Taxi Origin-Destination Demand Prediction with Contextualized Spatial-Temporal Network

Abstract

Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of the previous methods only considered the taxi demand prediction in origin regions, while ignoring the modeling of the specific situation of the destination passengers. In this paper, we present a more challenging task, called taxi origin-destination demand prediction, which aims at predicting the taxi demand between all origin-destination~(OD) pairs in a future time interval. Its main challenges lie in how to effectively capture the diverse contextual information to learn the demand patterns. We address this problem with a novel Contextualized Spatial-Temporal Network (CSTN), which can effectively capture various context of taxi demand into a unified framework. Specifically, the proposed network consists of three components for the modeling of local spatial context (LSC), temporal evolution context (TEC) and global correlation context (GCC) respectively. Extensive experiments and evaluations on a large-scale dataset well demonstrate the significant superiority of our CSTN over other compared methods of taxi origin-destination demand prediction.