Application of Machine Learning Techniques to Predict the Occurrence of Distraction-affected Crashes with Phone-Use Data Academic Article uri icon


  • Distraction occurs when a drivers 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.

published proceedings


author list (cited authors)

  • Ma, C., Peng, Y., Wu, L., Guo, X., Wang, X., & Kong, X.

citation count

  • 4

complete list of authors

  • Ma, Chaolun||Peng, Yongxin||Wu, Lingtao||Guo, Xiaoyu||Wang, Xiubin||Kong, Xiaoqiang

publication date

  • February 2022