GATR: A Road Network Traffic Violation Prediction Method Based on Graph Attention Network

Yuquan Zhou, Yingzhi Wang, Feng Zhang (), Hongye Zhou, Keran Sun and Yuhan Yu
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Yuquan Zhou: School of Earth Sciences, Zhejiang University, Hangzhou 310058, China
Yingzhi Wang: Department of Traffic Management Engineering, Zhejiang Police College, Hangzhou 310053, China
Feng Zhang: School of Earth Sciences, Zhejiang University, Hangzhou 310058, China
Hongye Zhou: School of Earth Sciences, Zhejiang University, Hangzhou 310058, China
Keran Sun: School of Earth Sciences, Zhejiang University, Hangzhou 310058, China
Yuhan Yu: School of Earth Sciences, Zhejiang University, Hangzhou 310058, China

IJERPH, 2023, vol. 20, issue 4, 1-18

Abstract: Prediction of traffic violations plays a key role in transportation safety. Combining with deep learning to predict traffic violations has become a new development trend. However, existing methods are based on regular spatial grids which leads to a fuzzy spatial expression and ignores the strong correlation between traffic violations and road network. A spatial topological graph can express the spatiotemporal correlation more accurately and then improve the accuracy of traffic violation prediction. Therefore, we propose a GATR (graph attention network based on road network) model to predict the spatiotemporal distribution of traffic violations, which adopts a graph attention network model combined with historical traffic violation features, external environmental features, and urban functional features. Experiments show that the GATR model can express the spatiotemporal distribution pattern of traffic violations more clearly and has higher prediction accuracy (RMSE = 1.7078) than Conv-LSTM (RMSE = 1.9180). The verification of the GATR model based on GNN Explainer shows the subgraph of the road network and the influence degree of features, which proves GATR is reasonable. GATR can provide an important reference for prevention and control of traffic violations and improve traffic safety.

Keywords: traffic violation; spatiotemporal prediction; graph attention network; road network; urban function (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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