GC-LSTM: A Deep Spatiotemporal Model for Passenger Flow Forecasting of High-Speed Rail Network

Accurate passenger flow forecasting is vital for passenger flow management and planning. However, it is a challenging task in practice as passenger flow of a certain transportation network is affected by complex factors including the unstructured spatial dependencies constrained by the transportation network topological structure, intra-location correlations (inflow relates to outflow), temporal dependencies, and exogenous factors. To cope with the aforementioned challenges, this paper proposes a novel deep learning-based spatiotemporal passenger flow forecasting model, named Graph Convolutional-Long Short Term Memory (GC-LSTM). The designed architecture of GC-LSTM extends convolution with Graph Convolutional Network (GCN) to handle graph-based spatial dependencies, while LSTM in the architecture is employed to capture the long-term temporal dependencies as well as nonlinear traffic dynamics. The proposed method also enables collectively forecasting of inflow and outflow at the location of interest within transportation network by capturing the intra-location correlations in parallel views. Then the proposed method is validated by the real-world passenger flow data of China High-Speed Rail (HSR) network, and the experimental results show that GC-LSTM can well capture the graph-based spatial and temporal dependencies and outperform state-of-art baselines in terms of forecasting accuracy.