Analysis of Factors Influencing the Severity of Vehicle-to-Vehicle Accidents Considering the Built Environment: An Interpretable Machine Learning Model

Abstract

Understanding the causes of traffic road accidents is crucial; however, as data collection is conducted by traffic police, accident-related environmental information is not available. To fill this gap, we collect information on the built environment within R = 500 m of the accident site; model the factors influencing accident severity in Shenyang, China, from 2018 to 2020 using the Random Forest algorithm; and use the SHapley Additive exPlanation method to interpret the underlying driving forces. We initially integrate five indicators of the built environment with 18 characteristics, including human and vehicle at-fault characters, infrastructure, time, climate, and land use attributes. Our results show that road type, urban/rural, season, and speed limit in the first 10 factors have a significant positive effect on accident severity; density of commercial-POI in the first 10 factors has a significant negative effect. Factors such as urban/rural and road type, commercial and vehicle type, road type, and season have significant effects on accident severity through an interactive mechanism. These findings provide important information for improving road safety.

Accident Severity Analysis of Traffic Accident Hot Spot Areas in Changsha City Considering Built Environment

“…The experimental data were divided into test and training sets. In order to ensure the comprehensive use of the data for model training, while preserving sufficient data to evaluate the model's ability to generalise to unseen data, this paper splits the test set/training set ratio into 70% and 30%, based on lessons learned from previous studies [15]. In this paper, the evaluation metrics commonly used in machine learning were used, including accuracy, precision, recall, f1-score, and area under the curve (AUC).…”

“…Lee investigated Seoul's built environment features in South Korea that impact the likelihood of pedestrian accidents among older people, indicating that the influence of the built environment differs based on pedestrian age and regional characteristics [14]. Wang examined the influence of the built environment on Vehicle-to-Vehicle Accidents and discovered that factors such as commercial, urban/rural, and road types notably heightened the probability of fatal accidents [15]. Yang delved into the connection between the built environment and the spatial distribution of truck-related collisions utilizing the XGBoost and SHAP methodologies, unveiling a significant correlation between demographics, land utilization, and roadway networks with truck accidents across all injury categories [16].…”

“…In summary, scholars' investigations and analyses of accident hotspots primarily centre on enhancing identification accuracy and scrutinizing the influences of personnel, roadways, and environmental factors on accidents. In parallel, research of the impact of the built environment on accidents predominantly concentrates on the census tract [14,16] or the surrounding areas of accident occurrences [15,17,18]. Few studies have seized upon the inherent existence of traffic accident hotspots within urban settings as a starting point to delve deeply into the influence of varied built environment factors on accident severity across diverse accident zones.…”