Co-Prediction of Multimodal Transportation Demands With Self-learned Spatial Dependence

Mingzhe Liu, Bowen Du, Leilei Sun

Abstract: Transportation demand prediction is a classic problem in intelligent transportation research. However, most exist studies have been focused on improving the prediction accuracy in a single demand mode, and there is a lack of understanding of the impact of multiple transportation modes. To this paper, we aim to uncover the interactions of multiple transportation modes and develop a co-prediction method for multimodal transportation demand prediction. Specifically, we first propose a self-learned spatial graph construction method, which automatically learns spatial dependencies of both homogeneous and heterogeneous transportation stations, and then constructs a mode-free spatial dependence graph of the studied transportation stations. Then, a spatiotemporal convolution module is provided to update the state of each station spatially and temporally according to its neighbor stations on the self-learned spatial graph. Moreover, we design an output layer to map the hidden state of each station to the demands of multimodal transportation stations. Finally, experimental results on real-world data have not only validated the effectiveness of the proposed method, but also revealed that co-prediction of multimodal transportation demands could always result in higher prediction performances than single-mode prediction methods as it takes the interactions of multiple transportation modes into account.