Developing Guidelines for Implementing Transit Signal Priority and Freight Signal Priority Using Simulation Modeling and a Decision Tree Algorithm

Transit signal priority (TSP) and freight signal priority (FSP) allow transportation agencies to prioritize signal service allocations considering the priority of vehicles and, potentially, decrease the impact signal control has on them. However, there have been no studies to develop guidelines for implementing signal control considering both TSP and FSP. This paper reports on a study conducted to provide such guidelines that employed a literature review, a simulation study, and a decision tree algorithm based on the simulation results. The guideline developed provides recommendations in accordance with the signal timing slack time, the proportion of major to minor street hourly volume, hourly truck volume per lane for the major street, hourly truck volume per lane for the minor street, the proportion of major to minor street hourly truck volume, the proportion of major to minor street hourly bus volume, the volume-to-capacity ratio for the major street, and the volume-to-capacity ratio for the minor street. The guideline developed was validated by implementing it for a case study facility. The validation result showed that the guideline works correctly for both high and low traffic demand.

Conditional Transit Signal Priority for Connected Transit Vehicles

Connected vehicle (CV) technologies enable safe and interoperable wireless communication among vehicles and the infrastructure with the possibility to run many applications that can improve safety, and enhance mobility. This paper develops CV-based algorithms which use transit vehicle speed and the estimated time that the vehicle needs to arrive at an intersection to trigger transit signal priority (TSP) initiation. This information is updated each second based on the traffic conditions such as speed, a current distance of a transit vehicle to the intersection, and queue conditions. The algorithm uses the actual speed of a transit vehicle and its latitude/longitude (lat/lon) coordinates to compute the time that the vehicle needs to reach the stop line. It was tested on a real-world network using VISSIM traffic simulation, but can easily be implemented in the field, since it is using world coordinates. The upgraded algorithm was applied to a future bus rapid transit (BRT) scenario, and included different levels of conditional TSP, which depend on three combined conditions: the time that a transit vehicle needs to reach the stop line, the number of passengers on board, and the lateness that the transit vehicle experiences. The test-case network used for model building is a corridor consisting of ten signalized intersections along State Street in Salt Lake City, UT. The CV algorithms coupled with TSP can yield notable delay reductions for both the regular bus and the BRT of 33% and 12%, respectively.

Safety Impacts of Transit Signal Priority Using a Full Bayesian Approach

Transit signal priority (TSP) is a strategy that prioritizes the movement of transit vehicles through a signalized intersection to provide better transit travel time reliability and minimize transit delay. Although TSP is primarily intended to improve the operational performance of transit vehicles, it may also have substantial safety benefits. This study explored the potential safety benefits of the TSP strategy deployed at various locations in Florida. An observational before–after full Bayes (FB) approach with a comparison group was adopted to estimate the crash modification factors (CMFs) for total 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. The deployment of TSP was found to reduce total crashes by 7.2% (CMF = 0.928), rear-end crashes by 5.2% (CMF = 0.948), and angle crashes by 21.9% (CMF = 0.781), and these results are statistically significant at a 95% Bayesian credible interval (BCI) except for the rear-end crashes. On the other hand, sideswipe crashes increased by 6% (CMF = 1.060) although the increase was not significant at a 95% BCI. Overall, the results indicated that TSP improves safety. The findings of this study may present key considerations for transportation agencies and practitioners when planning future TSP deployments.

Multi-Level Conditional Transit Signal Priority in Connected Vehicle Environments

Connected Vehicles (CV) are an emerging technology with a large potential to improve traffic operations and safety. This paper develops and tests advanced CV-based multi-level conditional Transit Signal Priority (TSP). The algorithms are using the latitude/longitude (lat/lon) coordinates of CV vehicles and intersections to establish communication, share information and request priority. The TSP strategies are implemented through controllers’ built-in features and logic processor, using Econolite ASC/3 as a representative traffic signal controller. The tests were performed in VISSIM microsimulation with the ASC/3 Software-in-the-Loop (SIL) controller emulator. State Street in Salt Lake City, UT, is selected as a test-case corridor. The paper shows that the developed signal control priority (SCP) algorithms are successful in reducing delays for target vehicles in excess of 6%, without significant impacts on other traffic. The information obtained from CV vehicles can be used to further enhance control algorithms and create adaptive SCP programs.