Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. This paper presents an adaptive transit signal priority （TSP） strategy that applies the parallel genetic...Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. This paper presents an adaptive transit signal priority （TSP） strategy that applies the parallel genetic algorithm （PGA） to optimize adaptive traffic signal control in the presence of TSP. The method can optimize the phase plan, cycle length, and green splits at isolated intersections with consideration for the performance of both the transit and the general vehicles. A VISSIM （VISual SIMulation） simulation testbed was developed to evaluate the performance of the proposed PGA-based adaptive traffic signal control with TSP. The simulation results show that the PGA-based optimizer for adaptive TSP outperformed the fully actuated NEMA control in all test cases. The results also show that the PGA-based optimizer can produce TSP timing plans that benefit the transit vehicles while minimizing the impact of TSP on the general vehicles.展开更多
This study evaluated the operational performance of Transit Signal Priority(TSP)using a microscopic simulation approach.The analysis was based on a 10-mile study corridor in South Florida.Two microscopic VISSIM simula...This study evaluated the operational performance of Transit Signal Priority(TSP)using a microscopic simulation approach.The analysis was based on a 10-mile study corridor in South Florida.Two microscopic VISSIM simulation models were developed:a Base model,calibrated and validated to represent field conditions,and a TSP model.With TSP,the study corridor experienced up to 8%reduction in travel times and up to 13.3% reduction in average vehicle delay time,for both buses and all other vehicles.To better quantify the mobility benefits of the TSP strategy,Mobility Enhancement Factors(MEFs)were developed,unlike previous studies.A MEF is a multiplicative factor to estimate the expected mobility level after implementing TSP at a specific site.A MEF<1 implies that the TSP yields mobility benefits.TSP’s impact on cross-streets were also estimated.The study results indicate TSP strategy has enhanced mobility for buses and all other vehicles.展开更多
文摘Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. This paper presents an adaptive transit signal priority （TSP） strategy that applies the parallel genetic algorithm （PGA） to optimize adaptive traffic signal control in the presence of TSP. The method can optimize the phase plan, cycle length, and green splits at isolated intersections with consideration for the performance of both the transit and the general vehicles. A VISSIM （VISual SIMulation） simulation testbed was developed to evaluate the performance of the proposed PGA-based adaptive traffic signal control with TSP. The simulation results show that the PGA-based optimizer for adaptive TSP outperformed the fully actuated NEMA control in all test cases. The results also show that the PGA-based optimizer can produce TSP timing plans that benefit the transit vehicles while minimizing the impact of TSP on the general vehicles.
基金sponsored by the Florida Department of Transportation(FDOT)and conducted as a cooperative effort by the Florida International University(FIU)and University of North Florida(UNF).
文摘This study evaluated the operational performance of Transit Signal Priority(TSP)using a microscopic simulation approach.The analysis was based on a 10-mile study corridor in South Florida.Two microscopic VISSIM simulation models were developed:a Base model,calibrated and validated to represent field conditions,and a TSP model.With TSP,the study corridor experienced up to 8%reduction in travel times and up to 13.3% reduction in average vehicle delay time,for both buses and all other vehicles.To better quantify the mobility benefits of the TSP strategy,Mobility Enhancement Factors(MEFs)were developed,unlike previous studies.A MEF is a multiplicative factor to estimate the expected mobility level after implementing TSP at a specific site.A MEF<1 implies that the TSP yields mobility benefits.TSP’s impact on cross-streets were also estimated.The study results indicate TSP strategy has enhanced mobility for buses and all other vehicles.
