A deep reinforcement learning approach for real-time demand-responsive railway rescheduling to mitigate station overcrowding using mobile data
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Real-time railway rescheduling is a timely and flexible technique to automatically alter the operation schedule in response to time-varying conditions. Current research lacks data-driven approaches that capture real-time passenger mobility during railway disruptions, relying mostly on OD-based data and model-based methods for estimating demands of trains. Meanwhile, the schedule-updating principles for a long-term disruption overlook the uneven distribution of demand over time. To fill this gap, this paper proposes a demand-responsive approach by inferring real-world passenger mobility from mobile data (MD) to facilitate real-time rescheduling. Unlike network-level approaches, this paper focuses on a heavy-demand station upstream of the disrupted area. The objective is to reschedule all trains on multiple routes passing through this target station, which have been affected by a severe emergency event such as a natural disaster. Particular attention should be given to avoiding the accumulation of overcrowded passengers at this station, to prevent additional accidents arising from overcrowding. This research addresses the challenges associated with this scenario, including the dynamics of arriving and leaving of passengers, station overcrowding, rolling stock shortage, open-ended disruption duration, integrated rescheduling on multiple routes, and delays due to detours. A deep reinforcement learning (DRL) framework is proposed to determine the optimal rescheduled timetable, route stops, and rolling stock allocation, while considering real-time demand satisfaction, station overcrowding, train capacity utilization, and headway safety.
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