基于低密度浮动车数据的在建工地园区车辆路径规划模型及算法研究  

Vehicle Path Planning Model & Algorithm for Construction Site by Low-density Floating Vehicle Data

作　　者：潘新昊 刘轶鹏 郭鑫铭 王建柱 吴建清 Pan Xinhao;Liu Yipeng;Guo Xinming;Wang Jianzhu;Wu Jianqing(School of Qilu Transportation,Shandong University,Jinan 250002,China)

机构地区：[1]山东大学齐鲁交通学院,山东济南250002

出　　处：《市政技术》2024年第10期19-23,226,共6页Journal of Municipal Technology

基　　金：山东省重点研发计划(2020CXGC010118)。

摘　　要：通过统筹车辆路径规划方法与求解算法,对道路长度、由浮动车技术获得的车辆坐标等数据进行耦合分析,建立了车辆路径规划模型,克服了需要依靠高密度浮动车技术才能进行有效计算的痛点问题,并针对经典车辆路径规划算法的缺陷,对Dijkstra算法的搜索方式和蚁群算法的路径选择、信息素更新等环节进行了改进,提出了优化后的Dijkstra-ACO算法,依托模拟路网验证了该算法的有效性,最后依托实际在建工地园区路网实例验证了该车辆路径规划模型的优越性与可行性。By the vehicle path planning method and solution algorithm,the road length,vehicle coordinates obtained by floating vehicle technology and other data are coupling analyzed,and a vehicle path planning model is established,which overcomes the pain point problem that should be effective calculated by high-density floating vehicle technology.Aiming at the defects of classical vehicle path planning algorithms,the search method of Dijkstra algorithm and the route selection of ant colony algorithm and pheromone updating were improved,and the improved Dijkstra-ACO algorithm was proposed.The effectiveness of the algorithm was verified by the simulation of road net-work.Finally,the superiority and feasibility of the vehicle route planning model was verified by relying on the actual road network of the site park.

关 键 词：车辆路径规划 在建工地园区 Dijkstra-ACO算法 低密度浮动车数据 

分 类 号：U116.2[交通运输工程]