Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting
Hong Kong University of Science and Technology · University of Southern California · +2 more institutions
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…
Citation impact
- FWCI
- 161.69
- Percentile
- 100%
- References
- 40
Authors
7- GXGeng XuCorresponding
Hong Kong University of Science and Technology
- YLYaguang Li
University of Southern California, California Southern University
- LWLeye Wang
Hong Kong University of Science and Technology
- LZLingyu Zhang
Lux Research (United States)
- QYQiang Yang
Hong Kong University of Science and Technology
Topics & keywords
- Computer science
- Convolution (computer science)
- Graph
- Demand forecasting
- Euclidean geometry
- Recurrent neural network
- ENCODE
- Euclidean distance