Citation

Liu X, Lu J, Chen X, Fong YHC, Ma X, Zhang F. Accid. Anal. Prev. 2023; 192: e107262.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.aap.2023.107262

PMID

37598458

Abstract

The urban road transportation has presented a high probability of crash occurrence, and the aim of the present study is to evaluate the crash risk for urban road networks. However, the irregular structure of urban road networks, the high-dimensional spatio-temporal correlations among multi-source risks (i.e., the contributing risks from traffic flow, meteorological conditions, road design, and so forth), and the issue of data imbalance have brought challenges to this topic. To solve these issues, an Attention based Spatio-Temporal Graph Convolutional Network (ASTGCN) model with focal loss function is used for the first time to evaluate crash risk on an urban road network. This work can be summarized as (1) adopting the spatio-temporal graph convolution structure to capture the spatio-temporal properties and characterize the multi-source risks; (2) utilizing an attention mechanism network to address the critical contributing risks during crash risk evaluation; (3) introducing the focal loss function to improve the model performance impacted by the imbalanced data; and (4) investigating the different contributions of multi-source risks to model performance. The evaluation performance is tested in a real-world urban road traffic network. The raw data consists of 1239 crash records with corresponding datasets of traffic flow characteristics, meteorological conditions, road attributes and the topological structure of the road network. At the same time, three baseline models Artificial Neural Network (ANN), Random Forest (RF), and Deep Spatio-Temporal Graph Convolutional Network (DSTGCN) are compared to the proposed ASTGCN on the same datasets. Overall, the results show that ASTGCN outperforms the baseline models in several evaluation metrics. ASTGCN with focal loss function further improves performance by tackling the issues of dataset imbalance. Additionally, it is also found that the traffic flow risk is most crucial to model performance. The findings of the present study indicate that the proposed model can efficiently evaluate dynamic crash risk for urban road networks, which will benefit the safety management of urban road transportation.

Language: en

Keywords

Crash risk evaluation; Graph convolution network; Imbalanced data; Multi-source risks; Urban road networks