基于遗传算法的交叉口信号控制多目标优化  

Multi-object optimization for intersection signal control based on genetic algorithm

作　　者：李振龙[1] 董文会[1] 韩建龙[1] 朱明浩[1] 

机构地区：[1]北京工业大学城市交通学院,北京100124

出　　处：《计算机应用》2016年第A02期82-84,88,共4页journal of Computer Applications

摘　　要：从城市交叉口的通行效益、环境保护两方面出发,综合考虑车辆延误、排队长度、尾气排放量三个性能指标,以配时参数为优化变量建立了平面交叉口信号控制多目标优化模型,其权重系数与环境污染程度有关;然后采用遗传算法求解该模型,得到最优信号配时方案;最后通过实例分析模型的有效性。实例结果表明,当空气质量良好时,最优配时方案下的车辆延误、排队长度、尾气排放总量比原始信号配时的对应三个指标值分别降低了9.51%、1.21%、6.24%;当环境严重污染时,最优配时方案下的车辆延误、排队长度、尾气排放总量分别降低了7.21%、1.01%、10.24%。可以看出该多目标优化模型不仅提高了交叉口的通行效率,同时降低了车辆的尾气排放。Considering traveling efficiency and environmental protection of urban intersections, a multi-objective signal control optimization model at intersections was established with the timing parameters as optimization variables, taking traffic delay, queue length and vehicle emission as the optimization objectives. The weight coefficient was related to the degree of environmental pollution. The genetic algorithm was used to calculate multi-objective model to obtain the optimal signal timing scheme. Finally, an actual example was used to analyze the validity of the model. Results show that when air quality is good, compared with the original timing scheme, traffic delay, queue length and vehicle emission of the optimal timing plan decrease by 9.51%, 1.21%, 6.24%, respectively. When environmental pollution is serious, the traffic delay, queue length and vehicle emission of the optimal timing plan decrease by 7.21%, 1.01%, 10.24%. It can be seen that the proposed multi- objective signal control optimization model not only improves the traffic efficiency of intersection but reduce the vehicle

关 键 词：交通控制 车辆延误 排队长度 车辆排放 遗传算法 

分 类 号：U491[交通运输工程—交通运输规划与管理]