An ensemble machine learning method for crash responsibility assignment in quasi-induced exposure theory

Abstract

Quasi-induced exposure theory requires the clear-cut assignment of crash responsibility for individual crash-involved drivers. The assignment method based on the citation by police officers poses a concern that the citation would be issued due to the nonmoving violations rather than the driving actions that directly contribute to the crash. Thus, the objective of the study is to improve the accuracy of citation-based responsibility assignments. Binary logistic regression is employed to identify the factors affecting the citation decision of the police officers. An ensemble machine learning method that combines random forest, neural network, and extreme gradient boosting classifiers is established to allocate the crash responsibility. The findings include that (1) the police citation is closely related to the presence of hazardous driving behavior, but it can also be influenced by several factors such as driver age, drinking status, and the collision impact point of the vehicle; and (2) compared to the conventional models, the ensemble machine learning methods have better performance for crash responsibility assignment in terms of accuracy, Kappa coefficient, and area under the curve. The study serves to provide a reliable crash responsibility assignment approach to improve the accuracy of exposure estimation.

Keywords:

• crash responsibility
• quasi-induced exposure
• police citation
• ensemble machine learning
• classification performance

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by Natural Science Foundation of Zhejiang Province (LQ21G010002), Shanxi Scholarship Council of China (2021-136), and Doctoral Research Foundation of Taiyuan University of Science and Technology (20202048).