Adaptive Traffic Signal Control(ATSC)adjusts signal timings to real-time traffic measure ments,increasing operational efficiency within a network.However,ATSC is both expen sive to install and operate making it infeas...Adaptive Traffic Signal Control(ATSC)adjusts signal timings to real-time traffic measure ments,increasing operational efficiency within a network.However,ATSC is both expen sive to install and operate making it infeasible to deploy at all signalized intersections within a network.This study presents a bi-level optimization framework that applies heuristic methods to identify a limited set of locations for ATSC deployment within an urban network.At the upper-level,the Population Based Incremental Learning(PBIL)algo rithm is employed to generate,evaluate,learn,and update different ATSC configurations.The lower-level uses the delay-based Max-Pressure algorithm to simulate the ATSC config uration within a microsimulation platform.The study proposes improvements to the PBIL algorithm by considering constraints on the maximum number of intersections for ATSC deployment and incorporates prior information about the intersection performance(i.e.,informed search).Simulation results on the traffic network of State College,PA reveal that the proposed PBIL algorithm consistently outperforms baseline methods that select loca tions only based on queue-lengths or delays in terms of reducing overall network travel times.The study also reveals that intersections experiencing the highest delays or longest queues are not always the best candidates for ATSC.Moreover,applying ATSC at all inter sections does not always provide the best performance;in fact,ATSC applied to some loca tions could increase travel times by contributing additional congestion downstream.Additionally,the modified PBIL algorithm with the informed search strategy is more effi cient at identifying promising solutions suggesting it can be readily applied to more gen eralized optimization problems.展开更多
基金This research was supported by NSF Grant CMMI-1749200.
文摘Adaptive Traffic Signal Control(ATSC)adjusts signal timings to real-time traffic measure ments,increasing operational efficiency within a network.However,ATSC is both expen sive to install and operate making it infeasible to deploy at all signalized intersections within a network.This study presents a bi-level optimization framework that applies heuristic methods to identify a limited set of locations for ATSC deployment within an urban network.At the upper-level,the Population Based Incremental Learning(PBIL)algo rithm is employed to generate,evaluate,learn,and update different ATSC configurations.The lower-level uses the delay-based Max-Pressure algorithm to simulate the ATSC config uration within a microsimulation platform.The study proposes improvements to the PBIL algorithm by considering constraints on the maximum number of intersections for ATSC deployment and incorporates prior information about the intersection performance(i.e.,informed search).Simulation results on the traffic network of State College,PA reveal that the proposed PBIL algorithm consistently outperforms baseline methods that select loca tions only based on queue-lengths or delays in terms of reducing overall network travel times.The study also reveals that intersections experiencing the highest delays or longest queues are not always the best candidates for ATSC.Moreover,applying ATSC at all inter sections does not always provide the best performance;in fact,ATSC applied to some loca tions could increase travel times by contributing additional congestion downstream.Additionally,the modified PBIL algorithm with the informed search strategy is more effi cient at identifying promising solutions suggesting it can be readily applied to more gen eralized optimization problems.
