Multi-Task Spatial-Temporal Graph Attention Network for Taxi Demand Prediction

Abstract

Taxi demand prediction is of much importance, which enables the building of intelligent systems and smart city. It is necessary to predict taxi demand accurately to schedule taxi fleet in a reasonable and efficient way and to reduce the pressure of traffic jam. However, the taxi demand involves complex and non-linear spatial-temporal impacts. The superiority of deep learning makes people explore the possibility to apply it to traffic prediction. State-of-the-art methods on taxi demand prediction only capture static spatial correlations between regions (e.g., Using static graph embedding) and only take taxi demand data into consideration. We propose a Multi-Task Spatial-Temporal Graph Attention Network (MSTGAT-Net) framework which models the correlations between regions dynamically with graph-attention network and captures the correlation between taxi pick up and taxi drop off with multi-task training. To the best of our knowledge, it is the first paper to address the taxi demand prediction problem with graph attention network and multi-task learning. Experiments on real-world taxi data show that our model is superior to state-of-the-art methods.