Deep learning model for accident risk prediction on expressways considering wind effects

Abstract

In this study, an AI model integrating vehicle trajectory images from ETC2.0 probe data and weather data was constructed to improve the prediction of traffic accident risk on expressways, and the effect of wind speed and direction on accident risk was quantitatively evaluated. The model was applied to a section of the Tomei Expressway and analyzed accident risk by wind component (headwind, tailwind, and crosswind) using traffic accident, traffic flow, and weather data for the years 2020 to 2021. The results suggested that crosswinds are associated with facility contact and rollover accidents, while headwinds and tailwinds are associated with specific accident types such as rear-end collisions. By adding weather variables to the deep learning model, the prediction accuracy was improved to a maximum of 0.241 compared to the baseline model (F value of 0.195). The results indicate that taking into account the effect of wind can provide practical and explanatory accident risk prediction.