LSTOD: Latent Spatial-Temporal Origin-Destination prediction model and its applications in ride-sharing platforms

Fan Zhou, Haibo Zhou, Hongtu Zhu

Abstract: Origin-Destination (OD) flow data is an important instrument in transportation studies. Precise prediction of customer demands from each original location to a destination given a series of previous snapshots helps ride-sharing platforms to better understand their market mechanism. However, most existing prediction methods ignore the network structure of OD flow data and fail to utilize the topological dependencies among related OD pairs. In this paper, we propose a latent spatial-temporal origin-destination (LSTOD) model, with a novel convolutional neural network (CNN) filter to learn the spatial features of OD pairs from a graph perspective and an attention structure to capture their long-term periodicity. Experiments on a real customer request dataset with available OD information from a ride-sharing platform demonstrate the advantage of LSTOD in achieving at least 6.5% improvement in prediction accuracy over the second best model.

Keywords: Origin-Destination Flow, Spatial Adjacent Convolution Network, Periodically Shift Attention Mechanism

TL;DR: We propose a purely convolutional CNN model with attention mechanism to predict spatial-temporal origin-destination flows.