Identifying the Roadway Infrastructure Factors Affecting Road Accidents Using Interpretable Machine Learning and Data Augmentation

Abstract

In modern society, vehicle accidents have been a factor that has adversely affected national development for a long time. Many countries have tried to solve this issue, and various solutions have been studied. This study aims to design a process for analyzing vehicle accidents to support safety interventions. In the data preprocessing section, a resampling technique was used to solve the data imbalance problem. Then, we applied five different machine learning models for classification by applying hyperparameter optimization. After classification, model-agnostic interpretation techniques were used to interpret the results of a series of machine learning models. Through the above series of processes, we were able to design a process that analyzes vehicle accident data and derives the factors that affect the accident. The classification model that uses XGBoost with ENN (Edited Nearest Neighbor) shows almost 84.3% accuracy. As a result, for “Length” and “Volume”, we found that certain points (Length: 200 m, 29,233 veh/day) were more likely to have an accident. Moreover, variables, such as volume or the volume of heavy vehicle, the probability of an accident increases as the value increases, but in the case of “Lane width” and “Shoulder width”, it can be confirmed that the probability of occurrence decreases as the value increases. These interpretations have meaningful information that could suggest policy recommendations for reducing traffic accidents and can be helpful in establishing effective traffic accident countermeasures.