  Real-time railway rescheduling is an important technique to enable
operational recovery in response to unexpected and dynamic conditions in a
timely and flexible manner. Current research relies mostly on OD based data and
model-based methods for estimating train passenger demands. These approaches
primarily focus on averaged disruption patterns, often overlooking the
immediate uneven distribution of demand over time. In reality, passenger demand
deviates significantly from predictions, especially during a disaster.
Disastrous situations such as flood in Zhengzhou, China in 2022 has created not
only unprecedented effect on Zhengzhou railway station itself, which is a major
railway 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-time
demand-responsive (RTDR) railway rescheduling problem focusing two specific
aspects, namely, volatility of the demand, and management of station
crowdedness. For the first time, we propose a data-driven approach using
real-time mobile data (MD) to deal with this RTDR problem. A hierarchical deep
reinforcement learning (HDRL) framework is designed to perform real-time
rescheduling in a demand-responsive manner. The use of MD has enabled the
modelling of passenger dynamics in response to train delays and station
crowdedness, and a real-time optimisation for rescheduling of train services in
view of the change in demand as a result of passengers' behavioural response to
disruption. Results show that the agent can steadily satisfy over 62% of the
demand with only 61% of the original rolling stock, ensuring continuous
operations without overcrowding. Moreover, the agent exhibits adaptability when
transferred to a new environment with increased demand, highlighting its
effectiveness in addressing unforeseen disruptions in real-time settings.
