Citation

Ma C, Peng Y, Wu L, Guo X, Wang X, Kong X. Transp. Res. Rec. 2022; 2676(2): 692-705.

Copyright

(Copyright © 2022, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981211045371

PMID

unavailable

Abstract

Distraction occurs when a driver's attention is diverted from driving to a secondary task. The number of distraction-affected crashes has been increasing in recent years. Accurately predicting distraction-affected crashes is critical for roadway agencies to reduce distracted driving behaviors and distraction-affected crashes. Recently, more and more emerging phone-use data and machine learning techniques are available to safety researchers, and can potentially improve the prediction of distraction-affected crashes. Therefore, this study first examines if phone-use events provide essential information for distraction-affected crashes. The authors apply the machine learning technique (i.e., XGBoost) under two scenarios, with and without phone-use events, and compare their performances with two conventional statistical models: logistic regression model and mixed-effects logistic regression model. The comparison demonstrates the superiority of XGBoost over logistic regression with a high-dimensional unbalanced dataset. Further, this study implements SHAP (SHapley Additive exPlanation) to interpret the results and analyze the importance of individual features related to distraction-affected crashes and tests its ability to improve prediction accuracy. The trained XGBoost model achieves a sensitivity of 91.59%, a specificity of 85.92%, and 88.72% accuracy. The XGBoost and SHAP results suggest that: (1) phone-use information is an important factor associated with the occurrences of distraction-affected crashes; (2) distraction-affected crashes are more likely to occur on roadway segments with higher exposure (i.e., length and traffic volume), unevenness of traffic flow condition, or with medium truck volume.

Language: en

Keywords

distraction-affected crashes; machine learning; phone use while driving; SHAP; XGBoost