基于多智能体交互作用的活动-出行时空分布特性仿真

Simulation of a Multi-agent Interactive Model for Activity-travel Pattern in Time and Space

在多智能体仿真平台上应用强化学习算法对出行者活动-出行的时间规划与地点选择进行了仿真.由于在模型中引入了道路拥挤程度的实时变化参数,环境能随各智能体的决策动态变化,体现出多个智能体处于相同环境时决策的相互影响与个体和环境的交互作用.仿真结果表明,基于多智能体交互作用方法得出的出行者个体活动模式,群体交通流量分布与弹性活动地点选择均与实际调查结果相符,二者在交通流量峰值的偏差小于5%,在弹性活动地点选择分布的相关度大于90%。

Activity is the source of travel.Temporal-spatial characteristics are important indicators of travelers’ activity features and the causes of congestion.In this paper the authors proposed a multi-agent based reinforcement learning algorithm which could simulate activity time and location choice for travelers.Since road congestion condition was a dynamic parameter in the model,the environment could change dynamically with agent’s behaviors.This reflects interactions among travelers and interactions between travelers and the environment.The result analysis of typical travel patterns,traffic flow distribution and activity location choice show that this algorithm’s simulation could represent actual survey results.The deviation of peak flow is less than 5% while the correlation coefficient of activity location choice is more than 90%.