Among the most commonly used methods of scheduling train stops are practical experience and various “one-step” optimal models. These methods face problems of direct transferability and computational complexity when considering a large-scale high-speed rail (HSR) network such as the one in China. This paper introduces a two-stage approach for train stop scheduling with a goal of efficiently organizing passenger traffic into a rational train stop pattern combination while retaining features of regularity, connectivity, and rapidity (RCR). Based on a three-level station classification definition, a mixed integer programming model and a train operating tactics descriptive model along with the computing algorithm are developed and presented for the two stages. A real-world numerical example is presented using the Chinese HSR network as the setting. The performance of the train stop schedule and the applicability of the proposed approach are evaluated from the perspective of maintaining RCR.
Train rescheduling is crucial and hard work in railway operation. The paper presented a greedy algorithm that could achieve good solution in a short time firstly. It then analyzed the characteristics of the algorithm further and added some extra rescheduling strategies in the process of greedy choice in each step to improve the quality of solution. The experiments' data showed that it was pretty effective.
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