Transit Signal Priority Implementation Research Articles

Development of Planning-Level Guidelines for Deploying Transit Signal Priority

Transit Signal Priority (TSP) has been proposed and implemented to reduce transit travel time, provide better schedule adherence, and improve transit efficiency. However, concerns have been raised about causing delays to cross street and left-turn movements. The cost-effective implementation of TSP requires methods to support the decisions associated with the implementation. The objective of this research was to examine the existing TSP guidance and propose enhanced planning-level guidelines for TSP deployment that can be applied to identify intersections where implementing TSP is beneficial. The proposed guidelines take consideration of bus delay, bus frequency, geometric and traffic feasibility, as well as the impacts on other movements. The effectiveness of the proposed guidelines was evaluated in comparison with existing guidelines using a microscopic simulation approach. The simulation results showed that the proposed TSP guidelines produce a better-balanced solution for minimizing both main street bus travel times and delays for other affected movements. The proposed guidelines also help to cut the TSP implementation costs by reducing the number of intersections identified for TSP implementation.

Evaluation of Transit Signal Priority on Arterial Performance - A Comparison between Conventional and Unconventional Intersection Operation

Transit Signal Priority (TSP) shows capability to improve traffic operations through providing transit vehicles a higher priority to cross intersections. However, implementing TSP could impose extra delays to the adjacent traffic. Specifically, TSP imposes extra delay on turning traffic that could be solved by converting the intersections to unconventional intersection. To do so, this paper models assesses and evaluates the possibility for implementing a TSP in arterial roadway in Amman, capital of Jordan, by using real traffic data under different scenarios including conventional and unconventional intersection designs. The evaluation was done using simulation. Simulation results show that the TSP implementation reduces system travel time and improves the overall system delay. Specifically, implementing the combination of TSP with unconventional intersection design reduces corridor travel time, system delay, and queue length. Moreover, simulation results indicated that applying TSP improves individual movements travel time and delay up to 35% per vehicle.

Modeling and optimizing bus transit priority along an arterial: A moving bottleneck approach

Bus operations on arterials are often hindered by traffic signals and car queues. Transit signal priority (TSP) strategies can be used to improve bus operations on arterials. Analytically quantifying the impacts of TSP in mixed traffic, where cars and buses share lanes, is challenging since car queues can slow down buses, while slow-moving buses can create bottlenecks for cars. Furthermore, computational costs increase significantly when considering an arterial with multiple intersections. To tackle these challenges, this paper first develops a dynamic programming framework to model and evaluate TSP along an arterial. Next, the algorithm is utilized to determine the changes to car and bus delays as a result of TSP implementation along an arterial, and the sensitivity of the algorithm to the signal timing plan, bus stop locations and dwell durations, and the bus headway is evaluated. Next, a bi-level optimization framework is proposed to determine the optimal location of TSP implementation along arterials. Finally, the results of integrating TSP with dedicated bus lanes is evaluated. The results suggest that the benefits of TSP largely depend on the signal setting, and bus stop location, and that as the bus headway decreases, the marginal benefit of providing TSP also decreases. Additionally, the results suggest that the specific intersection at which TSP is implemented can play a large role on its operational impacts. Finally, it is found that in some scenarios, the benefits of implementing TSP alone can be larger than implementing dedicated bus lanes alone.

Exploring the effects of passive transit signal priority design on bus rapid transit operation: a microsimulation-based optimization approach

ABSTRACT The Bus Rapid Transit (BRT) system can be a cost-effective alternative to foster public transportation. However, the delay in signalized intersections is critical to the stable progression of BRT operation. Transit Signal Priority (TSP) control has been studied to enhance the BRT operation. Nevertheless, the implementation of TSP may also impact general traffic and induce objection from the public. Upon this challenging aspect, this study focuses on passive TSP control for the BRT system. A microsimulation-based optimization model is developed to coordinate signal offsets along the arterial of BRT operation, seeking to minimize the delay of BRT vehicles without severely affecting the service level for other traffic. We conduct a case study over the Taichung BRT System in Taiwan; it indicates that the passive TSP control can reduce approximately 22% of transit delay while containing moderate impact on other traffic. In addition, the derived control strategies can increase system capacity with satisfactory service reliability.

Determining Optimum Transit Signal Priority Implementation Locations on a Network

Transit signal priority (TSP) can be used to improve bus operations at intersections. However, implementing TSP can often increase the delay of non-transit modes. Therefore, it is necessary to evaluate the effects of TSP both on car and bus operations to determine optimal locations to equip with TSP to improve network operations. To do so, the link transmission model is used to evaluate the travel times of both cars and buses on the network while accounting for dynamic queuing and queue spillover. This method is then used to evaluate different combinations of locations for TSP implementation and to determine the optimal configuration that can minimize the total travel time of network users, including bus and car passengers. The sensitivity of the proposed algorithm to demand level, changes in transit network, implementation strategy, and solution method are also evaluated. For all tested scenarios, the TSP configurations found to be optimum achieve a significant reduction of total bus passenger travel time while creating minimal effect on total car travel time. The results reveal that in general, not all intersections should be equipped with TSP, and intersections that carry high demand within a network are promising locations for TSP implementation to reduce the total travel time of network users. Additionally, it is found that the total travel time of network users can be further decreased by only activating TSP for buses with more than a certain number of on-board passengers.

An Improved Transit Signal Priority Strategy for Real-World Signal Controllers that Considers the Number of Bus Arrivals

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.

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

Transit Signal Priority (TSP) is a commonly used strategy to improve bus operations at signalized intersections. However, the impacts of TSP in mixed traffic environments has not been analytically studied. This is a challenging problem since car queues can slow down buses, while slow-moving buses can create bottlenecks for cars in mixed traffic. Furthermore, it is typically assumed that TSP is activated using bus information obtained from a fixed detector. The benefits of TSP to buses could be improved by using information from connected buses or even connected cars. To tackle these challenges, this paper models buses as moving bottlenecks, incorporating it into a kinematic wave theory (KWT) model. A dynamic programming (DP) algorithm is developed to evaluate the changes in delays to buses and cars caused by TSP using KWT and queuing theories considering the bus as a moving bottleneck. The proposed algorithm is utilized for sensitivity tests to determine the changes to car and bus delays as a result of TSP implementation as a function of the bus detector location, bus stop location and dwell duration, the existence of a downstream bottleneck, and bus detection technology. The detector location sensitivity analysis reveals that there exists an optimal (and different) bus detector location associated with each demand. The bus stop location and bus dwell duration sensitivity tests show that TSP implementation can reduce system-wide (i.e., total car and bus) delays. However, in general, it is found that the presence of a downstream bottleneck can diminish the benefits of providing TSP. Finally, different bus detection technologies are tested to quantify the benefits of using connected buses or connected cars for TSP provision in terms of car and bus delay savings. As a result of this test, it is found that connected cars can significantly improve both car and bus delays if used for TSP provision.

Planning and assessment of multimodal transportation systems: case studies in Salt Lake City, UT

The need for multimodal transportation alternatives in metropolitan areas is increasing because of the inability to build more capacities for low-occupancy vehicles. This paper describes multimodal alternative analysis and comparisons of potential improvements. The alternatives include Bus Rapid Transit (BRT), addition of bicycle lanes, implementation of Transit Signal Priority (TSP) and introduction of a streetcar line. The analysis was performed through traffic microsimulation for the target 2025 year. The implementation of upgraded transit systems and TSP has the potential to significantly improve system performance. Person-based delays decrease and transit speeds improve, with a significant increase in the offered transit productive capacities. With optimization of operations and geometry improvements, the performance of vehicular traffic is not impacted by transit-friendly solutions. In a well-balanced network, it is important to offer more travel choices through increased transit service and options for non-motorized traffic, equally considering efficiency and safety for all travel modes.

Effects of transit signal priority on traffic safety: Interrupted time series analysis of Portland, Oregon, implementations

Transit signal priority (TSP) has been implemented to transit systems in many cities of the United States. In evaluating TSP systems, more attention has been given to its operational effects than to its safety effects. Existing studies assessing TSP’s safety effects reported mixed results, indicating that the safety effects of TSP vary in different contexts. In this study, TSP implementations in Portland, Oregon, were assessed using interrupted time series analysis (ITSA) on month-to-month changes in number of crashes from January 1995 to December 2010. Single-group and controlled ITSA were conducted for all crashes, property-damage-only crashes, fatal and injury crashes, pedestrian-involved crashes, and bike-involved crashes. Evaluation of the post-intervention period (2003–2010) showed a reduction in all crashes on street sections with TSP (-4.5%), comparing with the counterfactual estimations based on the control group data. The reduction in property-damage-only crashes (-10.0%) contributed the most to the overall reduction. Fatal and injury crashes leveled out after TSP implementation but did not change significantly comparing with the control group. Pedestrian and bike-involved crashes were found to increase in the post-intervention period with TSP, comparing with the control group. Potential reasons to these TSP effects on traffic safety were discussed.

Large-Scale Transit Signal Priority Implementation: District of Columbia’s Path to Success

In 2016, the District Department of Transportation (DDOT) deployed transit signal priority (TSP) at 195 intersections in highly urbanized areas of Washington, D.C. Years before deployment, DDOT out...

Optimizing Transit Signal Priority Implementation along an Arterial

Transit signal priority (TSP) is a common method of providing priority to buses at signalized intersections. The implementation of TSP can affect travel time of cars traveling in the same, opposite, and cross directions. The bus delay savings and car travel-time impacts are not expected to increase linearly when considering multiple intersections along an arterial. This paper quantifies the influence of TSP on arterials with dedicated bus lanes considering an arterial-wide approach utilizing variational theory. Existing tools were modified to quantify the change in capacity along an arterial where TSP was implemented and it was shown that this effect was negligible. In addition, the bus delay savings and cross-street capacity losses were determined. Case studies provided insights into the influence of TSP among different network homogeneities and bus frequencies. Using these tools, an optimization framework was developed to determine where to implement TSP along an arterial to maximize the marginal benefits, or minimize marginal costs. In addition, a comparison of evaluating an arterial as a sum of isolated intersections as opposed to evaluating an arterial as a whole is presented. This analysis indicates the necessity of the arterial-based method in considering TSP impacts along corridors.

Assessing Effects of Transit Signal Priority on Traffic Safety: Empirical Bayes Before–After Study using King County, Washington, Data

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 analy...

Evaluation of Transit Signal Priority Implementation for Bus Transit along a Major Arterial Using Microsimulation

Transit Signal Priority (TSP) provides preferential treatment for public transit vehicles at signalized intersections when implemented. TSP is usually provided by interrupting the typical signal timings and extending the green or truncating the red for the signal phases that serve transits. This study investigates the impact of implementing a TSP treatment along a major arterial. A microsimulation approach was used to model, assess, and evaluate the potential benefits of implementing this treatment to bus transit vehicles. The network was built in a VISSIM multimodal microsimulation environment to test the traffic network performance with and without priority treatments. The study considered different peak hours for performance assessment. Three transit routes were considered in the microscopic modeling. The results showed a significant benefit of implementing TSP for the transit vehicles. The travel time was reduced by more than 40% in some cases, which can be translated into lower transit delay and more reliable transit service. The results also showed that TSP has a minimal negative effect on the general traffic. In fact, the general traffic along the studied transit routes benefited from the TSP implementation because of the better traffic progression and additional green times.

Evaluation of GPS-Based Transit Signal Priority for Mixed-Traffic Bus Rapid Transit

GPS-based transit signal priority (TSP) is a new type of transit preferential tool being considered in many North American cities. With GPS detection and advanced communications, GPS-based TSP provides flexible and conditional signal priority to transit vehicles. This is a relatively new system with limited field implementations and evaluations. The research in this study used microscopic simulation, to create a set of TSP scenarios for a 3-mi bus corridor and evaluated the influences of GPS-based TSP on transit and traffic operations. The simulation network was the 3300 South corridor, including side streets, in Salt Lake County, Utah. A proposal was evaluated to upgrade the Utah Transit Authority Bus Route 33 operating along the corridor into a bus rapid transit (BRT) line running in mixed traffic. Eight simulation scenarios were created to cover ( a) current field conditions, ( b) regular bus with traditional TSP implementation, ( c) regular bus with GPS-based TSP, ( d) BRT implementation with no TSP, ( e) BRT with traditional TSP implementation, ( f) BRT with GPS-based TSP, ( g) BRT with conditional TSP, and ( h) BRT with multiconditional TSP implementation. The evaluation included transit and nontransit operations, and the impacts on side-street traffic. The results show that GPS-based TSP performed as effectively as did traditional TSP. Conditional and multi-conditional TSP strategies showed benefits in providing the transit system considerable delay reduction (13% and 3%, respectively) and travel time savings (7% and 3%, respectively) while having the smallest impacts on side-street traffic (3% and 1% increase in delay, respectively) compared with other TSP strategies.

Evaluation of Conditional Transit Signal Priority Technology for Regional Implementation

This research evaluated the implementation of transit signal priority (TSP) on a test corridor along International Drive in Orlando, Florida, to see whether the implementation was successful and justified expansion to a regional implementation of TSP for bus tie-ins to the new regional SunRail commuter rail in Central Florida. TSP is a technology that provides preferential treatment to buses at signalized intersections. This research demonstrated the effectiveness of TSP in improving bus corridor travel time in a simulated environment by using real-world data for the International Drive corridor. Evaluation was conducted with microsimulation to compare unconditional and conditional TSP with the no TSP scenario. This evaluation looked at performance metrics (for buses and all vehicles), including average speed profiles, average travel times, average number of stops, and crossing street delay. Different conditional TSP scenarios of activating TSP when a bus is 3 or 5 min behind schedule were considered. The simulation demonstrated that conditional TSP significantly improved bus travel times with little effect on crossing street delays. Unconditional TSP resulted in significant crossing street delays at some intersections with only minor improvement to bus travel time compared with both conditional TSP scenarios. The results also showed that using TSP technology reduced the environmental emissions in the International Drive corridor. With a benefit–cost ratio of 7.92 in the International Drive corridor, conditional TSP 3 min behind schedule was determined to be the most beneficial and practical TSP scenario for real-world implementation at corridor and regional levels.

Using delay functions to evaluate transit priority at signals

In this paper a new method to evaluate the network-wide effect of transit signal priority (TSP) is presented. A delay function is developed to estimate the effect of TSP at intersections using parameters such as traffic flow and signal characteristics. The proposed method is tested in two case studies. Firstly, it is applied to a single intersection to address both TSP strategy and model efficiency. Secondly, a set of TSP implementation scenarios is defined for a corridor in South-East Queensland, Australia. Bus and car delays are calculated for each scenario and the results are compared with those obtained using microsimulation. The results confirm that the delay estimated by the proposed method closely matches microsimulation results. The proposed method reflects the effects of having TSP at each intersection without performing time consuming microsimulation analyses. This method makes it possible to evaluate the effectiveness of TSP in large networks. The new method can be used to perform preliminary evaluation of TSP schemes at the network level, thereby potentially reducing the significant computational time of network analyses.

Feasibility of Unconditional Transit Signal Priority Considering Delay Savings at Signalized Intersections: A Case Study of Dalian BRT Line No.1

Active transit signal priority (TSP) is a useful tool to remove or minimize control delay for buses at signalized intersections and consequently improve the performance of bus service. Conditional TSP has been proofed to be an effective measure with fewer impacts to non-prioritized traffic, and aroused increasingly research interests and implementation in European and North American cities nowadays. However, the implementation of conditional TSP requires a sophisticated mechanism for examining the schedule deviation of priority required buses, as well as scanning traffic operational conditions at network level. By contrast, unconditional TSP is a less costly and easier measure for implementation, because it gives priority to every required bus without considering its schedule deviation. The critical argument on unconditional TSP is its adverse impacts to non-prioritized traffic, particularly during peak hours. To determine the threshold for implementing unconditional TSP, in this paper, theoretical analysis based on signal display graphs of differential TSP granting strategies was undertaken to estimate delay savings and increments for prioritized and non- prioritized approaches respectively. In the next step, Dalian BRT line was taken as an example to verify the feasibility of unconditional TSP based on the proposed theory. Results show that unconditional TSP is feasible during off-peak hours, while for peak hours, feasibility of unconditional TSP mainly depended on the traffic volume of each approach.

Microscopic Simulation Approach to Effectiveness Analysis of Transit Signal Priority for Bus Rapid Transit

Transit signal priority (TSP) is one of the critical components of bus rapid transit (BRT) and a key technology in enhancing its operational efficacy. It not only provides priority to the BRT vehicle but also manages the trade-off between the delay of buses at an intersection and the impacts on other traffic. This paper focuses on the design and evaluation of a simulation of signal priority for BRT under mixed traffic flow conditions. Taking the Southern Axis BRT Line 1 in Beijing as a case study, different signal priority strategies, including green extension, red truncation, and special phase insertion, are developed for the signalized intersection along the BRT route. A signal coordination plan is designed for the section composed of four intersections in the BRT system. The study uses the VISSIM microscopic traffic simulation model to analyze the impacts of traffic parameters, especially those of nonmotorized traffic, on the effectiveness of TSP along the corridor. Simulation results indicated that the BRT vehicles would typically benefit from transit priority with travel times savings and delay reduction, as well as greater schedule adherence, and suggest that these benefits may be obtained with little negative impacts to the overall system. The results also indicated that the volume of nonmotorized traffic is one of the most important factors that influence the effectiveness of TSP implementation in a mixed traffic flow.

Analysis of Transit Signal Priority Using Archived TriMet Bus Dispatch System Data

Findings are presented on changes in bus running times, on-time performance, and excess passenger wait times following implementation of transit signal priority (TSP) in select bus corridors in the Portland metropolitan region. Analysis of the effectiveness of TSP is often undertaken by using simulation techniques or empirical studies that are limited in either scope or data availability, or both. The current research uses an abundance of trip-level data collected from TriMet's Bus Dispatch System, in Portland, Oregon. The study focuses on the most common performance measures of interest to both transit operators and passengers and shows that the expected benefits of TSP are not consistent across routes and time periods, nor are they consistent across the various performance measures. The authors believe that benefits of TSP will accrue only as the result of extensive evaluation and adjustment after initial deployment. In most cases, an ongoing performance monitoring and adjustment program should be imple...

Analysis of Transit Signal Priority Using Archived TriMet Bus Dispatch System Data

Findings are presented on changes in bus running times, on-time performance, and excess passenger wait times following implementation of transit signal priority (TSP) in select bus corridors in the Portland metropolitan region. Analysis of the effectiveness of TSP is often undertaken by using simulation techniques or empirical studies that are limited in either scope or data availability, or both. The current research uses an abundance of trip-level data collected from TriMet's Bus Dispatch System, in Portland, Oregon. The study focuses on the most common performance measures of interest to both transit operators and passengers and shows that the expected benefits of TSP are not consistent across routes and time periods, nor are they consistent across the various performance measures. The authors believe that benefits of TSP will accrue only as the result of extensive evaluation and adjustment after initial deployment. In most cases, an ongoing performance monitoring and adjustment program should be implemented to maximize TSP benefits.