ARFGCN: Adaptive Receptive Field Graph Convolutional Network for Urban Crowd Flow Prediction

Genan Dai, Hu Huang, Xiaojiang Peng, Bowen Zhang () and Xianghua Fu ()
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Genan Dai: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
Hu Huang: Guangdong Key Laboratory for Intelligent Computation of Public Service Supply, Shenzhen 518055, China
Xiaojiang Peng: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
Bowen Zhang: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
Xianghua Fu: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China

Mathematics, 2024, vol. 12, issue 11, 1-14

Abstract: Urban crowd flow prediction is an important task for transportation systems and public safety. While graph convolutional networks (GCNs) have been widely adopted for this task, existing GCN-based methods still face challenges. Firstly, they employ fixed receptive fields, failing to account for urban region heterogeneity where different functional zones interact distinctly with their surroundings. Secondly, they lack mechanisms to adaptively adjust spatial receptive fields based on temporal dynamics, which limits prediction performance. To address these limitations, we propose an Adaptive Receptive Field Graph Convolutional Network (ARFGCN) for urban crowd flow prediction. ARFGCN allows each region to independently determine its receptive field size, adaptively adjusted and learned in an end-to-end manner during training, enhancing model prediction performance. It comprises a time-aware adaptive receptive field (TARF) gating mechanism, a stacked 3DGCN, and a prediction layer. The TARF aims to leverage gating in neural networks to adapt receptive fields based on temporal dynamics, enabling the predictive network to adapt to urban regional heterogeneity. The TARF can be easily integrated into the stacked 3DGCN, enhancing the prediction. Experimental results demonstrate ARFGCN’s effectiveness compared to other methods.

Keywords: crowd flow prediction; graph convolutional network; time-aware gating mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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