基于最小费用最大流改进算法的多种交通方式开行方案协同优化研究

A Study on the Collaboration and Optimization for Multiple Traffic Modes Operation Plan based on the Minimum Cost and Maximum Flow Improvement Algorithm

为降低日常非拥挤状态下城际间综合运输系统的客运总成本,对运输通道内多种交通方式的列车开行方案或车辆运行作业计划进行了协同优化。以广义运输成本最小为目标函数,以成本和客流为约束条件,将协同优化归纳为最小费用最大流问题。为提高计算效率,根据图论中最小费用最大流常用算法之一的最小费用路算法设计出协同优化改进算法。以示例路网为基础,构建协同优化合适算例进行改进算法应用。结果表明,协同优化可以有效降低非拥挤状态下综合运输系统客运总成本,其改进算法表现出良好的有效性和准确性。

To reduce the total passenger transport cost of the intercity integrated transport system in the daily non-congested state, this paper collaboratively optimizes the train operation plan or vehicle running plan of the multiple traffic modes in the system. Collaborative optimization is summed up to the minimum cost maximum flow problem which takes the minimum social generalized transport cost as objective function and takes the cost and passenger flow as constraints in the public perspective. It designs plans to improve related algorithms according to one of the most commonly used algorithms about the minimum cost maximum flow in graph theory to improve the computational efficiency. After using an appropriate numerical example of collaborative optimization based on the example road network which is designed to improve algorithm application, the results show that the collaborative optimization can effectively reduce the total passenger transport cost of the integrated transport system under non-congested conditions, and the improved algorithm is effective and accurate in solving problems.