基于混沌云粒子群算法的交叉口信号智能配时优化

Intersection Traffic Signal Intelligent Timing Optimization Based on Chaos Cloud Particle Swarm Optimization Algorithm

为了提高交叉口的通行效率,基于相位饱和度对交叉口状态进行划分,根据划分状态对车辆延误和停车次数分别进行计算,以车辆延误、停车次数和通行能力为优化函数,提出了新的交叉口交通信号配时方法.同时基于猫映射和云模型对标准粒子群算法进行改进,提出了混沌云粒子群算法(CCPSO),建立了基于CCPSO进行优化的交叉口信号配时模型,结合具体交叉口流量统计数据进行数值实验,结果表明:新模型能够根据不同的交通流状态实时进行交通智能控制,减小了车辆延误和停车次数,提高了交叉口通行能力,验证了该模型处理交通配时优化问题的有效性和先进性.

To improve traffic efficiency at intersection, a new intersection traffic signal timing method was proposed, this method takes the delay vehicles, number of stops and the traffic capacity as the control objective, and uses the corresponding formula to calculate the vehicle delay and number of stops for different traffic condition, which is divided by phase saturation. The Chaos Cloud Particle Swarm Optimization （CCPSO） was presented based on the cat map and cloud model. On this basis, a new intersection traffic signal dynamic timing mode optimized by CCPSO was established. Contrastive analyses represent that this model can control the traffic real-time according to the traffic flow condi- tion, reduce the average vehicle delay, number of stops and improve intersection the traffic eapaeity, verifying the effectiveness and advanced of this model.