基于拥堵预测的路径规划算法研究

Research on Route Planning Based on Congestion Prediction

随着城市出行车辆数的急剧增加,城市交通路网拥堵日益严重,因此研究路网通行效率最大化、个人通行时间和路径最优化具有重要意义。目前智慧交通大多采用周期性监测拥堵并重新规划路径的方法,这类方法得到的最优路径往往导致下次重规划的绕行,不利于全局的最优化。该文提出一种基于拥堵预测的路径规划算法,通过分析大量历史交通流数据的时空相关性预测未来时刻的交通流参数,再通过熵权法和模糊综合判定得出拥堵状态分级,之后综合考虑当前时刻和未来时刻拥堵状况从而规划出更加合理的路径。仿真结果表明,与基于重规划的动态路径规划算法相比,该算法在行驶时间上减少5.35%,在行驶距离上减少11.72%。

With the rapid increase of vehicles,the road network has become increasingly congested.It is of great significance to study the maximization of road network traffic efficiency,personal traffic time and path optimization.The existing methods periodically monitor congestion and replan paths.The previous path obtained by this kind of algorithm often leads to the detour of the next replanning,which is not conducive to the global optimization.This paper proposes a path planning algorithm based on congestion prediction.By analyzing the temporal and spatial correlation in a large number of historical traffic flow data,the traffic flow parameters at the future time are predicted,and then the congestion state classification is obtained through entropy weight method and fuzzy comprehensive judgment.A more reasonable path is planned by comprehensively considering the current and future congestion conditions.The simulation results show that compared with the dynamic path planning algorithm,this algorithm reduces the travel time by 5.35%and the travel distance by 11.72%.