ST-KFNet-Based Framework for Online Metro Passenger OD Demand Forecasting under Uncertainty
TL;DR
A novel spatiotemporal Kalman filter network (ST-KFNet) framework for metro demand forecasting by integrating an autoregressive integrated moving average module, a Kalman filter (KF) module, and a convolutional neural network (CNN)-based variational autoencoder (VAE) module is proposed.
Abstract
Accurate passenger demand prediction is crucial for efficient metro operations, yet remains challenging due to complex network structures, temporal variability, and travel uncertainties. This paper proposes a novel spatiotemporal Kalman filter network (ST-KFNet) framework for metro demand forecasting by integrating an autoregressive integrated moving average (ARIMA) module, a Kalman filter (KF) module, and a convolutional neural network (CNN)-based variational autoencoder (VAE) module. ARIMA constructs the state prediction equation, further enhanced by a neural-network-based predictor. CNN-based VAE captures spatial dependencies and temporal dynamics, refining the KF’s state transition for improved accuracy. The KF then provides real-time, online predictions, effectively handling both stationary and nonstationary uncertainties. By combining the strengths of statistical modeling and deep learning, ST-KFNet achieves high predictive accuracy under normal conditions and robust performance under uncertainty, such as unplanned station closures. Empirical evaluations on a metro data set from Hangzhou show that ST-KFNet consistently outperforms existing benchmarks, including graph convolutional network (GCN)-based methods as well as ARIMA, KF, and CNN-based VAE models.
