A Spatial-Temporal Attention Multi-Graph Convolution Network for Ride-Hailing Demand Prediction Based on Periodicity with Offset

TL;DR

This paper introduces a novel spatial-temporal attention multi-graph convolution network that leverages periodicity with offset for more accurate ride-hailing demand prediction, demonstrating state-of-the-art results on multiple real-world datasets.

Contribution

It proposes a new network architecture with spatial-temporal attention and a feature clustering layer, along with a data formulation that incorporates periodicity with offset for improved demand forecasting.

Findings

Achieves state-of-the-art performance on three real-world datasets.

Effectively captures temporal and spatial correlations in demand data.

Reduces computational burden with feature clustering.

Abstract

Ride-hailing service is becoming a leading part in urban transportation. To improve the efficiency of ride-hailing service, accurate prediction of transportation demand is a fundamental challenge. In this paper, we tackle this problem from both aspects of network structure and data-set formulation. For network design, we propose a spatial-temporal attention multi-graph convolution network (STA-MGCN). A spatial-temporal layer in STA-MGCN is developed to capture the temporal correlations by temporal attention mechanism and temporal gate convolution, and the spatial correlations by multigraph convolution. A feature cluster layer is introduced to learn latent regional functions and to reduce the computation burden. For the data-set formulation, we develop a novel approach which considers the transportation feature of periodicity with offset. Instead of only using history data during the same time period, the history order demand in forward and backward neighboring time periods from yesterday and last week are also included. Extensive experiments on the three real-world datasets of New-York, Chicago and Chengdu show that the proposed algorithm achieves the state-of-the-art performance for ride-hailing demand prediction.