基于超网络的多模式交通的随机网络加载问题

基于超网络理论构建了城市多模式出行网络模型，同时考虑路径出行时间、换乘次数以及拥挤等影响因素，建立路径的广义费用函数。在此基础之上，重新定义了有效路径的含义。结合图论中深度优先遍历算法与回溯法的思想，提出有效路径的搜索算法，并通过改进的Dial算法实现对城市多模式出行网络上交通流的随机网络加载。最后，利用本文建立的多模式出行网络对算法的可行性和有效性进行验证。结果表明，该算法适用于求解城市多模式出行网络上交通流的随机网络加载问题，并且可以避免原始Dial算法在求解交通分配问题中可能出现的不合理结果。

Hyper-graph based stochastic multimodal traffic network assignment problem

We constructed a multimodal traffic network model based on the theory of hyper graph.Generalized route cost function was established with such influential factors as route travel time, transfer times and congestion. The concept of an effective route was further redefined. We presented the searching algorithm of all viable routes with depth first ergodic algorithm and backtracking algorithm. We also implemented stochastic network assignment of traffic flow in multimodal traffic network with improved Dial algorithm. The feasibility and effectiveness of the improved Dial algorithm were verified with the multimodal traffic network. Results show that the algorithm is applicable for solving stochastic network assignment of traffic flow in multimodal traffic network, and for avoiding the unreasonable results that may appear in original Dial algorithm.