Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (24): 266-273.DOI: 10.3778/j.issn.1002-8331.1910-0294

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Multi-factor Spatio-Temporal Graph Convolution Network for Taxi Demand Prediction

LU Minchao, LI Jianbo, PANG Junjie, LI Ying, DONG Xueshi   

  1. College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China
  • Online:2020-12-15 Published:2020-12-15



  1. 青岛大学 计算机科学技术学院,山东 青岛 266071


Accurately predicting taxi demand can reduce the waiting time of taxi driver and passengers, and alleviate traffic in cities. However, most of the current research work ignores the impact of taxi historical inflows and interregional potential spatial dependency on taxi demand. In view of this, this paper considers multiple factors affecting taxi demand and establishes Multi-Factors Spatio-Temporal Graph Convolutional Network(MFSTGCN) in attempt to improve the accuracy of taxi demand prediction. Specifically, MFSTGCN designs four components to model the temporal dependence of taxi demand on adjacent time for demand sequence, daily demand sequence, historical inflow sequence and the potential spatial dependence between different regions. These components can capture the potential spatio-temporal representations of different factors through spatio-temporal convolutional block. To prove the performance of the proposed MFSTGCN, this paper compares it with five reference models, which are commonly used in traffic predication. Three metrics of RMSE, MAE and MAPE are applied for model evaluation. It demonstrates MFSTGCN’s effectiveness.

Key words: taxi demand, potential spatial dependency, spatio-temporal convolutional block



关键词: 出租车需求, 潜在空间依赖性, 时空卷积块