Assessing crash severity of urban roads with data mining techniques using big data from in-vehicle dashcam

Abstract

The factors that affect the severity of crashes must be identified for pedestrian and traffic safety in urban roads. Specifically, in the case of urban road crashes, these crashes occur due to the complex interaction of various factors. Therefore, it is necessary to collect high-quality data that can derive these various factors. Accordingly, this study collected crash data, which included detailed crash factor data on the huge urban and mid-level roads. Using this, various crash factors including driver, vehicle, road, environment, and crash characteristics are constructed to develop a crash severity prediction model. Through this, this study identified more detailed factors affecting the severity of urban road crashes. The crash severity model was developed using both machine learning and statistical models because the insights that can be obtained from the latest technology and traditional methods are different. Therefore, the binary logit model, a support vector machine, and extreme gradient boosting were developed using key variables derived from the multiple correspondence analysis and Boruta-SHapley Additive exPlanations. The main result of this study shows that the crash severity decreased at four-street intersections and when traffic segregation facilities were installed. The findings of this study can be used to establish a traffic safety management strategy to reduce the severity of crashes on urban roads.

Keywords

• crash severity model
• in-vehicle dashcam video data
• crash data
• traffic safety
• machine learning
• urban road traffic management