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Real-time railway rescheduling is an important technique to enableoperational recovery in response to unexpected and dynamic conditions in atimely and flexible manner. Current research relies mostly on OD based data andmodel-based methods for estimating train passenger demands. These approachesprimarily focus on averaged disruption patterns, often overlooking theimmediate uneven distribution of demand over time. In reality, passenger demanddeviates significantly from predictions, especially during a disaster.Disastrous situations such as flood in Zhengzhou, China in 2022 has created notonly unprecedented effect on Zhengzhou railway station itself, which is a majorrailway hub in China, but also other major hubs connected to Zhengzhou, e.g.,Xi'an, the closest hub west of Zhengzhou. In this study, we define a real-timedemand-responsive (RTDR) railway rescheduling problem focusing two specificaspects, namely, volatility of the demand, and management of stationcrowdedness. For the first time, we propose a data-driven approach usingreal-time mobile data (MD) to deal with this RTDR problem. A hierarchical deepreinforcement learning (HDRL) framework is designed to perform real-timerescheduling in a demand-responsive manner. The use of MD has enabled themodelling of passenger dynamics in response to train delays and stationcrowdedness, and a real-time optimisation for rescheduling of train services inview of the change in demand as a result of passengers' behavioural response todisruption. Results show that the agent can steadily satisfy over 62% of thedemand with only 61% of the original rolling stock, ensuring continuousoperations without overcrowding. Moreover, the agent exhibits adaptability whentransferred to a new environment with increased demand, highlighting itseffectiveness in addressing unforeseen disruptions in real-time settings.

A Mobile Data-Driven Hierarchical Deep Reinforcement Learning Approach  for Real-time Demand-Responsive Railway Rescheduling and Station Overcrowding  Mitigation