Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 385-391.

• Big Data & Data Mining • Previous Articles     Next Articles

Citywide Crowd Flows Prediction Based on Spatio-Temporal Recurrent Convolutional Networks

GUO Sheng-nan, LIN You-fang, JIN Wen-wei, WAN Huai-yu   

  1. School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China;
    Beijing Key Laboratory of Traffic Data Analysis and Mining,Beijing Jiaotong University,Beijing 100044,China;
    Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Beijing Jiaotong University,Beijing 100044,China
  • Online:2019-06-14 Published:2019-07-02

Abstract: Accurately forecasting the crowd flows in urban areas can provide effective decision-making support for traffic management and citizens’ travel.The crowd flows in each urban region have strong correlations in both temporal dimensionsand spatial dimensions.These complex factors bring great challenges to accurate predictions.A novel neural network structure named attention-based spatio-temporal recurrent convolution networks (ASTRCNs) was proposed,which can simultaneously model various factors that affect the crowd flows.ASTRCNs consists of three components,which can respectively capture the short-term dependences,the daily periodicity influence and the weekly patterns of the crowd flows.Experimental results on a real data set of crowd flows in Beijing demonstrate that the proposed ASTRCNs outperforms the classical time series methods and the existing deep-learning based prediction methods.

Key words: Crowd flows prediction, Deep learning, Recurrent convolutional networks, Spatio-Temporal data

CLC Number: 

  • TP181
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