Factors Influencing Effectiveness of Transit Signal Priority and Late-Bus Recovery at Signalized-Intersection Level

Transportation Research Record: Journal of the Transportation Research Board, No. 2311, Transportation Research Board of the National Academies, Washington, D.C., 2012, pp. 186–194. DOI: 10.3141/2311-18 Department of Civil and Environmental Engineering, Portland State University, P.O. Box 751, Portland, OR 97207. Corresponding author: E. Albright, [email protected]. of buses over passenger vehicles (2–4). For instance, during peak demand periods in which the potential for queuing is high, TSP can allocate more green time for transit vehicles to travel through intersections and remain on schedule. Previous research has mostly focused on the study of TSP performance at the route level; however, in some practical situations, it is important to identify the location of the signalized intersections that significantly affect TSP performance. For example, a bus transit corridor may intersect several major cross streets for which traffic signal progression can be desirable. By understanding TSP performance at the level of signalized intersections, it is possible to provide progression for cross-street signals for which TSP does not play a significant role while keeping TSP on major crossings, where TSP plays a significant role in the schedule recovery of late buses. As this research indicates, it is also possible that TSP shows modest improvements at the aggregated-route level but significant improvement and bus recovery at specific intersections. Unfortunately, because of the stochasticity associated with bus schedules and operations, it is not possible to identify a priori the signalized intersections that perform a key role in TSP performance and late-bus recovery. This research aims to fill that research gap. The study used a diverse group of data sets to identify methods for analyzing TSP effectiveness: bus scheduling and routing data, passenger boarding and alighting data, and TSP request data. The goals of this research were (a) to determine specific periods and traffic signals for which TSP has enabled schedule recovery, (b) to develop a method for identifying factors influencing TSP effectiveness, and (c) to create a model to determine the statistical effect TSP and other factors have on bus performance. This method is applied to a congested arterial, southeast Powell Boulevard in Portland, Oregon.