School of Earth Sciences, Zhejiang University, Hangzhou 310058, China.
Department of Traffic Management Engineering, Zhejiang Police College, Hangzhou 310053, China.
Int J Environ Res Public Health. 2023 Feb 15;20(4):3432. doi: 10.3390/ijerph20043432.
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.
预测交通违法行为在交通安全中起着关键作用。将深度学习与预测交通违法行为相结合已成为一个新的发展趋势。然而，现有的方法基于规则的空间网格，这导致了模糊的空间表达，并忽略了交通违法行为与道路网络之间的强相关性。空间拓扑图可以更准确地表达时空相关性，从而提高交通违法行为预测的准确性。因此，我们提出了一个基于道路网络的图注意网络（GATR）模型来预测交通违法行为的时空分布，该模型采用了结合历史交通违法行为特征、外部环境特征和城市功能特征的图注意网络模型。实验表明，GATR 模型可以更清晰地表达交通违法行为的时空分布模式，并且比 Conv-LSTM（RMSE=1.9180）具有更高的预测精度（RMSE=1.7078）。基于 GNNExplainer 的 GATR 模型的验证显示了道路网络的子图和特征的影响程度，这证明了 GATR 的合理性。GATR 可以为交通违法行为的预防和控制提供重要参考，提高交通安全水平。
