Local and Global Spatial-Temporal Networks for Traffic Accident Risk Forecasting

• Published in: Jisuanji kexue yu tansuo Vol. 15; no. 9; pp. 1694 - 1702
• Main Author: WANG Beibei, WAN Huaiyu, GUO Shengnan, LIN Youfang
• Format: Journal Article
• Language: Chinese
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 01.09.2021
• Subjects: graph convolutional network (gcn); multi-source spatial-temporal data; traffic accident forecasting; zero-inflated
• ISSN: 1673-9418
• DOI: 10.3778/j.issn.1673-9418.2008093

Traffic accident forecasting is very important for urban public security, emergency treatment and construc-tion planning. However, the following problems still exist when forecasting traffic accident risk. Firstly, traffic accidents are affected by multiple factors, such as weather and road conditions. Besides, there are multi-scale correlations in the spatial dimension, i.e. local region spatial-temporal correlation and global region spatial-temporal similarity. Meanwhile, there is zero-inflated issue in the forecasting because of few traffic accidents in reality. Therefore, it is very challenging to forecast traffic accidents accurately, and existing traffic accident forecasting methods cannot take all the above problems into account. A novel model, named local and global spatial-temporal networks (ST-RiskNet), for traffic accident risk forecasting is proposed. The ST-RiskNet takes multi-source factors that affect traffic accidents into consideration, such as time, weather, traffic flow. It uses a local reg