Real Time Headway Predictions in Urban Rail Systems and Implications for Service Control: A Deep Learning Approach
This work addresses proactive operational control for rail operators to improve service consistency and passenger satisfaction, though it appears incremental as it adapts existing deep learning methods to a specific domain problem.
This study tackles real-time train headway prediction in urban metro systems using a ConvLSTM model that incorporates planned terminal headways as input, enabling dispatchers to evaluate control decisions without intensive simulations. The model demonstrates promising predictions on a large-scale metro dataset, offering actionable insights for optimizing dispatching strategies.
Efficient real-time dispatching in urban metro systems is essential for ensuring service reliability, maximizing resource utilization, and improving passenger satisfaction. This study presents a novel deep learning framework centered on a Convolutional Long Short-Term Memory (ConvLSTM) model designed to predict the complex spatiotemporal propagation of train headways across an entire metro line. By directly incorporating planned terminal headways as a critical input alongside historical headway data, the proposed model accurately forecasts future headway dynamics, effectively capturing both their temporal evolution and spatial dependencies across all stations. This capability empowers dispatchers to evaluate the impact of various terminal headway control decisions without resorting to computationally intensive simulations. We introduce a flexible methodology to simulate diverse dispatcher strategies, ranging from maintaining even headways to implementing custom patterns derived from observed terminal departures. In contrast to existing research primarily focused on passenger load predictioning or atypical disruption scenarios, our approach emphasizes proactive operational control. Evaluated on a large-scale dataset from an urban metro line, the proposed ConvLSTM model demonstrates promising headway predictions, offering actionable insights for real-time decision-making. This framework provides rail operators with a powerful, computationally efficient tool to optimize dispatching strategies, thereby significantly improving service consistency and passenger satisfaction.