Zoom-Based AutoEncoder for Origin-Destination Demand Prediction

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.