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目前城市道路交通普遍存在交通拥挤、交通出行困难等问题。尤其是一些大城市,交通拥挤问题已成为制约城市进一步发展的重要问题。因此,提高出行者的出行效率和可靠性对解决交通拥挤问题具有重大意义。城市道路交通网络是一个典型的动态随机网络,网络中弧和节点的耗费是随机的,且随时间变化。其最优路径问题可以转化为图论网络中的最短路径问题。提出一种基于蒙特卡罗模拟和遗传算法的动态随机网络最短路径算法来解决城市道路交通网络的最优路径问题,并提出基于出行时长95%可靠性的最优路径选择方法来保证出行时间的可靠性。实验表明该算法可以很好地解决城市道路交通网络出行时间可靠性的问题,可以很好地运用到交通出行的路径规划中去。
At present, urban traffic congestion is generally congested, traffic travel difficulties and other issues. Especially in some big cities, the problem of traffic congestion has become an important issue restricting the further development of cities. Therefore, improving the travel efficiency and reliability of travelers is of great significance to solve the traffic congestion problem. Urban road traffic network is a typical dynamic random network, the cost of arcs and nodes in the network is random, and changes with time. The optimal path problem can be transformed into the shortest path problem in graph theory network. A shortest path algorithm of dynamic stochastic networks based on Monte Carlo simulation and genetic algorithm is proposed to solve the optimal path problem of urban road network. The optimal route selection method based on travel time 95% reliability is proposed to ensure the travel time reliability. Experiments show that this algorithm can solve the problem of the reliability of travel time of urban road traffic network well and can be well applied to the path planning of traffic travel.