摘要
行车路线优化是城市智能交通系统的研究热点之一,对整个交通系统的优化起着重要作用.分析了影响行车时间的各种因素,结合图论中最短路径算法,建立了基于RBF神经网络的路径代价函数模型.基于该函数模型,可以计算出交通图中任意给定两地间的时间最优路径.将该模型应用于实际路况进行有效性验证,得到了有实用价值的结果,说明了该模型的正确性和有效性.
Vehicle route optimization is one of the hot topics in research on urban intelligent transportation systems （ ITS）, and it plays an important role in the optimization of the entire transportation system. This paper analyzed various lactors that attect the travel time and established a path cost tunctlon model with an radial basis lunctlon neural network, based on the shortest paths algorithms in graph theory. By this function model, the time-orientedoptimal path between any two given places on a traffic map can be calculated. The model was applied to actual traffic to validate the effectiveness, and its results are of practical value, showing the correctness and validity of themodel.
出处
《智能系统学报》
2011年第5期424-431,共8页
CAAI Transactions on Intelligent Systems
关键词
智能交通
路径代价函数
行车路线优化
RBF神经网络
图论
intelligent transportation
path cost function
vehicle route optimization
radial basis function neural network
graph theory
