%0 Journal Article %A LU Minchao %A LI Jianbo %A PANG Junjie %A LI Ying %A DONG Xueshi %T Multi-factor Spatio-Temporal Graph Convolution Network for Taxi Demand Prediction %D 2020 %R 10.3778/j.issn.1002-8331.1910-0294 %J Computer Engineering and Applications %P 266-273 %V 56 %N 24 %X

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.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1910-0294