Crash severity prediction and interpretation for road determinants based on a hybrid method

Abstract

In this study, 4 classification algorithms were employed to characterize the influence of road determinants on roadway crash severity with actual crash data. The crash data were obtained from crash records in Texas, USA, from January 2020 to April 2021. The prediction model of crash severity utilized 12 road-related features—including shoulder types, shoulder width, and curb types—as well as 10 other features—such as weather and illumination conditions—as input features. Three crash severity levels—“Minor Damage,” “Moderate Damage,” and “Severe Damage”—were used as output features. Decision tree, support vector machines, and multi-layer perceptron were employed to compare their prediction performance with the XGBoost model. The results show that the XGBoost model yields the best performance among the 4 algorithms. The overall accuracy, average precision, average recall, and average F1 score of the XGBoost model were 82.65%, 0.83, 0.82, and 0.82, respectively. Besides, SHapley Additive exPlanations (SHAP) and partial dependence plots were used to interpret the model results. Among the road-related features, the most influential one is the median width. Greater crash severity is related to paved right shoulder and curb. These findings are helpful for the design and planning of road safety.

Keywords:

• traffic safety
• road determinants
• crash severity
• XGBoost
• SHAP

Acknowledgments

The authors acknowledge that the crash data used in this paper were obtained from the Crash Records Information System (CRIS) of the Texas Department of Transportation (TxDOT), USA. The authors would like to express their gratitude to Hanzhen Wang for his valuable assistance in the data acquisition process. The findings, opinions, conclusions, and recommendations presented in this study are those of the authors and do not necessarily reflect the views of the agency that hosts the data.

Author contributions

Chuangang Cheng: methodology, software, validation, formal analysis, data curation, writing original draft, visualization, analysis, and interpretation of results. Shuyan Chen: conceptualization, methodology, validation, funding acquisition, resources, supervision, writing–review and editing. Yongfeng Ma: study conception and design, supervision, methodology, project administration, writing–review and editing. Fengxiang Qiao: investigation, writing–review and editing. Zhuopeng Xie: conceptualization, methodology, software.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could influence the work reported in this paper.

Additional information

Funding

This work is supported by the National Natural Science Foundation of China (No. 52172342), the Transport Science & Technology Project of Jiangsu Province (No. 2022G02), and the Key R &D Program of Ningxia Hui Autonomous Region (No. 2022BEG02017).