In metro system, unpredictable disturbances influence the normal operation and bring much inconvenience to passengers. This paper focuses on train timetable rescheduling (TTR) problem with considering practical operations in the metro management. At first, an optimization model that takes the deviation of rescheduled timetable, the total delay time of passengers and energy consumption as objective is developed. Meanwhile, the constraints, together with some practical rescheduling rules (e.g., the preprogrammed speed profiles, train detention strategy) are introduced. Secondly, the model is reformulated into an Markov decision process (MDP) with well defining the state, action and reward function, which is then solved by the proposed Q-learning approach. Finally, some case studies using the operational data of Beijing Yizhuang Subway Line are carried out to demonstrate the effectiveness of the proposed approach. The results indicate that a tradeoff solution among the optimization objectives can be obtained within a short time.
Metro Train Timetable Rescheduling Based on Q-learning Approach
2020-09-20
467183 byte
Conference paper
Electronic Resource
English
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