车辆拥堵状态下的最优路径规划建模研究

Modeling Research of Optimal Path Planning in Status of Vehicle Congestion

研究车辆路径规划建模问题,在对拥堵的路线进行车辆调度的过程中,多是在假设每条路径的复杂程度相对固定的前提下,计算权值结果较小的路径将会被频繁挖掘,一旦复杂程度不同,固定权值方法会造成路线挖掘过程误差较大,造成选取效率降低,耗时严重。提出采用自调整粒子群算法的路径拥堵状态下最优车辆路径挖掘方法。在车辆拥堵区域将车辆看作粒子,对车辆的人拥挤程度进行度量,在多种约束性建立多权值衡量挖掘模型,通过实时调整参与挖掘的车辆的数目,降低最优路径挖掘算法的复杂程度,缩短了挖掘耗时,提高了挖掘的精确度。实验结果表明,利用改进算法能够有效降低挖掘的复杂程度,缩短了车辆行驶的时间。

The issue of vehicle path planning modeling was researched in the paper. In the process of vehicle scheduling for the congestion paths,most of them are under the premise of the hypothesis that the complexity of each path is relative fixed to the calculated weights,and the smaller paths will be mined frequently. Once the complexity is different,the fixed weight method can lead to a large error in the course of the path mining process,which leads to the decrease of the selection efficiency and the seriousness of time consuming. By using the self-adjusting particle swarm optimization algorithm,the optimal vehicle path mining method was proposed in the status of path congestion.In the area of vehicle congestion,the vehicle was regarded as a particle,and the congestion degree of the people in vehicle was measured. With a variety of constraints,the mining model of multiple weight measurement can be established. By adjusting the number of the vehicles involved in the mining in real-time,the complexity of the optimal path mining algorithm was reduced,the mining time was shortened,and the accuracy of the mining was improved.The experimental results show that the improved algorithm can effectively reduce the complexity of mining,and shorten the time of vehicle travel.