The operation of urban rail transit is inevitably affected by disturbances in practice, causing the original train timetable and rolling stock circulation to be infeasible. This paper addresses the real-time train rescheduling problem through a novel two-step optimization framework. To realign train operations with the original plan, the first step manages traffic flow by optimizing dispatching measures, including retiming, cancellation, short-turning, and backup rolling stock utilization. To maintain the highest possible service quality during the transition period, the second step introduces stop-skipping and further fine-tunes the train timetable. Both steps are formulated as mixed-integer nonlinear programming models, and the second-step model is heuristically decomposed. For computational tractability, mathematical models are transformed using some linearization techniques and then solved according to the prescribed procedure. Numerical experiments based on a small-scale case and real-world data of the Beijing Yizhuang Metro Line show that the proposed two-step optimization framework can satisfy the real-time requirements and outperform the current rescheduling method employed in the automatic train supervision system. Furthermore, the framework is proven to be applicable to different disturbance durations and locations.
A Two-Step Optimization Framework for Real-Time Train Rescheduling in an Urban Rail Transit Line
IEEE Transactions on Intelligent Transportation Systems ; 26 , 6 ; 9065-9079
01.06.2025
6531064 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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