A spatial-temporal hierarchical modeling framework for multi-step ride-hailing demand forecasting

Forecasting urban ride-hailing demand is the main duty in Intelligent Transportation Systems. Accurate predicting can significantly improve the efficiency of regional capacity allocation, promoting energy conversation and emission reduction. Currently, the mainstream approaches utilize advanced deep learning-based methods to model aspects of both temporal and spatial. However, these previous methods overlook the long-term temporal dependence of selection and the hierarchical nature of urban road networks. To this end, we propose a novel Spatial-Temporal Hierarchical Network (STHNet) for urban ride-hailing demand prediction. Specially, The Depthwise Separable Convolution Nerual Networks (DSCNNs) extract spatial features of the road network through channel-wise and point-wise convolution operations, while the Nested Long Short-Term Memory networks (NLSTMs) are used to capture the hierarchical temporal dependencies in sequential data. DSCNNs and NLSTMs are cascaded to form the basic module of the multi-step prediction framework, called Spatial-Temporal Hierarchical block. The block can be easily extended to other spatial-temporal modeling tasks. At the end of the network, we introduce a 3DCNN to learn Spatial-Temporal heterogeneity and integrate information. Furthermore, Teacher Forcing and secondary information are incorporated into STHNet to enhance training efficiency. Extensive experiment are conducted on a Xiamen transportation network with 64 regions shows that STHNet outperforms multiple State-Of-The-Art baselines. The qualitative results underscoring the practical engineering applicability of the proposed model.