摘要
干线协调控制通常以干线方向通行效率最大为目标,导致一些小型交叉口次路方向延误较大。针对该问题,基于车路协同环境,研究了车速引导下的双周期干线多目标优化方法。针对上游交叉口饱和交通流与非饱和交通流2种情况,提出了考虑排队消散和相位差的动态车速引导模型。以干线延误、通行能力、停车次数,双周期交叉口次路方向延误为优化目标,构建了车速引导下的双周期干线多目标优化模型,采用遗传算法对模型进行求解。基于COM接口,采用Python和Vissim搭建车路协同仿真环境,以北京市两广路的3个路口为例进行仿真验证。对比了本文模型与原配时方案、无车速引导下双周期干线多目标优化模型的效果,结果表明,本文模型相比于原配时方案和无车速引导下多目标优化模型,干线平均延误分别减少19.6%,8.3%,通行能力分别提升5.6%,8.4%,平均停车次数分别减少11.2%,24.2%,双周期交叉口次路方向平均延误分别减少33.9%,5.8%,表明本文模型将速度引导与多目标优化相结合,提高了双周期干线的通行效率,降低了双周期交叉口次路方向的延误,达到了干线和双周期交叉口共同优化的目的。
Arterial coordination control usually aims at maximizing the traffic efficiency in the main direction,which leads to a large delay in the cross street of some minor intersections.Based on the cooperative vehicle infrastructure,the work studies the multi-objective optimization method of double-cycling arterials under speed guidance.Aiming at the saturated and unsaturated traffic flow at the upstream intersection,a dynamic speed guidance model considering queue dissipation and offset is proposed.Furthermore,a double-cycling arterials multi-objective optimization model is constructed taking the average delay time,the average number of stops,the capacity of arterials,and the average delay of the double-cycling intersection as the comprehensive optimization objectives.Then,the genetic algorithm is used to solve the model to obtain the optimized coordinated signal-timing scheme.Based on the COM interface,the cooperative vehicle infrastructure environment is built using Python and Vissim software,and the model is simulated by taking three intersections of Guanganmen Inner Street in Beijing as a case study.The results of this model are compared with those of the original scheme and the multi-objective optimization model of the double-cycling artery without speed guidance.Compared with the original scheme and the multi-objective optimization model without speed guidance,the average delay of arterial is reduced by19.6%and8.3%;the capacity increased by5.6%and8.4%;the average number of stops is reduced by11.2%and24.2%;the average delay of the cross street of the double-cycling intersection reduced by33.9%and5.8%,respectively.The results show that this model combines speed guidance with multi-objective optimization to achieve dynamic speed guidance,with the increased traffic efficiency of the double-cycling artery,the reduced delay of a cross street at a double-cycling intersection,and the mutual optimization of the artery and double-cycling intersection.
作者
张靖思
李振龙
邢冠仰
ZHANG Jingsi;LI Zhenlong;XING Guanyang(Beijing Key Laboratory of Traffic Engineering,Beijing University of Technology,Beijing 100124,China)
出处
《交通信息与安全》
CSCD
北大核心
2021年第3期60-67,共8页
Journal of Transport Information and Safety
基金
北京市交通行业科技项目(2020-kjc-02-180)资助。
关键词
交通控制
双周期干线协调
车速引导
多目标优化
遗传算法
traffic control
double-cycling arterial coordination
speed guidance
multi-objective optimization
genetic algorithm
