Forecasting Citywide Crowd Transition Process via Convolutional Recurrent Neural Networks

Perceiving and modeling urban crowd movements are of great importance to smart city-related fields. Governments and public service operators can benefit from such efforts as they can be applied to crowd management, resource scheduling, and early emergency warning. However, most prior research on urban crowd modeling has failed to describe the dynamics and continuity of human mobility, leading to inconsistent and irrelevant results when they tackle multiple homogeneous forecasting tasks as they can only be modeled independently. To overcome this drawback, we propose to model human mobility from a new perspective, which uses the citywide crowd transition process constituted by a series of transition matrices from low order to high order, to help us understand how the crowd dynamics evolve step-by-step. We further propose a Deep Transition Process Network to process and predict such new high-dimensional data, where novel grid embedding with Graph Convolutional Network, parameter-shared Convolutional LSTM, and High-Dimensional Attention mechanism are designed to learn the complicated dependencies in terms of spatial, temporal, and ordinal features. We conduct experiments on two datasets generated by a large amount of GPS data collected from a real-world smartphone application. The experiment results demonstrate the superior performance of our proposed methodology over existing approaches.