Analyzing the Risk Factors of Traffic Accident Severity Using a Combination of Random Forest and Association Rules
Source abstract
Abstract
This study explores risk factors influencing the at-fault party in traffic accidents and analyzes their impact on traffic accident severity. Based on the traffic accident data of Shenyang City, Liaoning Province, China, from 2018 to 2020, 19 attribute variables including road attributes, time attributes, environmental attributes, and characteristics of the at-fault parties with either full responsibility, primary responsibility, or equal responsibility of the traffic accidents were extracted and analyzed in conjunction with the built environment attributes, such as road network density and POI (points of interest) density at the sites of traffic accidents. Using the RF-SHAP method to determine the relative importance of risk factors influencing the severity of traffic accidents with either motor vehicles or vulnerable groups at-fault, the top ten risk factors influencing the severity of traffic accidents with vulnerable road users as the at-fault parties are: functional zone, density of shopping POI, density of services POI, cause of accident, travel mode, collision type, season, road type, age of driver, and physical isolation. Travel mode, season, and road speed limit are more important risk factors for traffic accidents, with motor vehicle drivers as the at-fault parties. The density of service POI and cause of the accident are less critical for traffic accidents with motor vehicle drivers than traffic accidents with vulnerable road users who are at-fault. Subsequently, the Apriori algorithm based on association rules is used to analyze the important causal factors of traffic accidents, so as to explore the influence mechanism of multiple causal factors and their implied strong association rules. Our results show that most combined factors are associated with the matched Service and Shopping POI features. This study provides valuable information on the perceived risk of fatal accidents and highlights the built environment’s significant influence on fatal traffic accidents. Management strategies targeting the most typical combinations of accident risk factors are proposed for preventing fatalities and injuries in serious traffic accidents.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic and Road Safety · Urban Transport and Accessibility · Traffic Prediction and Management Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Beijing Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Institute of Transportation Engineering and Geomatics, Tsinghua University, Beijing 100084, China
Funding
Beijing Natural Science Foundation · Beijing University of Civil Engineering and Architecture · Beijing Municipal Education Commission · Ministry of Education of China · National Natural Science Foundation of China · Beijing Social Science Foundation · National Social Science Foundation of China · BUCEA Post Graduate Innovation Project
References (33)
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