Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting

Authors

  • Xu Geng The Hong Kong University of Science and Technology
  • Yaguang Li University of Southern California
  • Leye Wang The Hong Kong University of Science and Technology
  • Lingyu Zhang Didichuxing Inc.
  • Qiang Yang The Hong Kong University of Science and Technology
  • Jieping Ye Didichuxing Inc.
  • Yan Liu University of Southern California

DOI:

https://doi.org/10.1609/aaai.v33i01.33013656

Abstract

Region-level demand forecasting is an essential task in ridehailing services. Accurate ride-hailing demand forecasting can guide vehicle dispatching, improve vehicle utilization, reduce the wait-time, and mitigate traffic congestion. This task is challenging due to the complicated spatiotemporal dependencies among regions. Existing approaches mainly focus on modeling the Euclidean correlations among spatially adjacent regions while we observe that non-Euclidean pair-wise correlations among possibly distant regions are also critical for accurate forecasting. In this paper, we propose the spatiotemporal multi-graph convolution network (ST-MGCN), a novel deep learning model for ride-hailing demand forecasting. We first encode the non-Euclidean pair-wise correlations among regions into multiple graphs and then explicitly model these correlations using multi-graph convolution. To utilize the global contextual information in modeling the temporal correlation, we further propose contextual gated recurrent neural network which augments recurrent neural network with a contextual-aware gating mechanism to re-weights different historical observations. We evaluate the proposed model on two real-world large scale ride-hailing demand datasets and observe consistent improvement of more than 10% over stateof-the-art baselines.

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Published

2019-07-17

How to Cite

Geng, X., Li, Y., Wang, L., Zhang, L., Yang, Q., Ye, J., & Liu, Y. (2019). Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 3656-3663. https://doi.org/10.1609/aaai.v33i01.33013656

Issue

Section

AAAI Technical Track: Machine Learning