Zoom-Based AutoEncoder for Origin-Destination Demand Prediction

Xiaojian Ma, Liangzhe Han, Gang Wang, Xu Liu, Tongyu Zhu

Abstract: The use of deep neural networks for traffic demand forecasting has garnered significant attention from both academic and industrial communities. Compared with the traditional traffic flow forecasting task, the Origin-Destination(OD) demand prediction task is more valuable and challenging, and several methods have been proposed for OD demand prediction. However, most existing methods follow a general technical route to aggregate historical information spatially and temporally. This paper proposes an alternative approach to predict Origin-Destination demand, named Zoom-based AutoEncoder for Origin-Destination demand prediction (ODZAE). The main objective of our research is to enhance the integration of diverse inherent patterns in real-world OD demand data in a more efficient manner. Besides, we proposed a zoom operation to learn spatial relationships between traffic nodes and 3DGCN to simultaneously model spatial and temporal dependencies. We have conducted experiments on two real-world datasets from Beijing Subway and New York Taxi, and the results demonstrate the superiority of our model against the state-of-art approaches.