MTLMetro: A Deep Multi-Task Learning Model for Metro Passenger Demands Prediction

Accurate prediction of passenger demand is essential for the efficient operation and management of metro systems. In practical scenarios, strategies to enhance metro service quality often require passenger demand information on multiple fronts, such as inflow to a station, outflow from a station, as well as transition flow between entry/exit stations. While predictions for a single type of passenger demand have been extensively studied, limited attention was paid to jointly predicting multiple demands. This problem is challenging due to the complex relationships among multiple demands (e.g., inflow is only correlated with historical inflow, while the outflow is not only correlated with outflow but also determined by the inflow) and the imbalanced training issue of multiple prediction tasks. To address these challenges, this paper proposes a deep multi-task learning (MTL) model called MTLMetro to co-predict multiple demands in metro systems. More specifically, we deploy the message-passing schemes in graph neural networks (GNNs) as the knowledge-sharing mechanisms in the MTL model to capture the inherent relationships among multiple demands. To balance the training of multiple tasks, we introduce a novel weighting scheme named dynamic weight average (DWA), which can dynamically adapt relative weight for each task. In addition, the partial observability problem of transition flow is also considered in MTLMetro in an end-to-end manner. Empirical evaluation on a real-world dataset demonstrates MTLMetro’s superior performance across the different demand prediction tasks when compared to several benchmarks. Further ablation experiments verify the effectiveness of the proposed modules and the weighting method.