Applying an interpretable machine learning framework to the traffic safety order analysis of expressway exits based on aggregate driving behavior data

In light of the increasing amount of traffic disorder at road traffic hubs, to improve traffic safety, it is essential to detect road risks in advance and analyze the causes after its occurrence. In this study, interpretable machine learning (ML) is employed to analyze the traffic order by using a set of multi-source data comprising traffic conditions, traffic control devices, road conditions, and external conditions. Data were collected from the exits of some Beijing expressways via navigation and field investigations. The traffic order index (TOI) based on aggregate driving behavior data is used as a new surrogate index to evaluate the safety risk. A traffic order prediction model is then constructed by adapting the eXtreme Gradient Boosting (XGBoost) ML method. In addition, SHapley Additive exPlanation (SHAP) is employed to interpret the results and explore the relationships between the influencing factors and the traffic order. The results indicate that XGBoost could predict the traffic order levels well, and achieved an accuracy, precision, recall, and F-1-score of 92.62%, 92.67%, 92.62%, and 92.63%, respectively. The congestion index was found to have a great influence on traffic order. Furthermore, the number of lanes, advance guide signs, and weather conditions can have different effects on the traffic order under different traffic conditions. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

In order to improve traffic safety, this study uses interpretable machine learning to analyze traffic order based on multi-source data. The results show that the XGBoost method can accurately predict traffic order levels and the congestion index has a significant impact on traffic order. Additionally, the number of lanes, advance guide signs, and weather conditions also have different effects on traffic order under different traffic conditions.