Train Rescheduling Algorithm using Pattern Description Language and Integrated Algorithm Framework

C. Hirai, N. Tomii, Y. Tashiro, S. Kondou, and A. Fujimori (Japan)


Train rescheduling, scheduling algorithm, heuristics, knowledge acquisition, meta-heuristics, railway.


We propose an algorithm and a framework for effective automatic train rescheduling. Intended for restoration from heavy train traffic disruption, our approach has inherent abilities to make effective train rescheduling plans. While the previous algorithm tries to make a train rescheduling plan in small steps, the proposed one surveys the train time table at first and applies “train rescheduling patterns” to prepare rescheduling plans. Applying actual train schedule data, we believe that our algorithm and framework presented in this paper can provide an effective rescheduling plan. Especially for a severe train traffic disruption caused by an accident requiring more than about an hour suspending train operations, our algorithm and framework are helpful for preparing adequate rescheduling plans for practical use.

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