研究并提出了一种基于物联环境下拥堵道路汽车最优通行路径选择的“分布式”算法,通过计算物联环境下汽车行驶路况信息素,构建算法模型,推演拥堵道路汽车最优通行路径信息素发展趋势,标定拥堵道路重复路径优选信息,栅格化处理容易将通行道路信息素和路面障碍物重叠,由此选择基于局部最优和整体最优的拥堵道路汽车通行可行性路径。经过最优道路扩展运算和通行路径细化优选分析,在模拟运行环境下,测试验证了该算法优选方案及其决策支持下的最优通行路径的合理性、安全性与经济性。

The traditional path planning and selection system is based on a single target object to select the access road,which is often prone to problems such as path overlap,many nodes,and great safety hazards,and causes risks such as road path overlap.In order to obtain the optimal travel path of vehicles on congested roads in the IoT environment,this study designs and proposes a "distributed" multi-objective optimization algorithm for optimal vehicle travel paths under complex congested road conditions.By calculating the pheromone of vehicle driving conditions in the IoT environment,an algorithm model is constructed to deduce the development trend of the pheromone of the optimal passage of vehicles on the congested road,and the optimal information of the repeated route of the congested road is calibrated.Therefore,the feasible path for vehicles on the congested road based on the local optimum and the overall optimum is selected.After the optimal road expansion operation and the optimization analysis of the pass path refinement,in the simulated operating environment,the test verifies the rationality,safety and economy of the algorithm optimization scheme and the optimal pass path under the decision support.