| The major deficiency in the literature about transit signal priority (TSP) is the lack of understanding of how traffic systems are impacted under various levels of traffic flow and geometric characteristics (i.e. spacing of signals, number of lanes, bus-stop location) when transit signal priority control strategies are employed. This suggests that different traffic conditions and geometric characteristics impact the effectiveness of TSP. This research effort, hence, seeks to provide information on how TSP is impacted due to varying traffic volumes, transit headways, intersection spacing, and two or three lane directional roadways (four or 6 lane facilities). The outcomes from the research effort seek to provide this information in the context of cycle lengths, detector usage, and extension or truncation times for TSP implementation as well.; For this research, the overall goal is to improve the transportation planning process by determining guidelines and effective planning strategies for implementation of transit signal priority control in traffic systems operations on arterials. To accomplish this, the research objective revolved around determining the change in measures of effectiveness (MOEs) under varying traffic volumes, bus volumes, intersection spacing, and number of lanes. MOEs used in the research included average transit travel time and standard deviation, average travel time for vehicles on arterial, average system stopped delay, and average cross street delay.; To accomplish the research, a general case study was developed to investigate the various combinations of traffic and geometric characteristics under various systems consisting of three signals each. The investigation was performed through the use of the VISSIM simulation model. This is a microscopic traffic simulation model, explicitly designed for traffic and transit simulation, that emulates vehicular and pedestrian movement based on a psycho-physical, car-following theory. A TSP algorithm that combined green extension and red truncation was used in conjunction with exit detection to perform the transit signal priority. Cycle lengths varied between 60 and 100 seconds.; The results of the modeling include findings of consistent reductions in average transit travel time and standard deviation of transit travel time with the use of TSP. Reductions for transit travel time ranged up to 10.5 percent, and reductions in standard deviation ranged to 70%. Changes in the results were tested through the use of paired t-tests, F-tests, and ANOVA. An evaluation technique was developed, based on these analyses, that predicts traffic system performance under TSP according to the number of lanes, buses per hour, vehicles per hour on arterial, and intersection spacing. |
