A Spatiotemporal Bidirectional Attention-Based Ride-Hailing Demand Prediction Model: A Case Study in Beijing During COVID-19

Abstract

The COVID-19 pandemic has severely affected urban transport patterns, including the way residents travel. It is of great significance to predict the demand of urban ride-hailing for residents’ healthy travel, rational platform operation, and traffic control during the epidemic period. In this paper, we propose a deep learning model, called MOS-BiAtten, based on multi-head spatial attention mechanism and bidirectional attention mechanism for ride-hailing demand prediction. The model follows the encoder-decoder framework with a multi-output strategy for multi-steps prediction. The pre-predicted result and the historical demand data are extracted as two aspects of bidirectional attention flow, so as to further explore the complicated spatiotemporal correlations between the historical, present and future information. The proposed model is evaluated on the real-world dataset during COVID-19 in Beijing, and the experimental results demonstrate that MOS-BiAtten achieves a better performance compared with the state-of-art methods. Meanwhile, another dataset is used to verify the generalization performance of the model.