基于遗传算法的单点交叉口信号配时优化

以相位的周期时长、绿灯时间作为约束条件,平均停车次数、平均延误最小作为优化目标函数,建立了信号配时优化非线性模型.以上海某一交叉口作为研究对象,将其交叉口的交通数据应用于该模型中,以Matlab为模拟环境,应用实数编码遗传算法对其求解.运行结果显示:交叉口的信号周期由145s变为118s,缩短了19%;车辆的平均延误由45s/veh变为36s/veh,下降了20%;车辆的平均停车次数由0.828 2变为0.736 1,下降了11%.研究结论表明,该方法得出的信号配时方案可以有效地减少停车延误和停车次数,优于现有控制方案及传统的Webster算法得出的方案,从而证明了此模型的实用性.

Signal Timing Optimization at Single-Point Intersection Based on Genetic Algorithm

A non-linear model for signal timing optimization was created to study the problem of four phases signal timing optimization at city's single-point intersections.The minima of average delay and average stop times were regarded as the optimization objective function,the phase green time and the cycle time were regarded as the constraints and one of the Shanghai intersections was regarded as the research object.With the real-coded genetic algorithm in the Matlab simulation environment,the optimization problem was solved.By applying the method,the intersection cycle time changes from 145 s to 118 s,shortened by 19%,the average vehicle delay changes from 45 s/veh to 36 s/veh,shortened by 20%,and the average stops changes from 0.828 2 to 0.736 1,shortened by 11%.The results show that the signal timing plans drawn by using this method can reduce parking delay and stops effectively,thus it proves the usefulness of the model which is better than the existing control scheme and the Webster optimization algorithm.