计及路网权值时变特性的全局最优路径规划

由于静态路径规划（static path planning，SPP）和滚动路径规划（rolling path planning，RPP）思想无法求解全局最优路径，提出了一种计及路网权值时变特性的全局最优路径规划方法（global optimal path planning，GOPP）。利用Vissim软件对重庆大学城某区域路网进行建模与仿真，采用改进的前向关联边数据结构存储路网拓扑关键要素及行程时间仿真数据，以此作为路径规划数据库。在此基础上，推导跨时段路段的实际权值，提出一种基于Dijkstra算法的GOPP方法。最后基于路径规划数据库，在证明经典Dijkstra算法相比智能启发式算法具有全局最优求解能力的基础上，分别采用SPP、RPP和GOPP方法在MATLAB环境下仿真得到3条规划路径，结果表明GOPP累计行程时间为1 158.7 s，相比SPP和RPP分别减少了212.7 s和57.6 s，有效验证了GOPP在缩短交通出行时间的优越性，对今后智能交通系统的发展具有一定的理论指导意义。

Global optimal path planning considering time-varying weight of road network

Because static path planning (SPP) and rolling path planning (RPP) cannot solve the global optimal path, a global optimal path planning method (GOPP) considering the time-varying characteristics of road network weights was proposed. Vissim software was used to model and simulate a regional road network in Chongqing University Town, and the improved forward associated edge data structure was used to store the road network topology key elements and travel time simulation data, which were used as the path planning database. On this basis, the actual weights of cross period road sections were derived, and a GOPP method based on Dijkstra algorithm was proposed. Based on the path planning database, the classical Dijkstra algorithm was proved to have the global optimal solution ability compared with the intelligent heuristic algorithm. Finally, three planning paths in Matlab software were simulated by using the SPP, RPP and GOPP methods. The results show that the cumulative travel time of GOPP is 1 158.7 s, which is 212.7 s and 57.6 s less than those of SPP and RPP, respectively, verifying that GOPP is superior in shortening travel time. The proposed GOPP has certain theoretical significance for the development of intelligent vehicles in the future.