WCSE 2022
ISBN: 978-981-18-3959-7 DOI: 10.18178/wcse.2022.06.018

Characterization of Urban Road Traffic Accidents based on Data Mining

Weifan Zhong, Jie Yuan, Yue Ren, Lijing Du

Abstract— Road traffic safety is a social issue of concern, and China is one of the countries with the largest number of total road traffic accidents and accident fatalities. The occurrence of traffic accidents mainly includes four factors: people, vehicles, facilities, and the environment. Under the background of big data, to explore the relationship between various factors of urban road traffic accidents and analyze the characteristics of traffic accidents given the current situation of frequent traffic accidents and serious losses, this paper collects week, time period, weather, road conditions, alarm subcategories, collision types, and accident vehicles as research factors. The overall association rule, week and time period association rule, road and climate association rule, and accident vehicle association rule are applied to the road traffic accident factors. The results show that there is a correlation between road traffic accident factors and accident outcomes, revealing important factors that cause traffic accidents.

Index Terms—urban roads, traffic accidents, accident features, association rules, Apriori algorithm

Weifan Zhong, Jie Yuan, Yue Ren, Lijing Du
Wuhan University of Technology, CHINA


Cite: Weifan Zhong, Jie Yuan, Yue Ren, Lijing Du, "Characterization of Urban Road Traffic Accidents based on Data Mining, " Proceedings of 2022 the 12th International Workshop on Computer Science and Engineering (WCSE 2022), pp. 117-125, June 24-27, 2022.