ST-GSP: Spatial-Temporal Global Semantic Representation Learning for Urban Flow Prediction

Abstract

Urban flow prediction plays a crucial role in public transportation management and smart city construction. Although previous studies have achieved success in integrating spatial-temporal information to some extents, those models lack thoughtful consideration on global information and positional information in the temporal dimension, which can be summarized by three aspects: a) The models do not consider the relative position information of time axis, resulting in that the position features of flow maps are not effectively learned. b) They overlook the correlation among temporal dependencies of different scales, which lead to inaccurate global information representation. c) Those models only predict the flow map at the end of time sequence other than more flow maps before that, which results in neglecting parts of temporal features in the learning process. To solve the problems, we propose a novel model, Spatial-Temporal Global Semantic representation learning for urban flow Prediction (ST-GSP) in this paper. Specifically, for a), we design a semantic flow encoder that extracts relative positional information of time. Besides, the encoder captures the spatial dependencies and external factors of urban flow at each time interval. For b), we model the correlation among temporal dependencies of different scales simultaneously by using the multi-head self-attention mechanism, which can learn the global temporal dependencies. For c), inspired by the idea of self-supervised learning, we mask an urban flow map on the time sequence and predict it to pre-train a deep bidirectional learning model to catch the representation from its context. We conduct extensive experiments on two types of urban flows in Beijing and New York City to show that the proposed method outperforms state-of-the-art methods.