.

OPTIMIZING PHASE COMPRESSION FOR TRANSIT SIGNAL PRIORITY AT ISOLATED INTERSECTIONS

Transit signal priority (TSP) is a promising low-cost strategy that gives preferential treatments for the buses to go through intersections with minimum delay time. In this paper, a new TSP control model was presented for isolated intersections to minimize bus delay and to reduce the impact of TSP on other vehicles by optimizing signal control phase selection and compression. This paper starts with the phase selection and compression strategies to provide treatments to bus priority requests. Then, two new features on phase selection and compression aspects are applied to TSP, i.e. the time that a bus priority request needs is provided by the phase(s) with the lowest traffic volume, and multi-phases can be selected to serve a bus request. Field data are collected from a major traffic corridor in Changzhou (China) and applied for VISSIM simulation. The proposed TSP control model as well as the fixed-time control and the conventional TSP control models are tested and compared under different traffic demands, headways and maximum saturation degrees. The comparative results showed that the proposed model outperformed the conventional TSP control model in terms of reducing bus delay, minimizing the impact on other vehicles and reducing the stop rate for buses. This paper reveals that, the proposed TSP strategy can significantly optimize the phase compression process and improve transit efficiency.

A coordinated signal priority strategy for modern trams on arterial streets by predicting the tram dwell time

The modern tram has been recognized as a popular and efficient mode of public transit, and the signal priority for trams, particularly coordination on arterial streets, is a crucial and feasible strategy for improving the operation efficiency and level of service of trams. Different from the traditional “green wave” method, the fluctuations in dwell time at stops should be considered, and sacrifices to the automobile right-of-way must be controlled. Accordingly, a Support Vector Machine (SVM) model was proposed to predict the dwell time of trams at stops, which determines tram arrival times at the stop line. A two- sequential programming model to determine signal timings, including offsets and reallocations of green time, was then established. The upper level programming aims to minimize the stop frequency of a tram at the coordination region, and then, the optimal solution becomes the new restriction of the lower programming, whose objective is to minimize the overall delay of automobiles. This strategy was evaluated using the microscopic simulation software VISSIM using Tongjiang Road in Changzhou as the study region. The findings demonstrate that this strategy can significantly reduce more tram travel delays and stop rates on arteries than the conventional Transit Signal Priority (CTSP), and it definitively outperforms and the Static Two-direction Green Wave (STGW) method in decreasing these indicators of automobiles driving along arteries. Moreover, the satisfactory decreases in delay and average queue length at each intersection reflect a remarkable reduction or even elimination of negative effects from tram signal priority (tram SP). Finally, the stability of the proposed strategy in promoting both tram and automobile travel efficiency was proven by traffic volume sensitivity tests. The results and findings are meaningful for traffic managers to enhance the operation efficiency and attractiveness of trams by flexible signal control strategies. In addition, the conflicts between automobiles and tram SP can be successfully solved.

Transit signal priority accommodating conflicting requests under Connected Vehicles technology

In this research, a person-delay-based optimization method is proposed for an intelligent Transit Signal Priority (TSP) logic that resolves multiple conflicting TSP requests at an isolated intersection. This TSP with Connected Vehicles accommodating Conflicting Requests (TSPCV-CR) overcomes the challenge bore by the conventional “first come first serve” strategy and presents significant improvement on bus service performance. The feature of TSPCV-CR includes green time re-allocation, simultaneous multiple buses accommodation, and signal-transit coordination. These features help maximize the transit TSP service rate and minimize adverse effect on competing travel directions. The TSPCV-CR is also designed to be conditional. That is, TSP is granted only when the bus is behind schedule and the grant of TSP causes no extra total person delay. The optimization is formulated as a Binary Mixed Integer Linear Program (BMILP) which is solved by standard branch-and-bound routine. Minimizing per person delay is the objective of the optimization model.The logic developed in this research is evaluated using both analytical and microscopic traffic simulation approaches. Both analytical tests and simulation evaluations compared three scenarios: without TSP (NTSP), conventional TSP (CTSP), and TSP with Connected Vehicles that resolves Conflicting Requests (TSPCV-CR). The measures of effectiveness used include bus delay and total travel time of all travelers. The performance of TSPCV-CR is compared against conventional TSP (CTSP) under four congestion levels and three different conflicting scenarios. The results show that the TSPCV-CR greatly reduces bus delay at signalized intersection for all congestion levels and conflicting scenarios considered. Simulation based evaluation results show that the TSPCV-CR logic reduces average bus delay between 5% and 48% compared to the conventional TSP. The range of improvement corresponding to the four different v/c ratios tested, which are 0.5, 0.7, 0.9 and 1.0, respectively. No statistically significant negative effects are observed.

Predictive–Tentative Transit Signal Priority with Self-Organizing Traffic Signal Control

Reducing bus delay beyond what can be achieved with conventional transit signal priority requires making and responding to longer-range predictions of bus arrival time, which include dwell time at an upstream stop. At the same time, priority decisions based on such uncertain predictions should be reversible if the dwell time should be much longer than expected. Rules for applying these concepts are proposed for application in the framework of self-organizing traffic signal control developed by authors Cesme and Furth. Predicted arrival time is based on a calculation of expected remaining dwell time and is compared with the earliest time the bus phase can be expected to return to green. One possible decision is to expedite return to green so that secondary extensions (a feature of self-organizing control logic) are inhibited. The other is to hold the green; however, this decision can be reversed if updated predictions of expected remaining dwell time indicate that the bus will arrive after the maximum green extension has expired. Simulation tests on a corridor with nine signalized intersections showed a 75% reduction in bus delay, to only 5 s per intersection, with only a 3% increase in general traffic delay.

Transit Signal Priority with Connected Vehicle Technology

Transit signal priority (TSP) has been studied as a control strategy that offers preference to transit vehicles at signalized intersections. Although TSP has been deployed in many places, several shortcomings, such as adverse effect on side streets and uncertainty about the benefit, have been identified. Therefore, a new TSP logic proposed to overcome these shortcomings takes advantage of the resources provided by connected vehicle technology, including two-way communications between buses and the traffic signal controller, accurate bus location detection and prediction, and number of passengers. The key feature of the proposed TSP logic is green time reallocation, which moves green time instead of adding extra green time. TSP is also designed to be conditional. That is, delay per person is used as the most important criterion in deciding whether TSP is to be granted. The logic developed in this research was evaluated in two ways: with analytical and microscopic simulation approaches. In each evaluation, the proposed TSP was compared with two scenarios: no TSP and conventional TSP. The analysis used bus delay and per person delay of all travelers as measures of effectiveness. The simulation-based evaluation results showed that the proposed TSP logic reduced bus delay between 9% and 84% compared with conventional TSP and between 36% and 88% compared with the no-TSP condition. The range of improvement corresponding to four volume-to-capacity ratios was tested. No significant negative effects were caused by the proposed TSP logic.

Development and Evaluation of Adaptive Transit Signal Priority Control with Updated Transit Delay Model

Transit signal priority (TSP) strategies are widely used to reduce bus travel delay and to increase bus service reliability. State-of-the-art strategies enable dynamic (and optimal), rather than predetermined, TSP plans to reflect real-time traffic conditions. These dynamic plans are called adaptive TSP. Existing adaptive TSP strategies normally use a performance index (PI), which is a weighted summation of all types of delays, to evaluate each candidate TSP plan and the weights to reflect the corresponding priority. The performance of an adaptive TSP depends on three factors: delay estimation, weights determination, and optimization formulation. In this context, there are three key academic contributions: (a) an enhanced bus delay estimation model based on advance detection, (b) a mechanism to adjust the PI weights dynamically to reflect the changing necessity of TSP under different conditions, and (c) TSP optimization formulated into a quadratic programming problem with an enhanced delay-based PI to obtain global optimization with the use of MATLAB solvers. In addition, an adaptive TSP simulation platform using a full-scale signal simulator, ASC/3, in VISSIM was developed. The optimal TSP plans were granted or were rejected on the basis of TSP events, such as check-in, check-out, and multiple TSP requests. Through a case study in VISSIM, this research found that, compared with conventional active TSP strategies, the new adaptive TSP strategy could further reduce bus travel time while maintaining a better balance of service on non-TSP approaches along a 7.4-km bus corridor in Edmonton, Alberta, Canada.

Performance of the Priority Control Strategies for Bus Rapid Transit: Comparative Study from Scenario Microsimulation Using VISSIM

Bus rapid transit (BRT) has a great potential to improve the service level of transit system and has been implemented in many Chinese cities. However, the priority it can provide to buses has not been explored fully. Therefore, this study mainly investigated two advanced control strategies (signal priority using advanced detection and transit speed control). Signal priority using advanced detection is a strategy which detects one cycle ahead of buses’ arrival in order to adapt a more flexible control algorithm to provide signal priority for buses. Another is transit speed control, which provides priority at intersections for buses by controlling the speed of them and predicting their arrival at certain intersection. These two advanced strategies were modeled and evaluated using simulation software VISSIM and presented better performance than other three scenarios (base case, exclusive bus lane, and conventional transit signal priority). Only the eastbound direction would be researched as its traffic flow and bus volume are much larger than those of the other direction. Data used in this model was collected in Yingtan City. It is also shown that both the operation of BRT and the efficiency of private traffic can be much improved by applying the two strategies proposed above.

Predictive Strategy for Transit Signal Priority at Fixed-Time Signalized Intersections: Case Study in Nanjing, China

It is essential to give priority to transit vehicles at signalized intersections in the megacities of China such as Nanjing. This study developed a transit signal priority (TSP) strategy for fixed-time signals with the use of techniques for advanced and continuous vehicle detection and prediction of arrival time. In the proposed model, transit vehicles were traced once they were positioned at one intersection and were advancing to the subject intersection. For instance, the location and the speed of a transit vehicle were measured every few seconds, and the prediction was made simultaneously by a model integrating historic and real-time data. With the predicted arrival time, the length of the transit signal phase would be adjusted to give the green light to the transit vehicle. The proposed strategy was evaluated with VISSIM simulation and compared with conventional TSP strategies (green extension and early green) in a typical roadway and traffic setting of Nanjing. Results indicated that the conventional TSP strategy could hardly result in significant improvement in transit delay and that the proposed strategy significantly reduced the transit travel time. Despite these findings, the adverse impacts on the delay of other road traffic by TSP could not be ignored. Results of the simulation model indicated that the proposed predictive TSP strategy was less disruptive to other traffic and that its performance in nonpeak periods was superior to that in peak periods. The results also suggest that a well-designed algorithm for the prediction of arrival time is the key to success in implementing predictive TSP control.

Development of Dynamic Transit Signal Priority Strategy

Transit signal priority (TSP) is a popular strategy used to enhance the performance of transit systems by modifying the signal control logic to give transit vehicles priority at signalized intersections. Conventional TSP strategies used in most cities have been shown to offer significant benefits by reducing delay of transit vehicles. However, concerns about shortcomings of conventional TSP strategies have limited their application. The main concern is a potential negative impact on cross street traffic. Another concern is the static nature of conventional TSP strategies and the lack of responsiveness to real-time traffic and transit conditions. A dynamic TSP control system has been developed that can provide signal priority in response to real-time traffic and transit conditions. The dynamic TSP system consists of three main components: a virtual detection system, a dynamic arrival prediction model, and a dynamic TSP algorithm. Two case studies are presented to test and compare the dynamic and the conventional TSP systems. A hypothetical intersection is simulated in the first case study, and a proposed light rail transit line is simulated in the second. For both case studies, a virtual detection system was developed in VISSIM, along with a linear travel time arrival prediction model. Finally, a dynamic TSP algorithm was developed to determine what TSP strategy to use and when to apply it. The results show that the dynamic TSP system reduced the total delay of transit vehicles and outperformed the conventional TSP system for reducing transit trip travel time.