TSP-Friendly Underlying Traffic Signal Control: An Essential Complement to Transit Signal Priority

Queue jumper lanes are a special type of bus preferential treatment that allows buses to bypass a waiting queue through a right-turn bay and then cut out in front of the queue by getting an early green signal. The performance of queue jumper lanes is evaluated under different transit signal priority (TSP) strategies, traffic volumes, bus volumes, dwell times, and bus stop and detector locations. Four TSP strategies are considered: green extension, red truncation, phase skip, and phase insertion. It was found that queue jumper lanes without TSP were ineffective in reducing bus delay. Queue jumper lanes with TSP strategies that include a phase insertion were found to be more effective in reducing bus delay while also improving general vehicle operations than those strategies that do not include this treatment. Nearside bus stops upstream of check-in detectors were preferred for jumper TSP over farside bus stops and nearside bus stops downstream of check-in detectors. Through vehicles on the bus approach were found to have only a slight impact on bus delay when the volume-to-capacity (v/c) ratio was below 0.9. However, when v/c exceeded 0.9, bus delay increased quickly. Right-turn volumes were found to have an insignificant impact on average bus delay, and an optimal detector location that minimizes bus delay under local conditions was shown to exist.

Transit signal priority (TSP) has gained popularity in providing public transportation buses with preferential treatment at signalized intersections. Many studies have addressed its implementation in prompting enhanced public transportation service, such as reducing person delay and reducing transit travel time. However, most TSP implementations are done at the intersection level. Only a few studies have addressed the problem of integrating signal priority in coordinated real-time traffic signal control systems. A particular problem in this case is the uncertainty of predicting transit movements when considering the variability of dwell times at service stops. This study presents the development of a real-time traffic signal control integrating traffic signal timing optimization and TSP control using genetic algorithms (GA) and artificial neural networks (ANN) modeling. The GA is used to find near-optimal signal timings. Six different signal control systems were evaluated: fixed-time control with and without standard TSP, actuated signal control with and without standard TSP, real-time GA-based control without TSP, and real-time GA-based with advanced TSP logic. The standard TSP is implemented at the intersection level, by providing either early green (red truncation) or green extension strategies whenever a bus exists. A traffic signal control system that incorporates GA to optimize the fitness function and ANN for transit travel time prediction is developed. A microscopic simulation environment using VISSIM 4.3 simulation environment is used to test the previously mentioned six traffic control systems. The simulation results show that the proposed control system can reduce transit vehicle delay and improve schedule adherence. The reductions in delay and schedule adherence are statistically significant.

A dynamic coordination strategy model of the transit signal priority and traffic signal control cooperative system is created by using Colored Time Petri Nets (CTPNs). To extend the coordination from isolated intersections to the arterial network of intersections, a multi-level and multi-group computer supported cooperative system is described based on CTPNs. Then inter-intersection and cross-intersection coordination strategy for busses and vehicles was proposed to model transit signal priority in urban networks. INTRODUCTION Traffic management systems address the problem of reducing congestion, vehicle delay time, fuel consumption, and pollution. The most common technique to regulate and manage urban traffic areas and surface street networks is traffic signal control (Dotoli and Fanti 2006). Traffic signal control plays a central role in modern urban traffic management (Dotoli et al. 2003). Public transit, with the advantages of large capacity, reduced road occupancy, low fuel consumption and so forth, is considered to be one of the most effective ways to solve urban transportation problems (Han and Liu 2008). Transit signal priority (TSP) is a direct way to improve public transit service levels and attract residents to use public transit. In traffic control systems, general vehicle signal control and transit signal priority control share resources, resource conflicts, a tendency to deadlock and overflow, and require well-planned synchronization and scheduling. They are controlled to achieve satisfactory performance which includes reduced travel time for transit customers, improved schedule adherence and side-street traffic, and the smooth flow of traffic. (Dotoli et al. 2006). Therefore, it is of practical importance to develop, verify and, validate simple, yet powerful models that help to design and improve the safety and efficiency of traffic signal control systems. Petri nets have been proven to be a powerful modeling tool for various kinds of discrete event systems (Chung and Huang 2007). Compared with other analytical and simulation methods, Petri nets have the following advantages (Dos Santos Soares and Vrancken 2007; Wang et al. 1999): 1) Petri nets can easily express concurrency, competition, and synchronization activities among traffic, TSP and traffic control 2) Petri net modeling allows easy changes to be made to the network configuration, the traffic signal control logic, timing and coordination, and the assumptions on intersection physical layout, vehicle flow rate, turning movement, ICCTP 2009: Critical Issues in Transportation Systems Planning, Development, and Management ©2009 ASCE 1590

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.

Queue jumper lanes are a special type of bus preferential treatment that allows buses to bypass a waiting queue through a right-turn bay and then cut out in front of the queue by getting an early green signal. The performance of queue jumper lanes is evaluated under different transit signal priority (TSP) strategies, traffic volumes, bus volumes, dwell times, and bus stop and detector locations. Four TSP strategies are considered: green extension, red truncation, phase skip, and phase insertion. It was found that queue jumper lanes without TSP were ineffective in reducing bus delay. Queue jumper lanes with TSP strategies that include a phase insertion were found to be more effective in reducing bus delay while also improving general vehicle operations than those strategies that do not include this treatment. Nearside bus stops upstream of check-in detectors were preferred for jumper TSP over far-side bus stops and nearside bus stops downstream of check-in detectors. Through vehicles on the bus approach we...

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.

Most of the previous studies on Transit Signal Priority (TSP) have assessed benefits of transit and impacts on other road users. However, the typical detection method of TSP contains two steps: Check-in and Check-out. A new mechanism of multi-step detection (Advance call 1, Confirmation, advance call 2, and Check-out) to expand the signal timing adjustment scope for near-side bus stops is presented. Four models with multi-step detection are employed: No TSP, TSP, TSP with Phase Rotation (PR), and Custom TSP. The results show that TSP, TSP with PR and custom TSP can be considered for implementation in different scenarios. TSP with PR can provide significant benefits for bi-direction transit, and is especially suitable for trams in terms of average person delay. This study contains a number of instructions on how the described control algorithms based on logic rules can be implemented in traffic controllers.

Active transit signal priority (TSP) is used more conveniently and widely than the other strategies for real-world signal controllers. However, the active TSP strategies of real-world signal controllers use the first-come-first-served rule to respond to any active TSP request and are not effective at responding to the number of bus arrivals. With or without the green extension strategy, the active TSP has little impact on the final green time of priority phase, even in the case where more buses arrive during the priority phase. The reduced green time of early green strategy is relatively large when a bus arrives, and it would be worse when more buses arrive, the active TSP has a big adverse impact on the final green time of the non-priority phase. Therefore, the active TSP strategies of real-world signal controllers cannot handle the downtown intersection where many bus lines converge or where many buses arrive in a signal cycle during the evening rush hour. Traffic engineers need to do much work to optimize the TSP parameters before field application. Consequently, it is necessary to improve the TSP strategy of the real-world signal controllers for the intersections with a lot of bus arrivals. In order to achieve that objective, the authors present the CNOB (cumulative number of buses) TSP strategy based on the Siemens 2070 signal controller. The TSP strategy extends the max call time according to the number of buses in the arrival section when priority phases are active. The TSP strategy truncates the green time according to the number of buses in the storage section when non-priority phases are active. The experiment’s result shows that the CNOB TSP strategy can not only significantly reduce the average delay per person without using TSP optimization but can also reduce the adverse impact on the general vehicles of non-bus-priority approaches for the intersections with a lot of bus arrivals. Additionally, because the system dynamically adjusts, traffic engineers do not need to do much optimization work before the TSP implementation.

Transit signal priority accommodating conflicting requests under Connected Vehicles technology

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.

This paper presents the findings of a simulation study evaluating the potential benefits of implementing transit signal priority (TSP) combined with arterial signal coordination for an isolated intersection. Traffic signal coordination is usually implemented along corridors with bus lanes. Active transit signal priority (active TSP) is a traffic-responsive control that prioritizes transit vehicles at signalized intersections. Thus, implementing active TSP under a stable cycle length is necessary to meet the relative demand of the non-priority phase and to maintain system stability. A real key intersection on an artery is taken as the object, and TSP controlling logics with specific restrictions are realized by using the VISSIM vehicle actuated programming module. Simulation analysis reveals the effect of TSP strategies with flow variation on the optimal cycle, and also identifies a reasonable method for selecting the gap time and initial green time of the priority phase. Results show that under special flow combination, increasing the cycle time generated by the traditional transportation and road research laboratory approach can give rise to additional benefits. The volume influences both the gap time and initial green time of the TSP phase. Moreover, the efficiency of red truncation is slightly better than that of the green extension strategy.

Estimating the impacts of transit signal priority on intersection operations: A moving bottleneck approach

Bus delay reduction is an essential input to the objective evaluation of the performance of Public Transport Priority (PTP) measures. Functions for estimating bus delay reduction associated with various types of PTP measures can be used to optimise priority and signal control, or used as a planning tool when more expensive simulation is not available. However, existing delay functions for evaluating PTP measures, such as Dedicated Bus Lanes (DBLs) or Transit Signal Priority (TSP), tend to ignore delay associated with bus acceleration. Moreover, there is no study dedicated to developing bus delay functions to evaluate the performance of Queue Jump Lanes (QJLs) or QJLs combined with TSP. This paper proposes delay functions to analytically estimate bus delay effects for a range of PTP measures, including DBLs, QJLs, TSP, TSP combined with QJLs, and TSP combined with DBLs. Kinematic Wave Theory (KWT) is used to estimate queuing delays at traffic signals. Delay associated with bus acceleration is also analytically approximated. The proposed bus delay functions are validated using traffic micro-simulation, which indicates that the proposed functions accurately estimate bus delay effects of the considered PTP measures with small errors.

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.

Transit Signal Priority (TSP) is a broadly used traffic signal control strategy designed for reducing transit delays at signalized intersections. Although recent TSP systems began to consider more objectives, TSPs that addressed transit reliability issues commonly focused on improving schedule adherence and were only able to reduce schedule delays by expediting buses. Buses running ahead of the schedule were not considered. This paper proposed a dual-objective two-way TSP algorithm (D2 TSP) using Deep Reinforcement Learning (DRL). D2 TSP concurrently optimizes transit delays and reliability (i.e., headway adherence) by expediting late buses or delaying early buses. Further, the DRL agents were enhanced with a coordination algorithm for an optimized solution balancing opposite directions. This D2 TSP reacts adaptively and efficiently to real-time bus performance using data provided by readily available technology (loop detector) at low communication frequencies. We trained and tested this algorithm in a stochastic microsimulation environment in Aimsun Next that modelled a transit route segment with reliability issues in the City of Toronto. The performance of D2 TSP was compared with four baseline scenarios, one without TSP, one with the current TSP algorithm used in the field in the City of Toronto, one conditional TSP with an arrival prediction model, and one using DRL agents with a First-Come-First-Served logic. D2 TSP demonstrated its advantages in providing an efficient and balanced solution in reducing headway variability and travel time for both directions.