Transit Signal Priority Optimization under Arterial Signal Coordinated Control Conditions

Transit signal priority has a positive effect on improving traffic condition and level of transit service in the urban area. In this paper, a passenger-based transit signal priority (TSP) optimization model is formulated to optimize intersection signal phasing based on minimizing accessibility-based passenger delay at the intersection and increased waiting-delay at the downstream bus stop simultaneously. Genetic Algorithm is utilized to calculate passenger-based optimization model that is calibrated by evening rush hour actual traffic data (17:30–18:30, October 13th–October 15th, 2015) along Shuiximen Boulevard in Nanjing, China. The performance of the proposed optimization model in decreasing delay and improving system reliability is simulated and evaluated by VISSIM-based simulation platform, and the results illustrate that the proposed optimization model presents promising outcomes in decreasing accessibility-based passenger delay at intersection (average reduction of 12%) and passenger waiting-delay at downstream bus service stop (average reduction of 18%) compared with traditional vehicle-based TSP optimization method in rush hour.

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.

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.

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.

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.

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 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.

An analytical model to conduct a person-based evaluation of transit preferential treatments on signalized arterials

Public bus transit travel time is affected by many factors, including traffic signals and traffic condition. Transit agencies have implemented transit signal priority (TSP) strategies to reduce transit travel time and improve service reliability. However, due to the lack of empirical data, these factors' collective impact and bus travel time strategies have not been studied at the stop-to-stop segment level. This research focuses on the factors affecting travel time reliability, emphasising the variability between operators and the policy implications of such differences. One-way analysis of variance (ANOVA) statistical methods have been used to assess the quality implications of public bus transportation time reliability. This research seeks to investigate the factors affecting the travel time (TT) reliability of bus transport. Studies were conducted along three bus routes serving different areas. Factors strongly related to TT reliability include route length, number of signalised intersections, day of the week, bus stops, departure delays, bus lane, passenger boarding and alighting, weather condition, and fare structure. Based on the proposed model factors affecting TT reliability, it was found that TT is strongly affected by the number of bus stoppings and also the length of the route. The reliability of all three routes during the weekday is low because of delays in departure. The number of signalised intersections along the route affects reliability. Meanwhile, more passengers boarding and paying cash increased the travel time reliability of buses.

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.

This article studied bus signal priority (BSP) strategies at road intersections containing bus lane through simulation. Based on cellular automata (CA) theory, the coordination of single-phase buses and cars under different BSP control strategies is studied and the “speed-distance” condition is introduced in transit signal priority, with which two indicators for evaluation considered resource constraints are proposed, namely, fuel consumption and exhaust emissions. The effect on the consumption of general vehicles from bus signal priority is shown by CA simulation, and the judgment of the bus priority level is determined by the calculation of fuel consumption and exhaust emissions.

ABSTRACTConventional Transit Signal Priority (TSP) controls often reach the limitation for arterials accommodating heavy bus flows since the priority function can significantly increase delay at minor streets. Under such conditions, a proper signal progression plan that accounts for the benefits of buses may offer the potential to improve the reliability of bus operations and increase the bus ridership. This study proposes a bus-based progression model to reduce the delay of buses on local arterials. Given the cycle length and green splits at each intersection, the bus-based progression model, grounded on the same notion as conventional signal progression methods, considers the operational characteristics of transit vehicles, such as the impact of bus dwell time and the capacity constraints at bus stops. Also, to deal with the stochastic nature of dwell time, this study introduces additional constraints to maximize the percentage of buses which can stay within the green band after leaving bus stops. Taking an arterial with five intersections and three two-way bus stops as an example, this study applies VISSIM as an unbiased tool for model evaluation. The simulation results demonstrate that the proposed model can significantly reduce bus passenger delays and the average person delays for the entire arterial, compared with the conventional progression models.

How should customer delay on a rail transit system be measured? Research into quantifying reliability and passenger delay often defines “delay” as when speeds or travel times fall above or below a fixed threshold. When applied to a rail transit system, however, this technique may not fully capture the impacts on all transit riders. This paper proposes a new way to assess passenger delay by leveraging the power and richness of transaction-level fare system data not always available in other contexts. This method enables a true understanding of the full spectrum of the impact on transit passengers by an analysis of the entire distribution of passenger travel times rather than by a simple tally of individuals with travel times above a threshold. This method—comparing the cumulative distribution of travel times—combines concepts from traffic queueing theory and travel time reliability research in both the transit and the highway arenas. The proposed method was applied to fare system data from the Washington, D.C., Metropolitan Area Transit Authority’s Metrorail system to evaluate the delays that arose from using different strategies for providing continued service during rail system rehabilitation.

Transit Signal Priority (TSP) strategies are widely used to reduce bus travel delay and improve bus service performance. State-of-the-art TSP strategies help buses cross intersections without stopping, either by green extension or red truncation, and enable adaptive TSP plans that reflect real-time traffic conditions. Among all existing adaptive TSP strategies, there are two types of approaches: 1) objective function-based optimization; 2) logic and (or) rule-based optimization. This thesis develops an adaptive TSP strategy via the objective-function approach. The key contributions include an accurate bus delay estimation model, which implements an adaptive TSP strategy into a programming problem, and an adaptive TSP simulation platform, which uses a full-scale signal emulator, ASC/3, in VISSIM. A case study in VISSIM is conducted to evaluate the proposed adaptive TSP strategy versus conventional TSP strategies. Finally, the proposed TSP is compared with previous studies to investigate advantages and disadvantages.