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首页> 外文期刊>Intelligent decision technologies >Traffic management model for vehicle re-routing and traffic light control based on Multi-Objective Particle Swarm Optimization
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Traffic management model for vehicle re-routing and traffic light control based on Multi-Objective Particle Swarm Optimization

机译：基于多目标粒子群算法的车辆改道与交通信号灯交通管理模型

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摘要

In this paper, a novel traffic management modelis presented, which simultaneously optimizes vehicle re-routing and trafficlight control to alleviate traffic congestion and limit the effects ofincidents on traffic flow based on Multi-Objective Particle SwarmOptimization (MOPSO) method. Once a congested road is predicted, ourproposed Multi-Objective Traffic Light Control is then applied to optimizesignal timing which takes the maximization of traffic flow on the edge wherethe incident takes place and the minimization of the average junctionwaiting time as two objectives. To improve the performance and sensitivityof MOPSO algorithm, we used Q-Learning algorithm to grant to each agent ofthe swarm the ability of selecting appropriate MOPSO parameters adapted tothe structure of the problem. At the same time, when the situation of thetraffic flow starts to become more serious, we adopt a novel Multi-ObjectiveVehicle Re-routing strategy for assigning alternatives routes to cars beforeentering the congested road, in order to perform dynamic load balancing.Vehicle re-routing is also optimized by MOPSO to simultaneously find theshortest and least popular path. The obtained results from the simulationusing SUMO, a well-known microscopic traffic simulator, confirm theefficiency of the proposed system.
机译：本文提出了一种新颖的交通管理模型，该模型同时基于多目标粒子群优化（MOPSO）方法优化了车辆的重新路由和交通信号灯控制，以缓解交通拥堵并限制事故对交通流的影响。一旦预测到拥堵的道路，我们建议的多目标交通灯控制将用于优化信号定时，这将发生事件的边缘的交通流量最大化和将平均路口等待时间最小化作为两个目标。为了提高MOPSO算法的性能和敏感性，我们使用Q-Learning算法为群体的每个代理赋予了选择适合问题结构的适当MOPSO参数的能力。同时，当交通流量情况开始变得更加严重时，我们采用了一种新颖的多目标车辆重路由策略，以便在进入拥挤的道路之前为汽车分配替代路线，以实现动态负载平衡。 MOPSO还优化了路由，以同时找到最短和最不受欢迎的路径。使用著名的微观交通模拟器SUMO进行的模拟获得的结果证实了所提出系统的效率。

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