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Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework

Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, China
Intelligent Transportation Research Center, Southeast University, Nanjing 210096, China
Jiangsu Intelligent Transportation Systems Co., Ltd., Nanjing 210096, China
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(3), 334;
Received: 3 December 2018 / Revised: 20 January 2019 / Accepted: 20 January 2019 / Published: 25 January 2019
PDF [3900 KB, uploaded 15 February 2019]


The objective of this paper is to predict the future driving risk of crash-involved drivers in Kunshan, China. A systematic machine learning framework is proposed to deal with three critical technical issues: 1. defining driving risk; 2. developing risky driving factors; 3. developing a reliable and explicable machine learning model. High-risk (HR) and low-risk (LR) drivers were defined by five different scenarios. A number of features were extracted from seven-year crash/violation records. Drivers’ two-year prior crash/violation information was used to predict their driving risk in the subsequent two years. Using a one-year rolling time window, prediction models were developed for four consecutive time periods: 2013–2014, 2014–2015, 2015–2016, and 2016–2017. Four tree-based ensemble learning techniques were attempted, including random forest (RF), Adaboost with decision tree, gradient boosting decision tree (GBDT), and extreme gradient boosting decision tree (XGboost). A temporal transferability test and a follow-up study were applied to validate the trained models. The best scenario defining driving risk was multi-dimensional, encompassing crash recurrence, severity, and fault commitment. GBDT appeared to be the best model choice across all time periods, with an acceptable average precision (AP) of 0.68 on the most recent datasets (i.e., 2016–2017). Seven of nine top features were related to risky driving behaviors, which presented non-linear relationships with driving risk. Model transferability held within relatively short time intervals (1–2 years). Appropriate risk definition, complicated violation/crash features, and advanced machine learning techniques need to be considered for risk prediction task. The proposed machine learning approach is promising, so that safety interventions can be launched more effectively. View Full-Text
Keywords: driving risk; traffic violation behavior; machine learning; temporal transferability driving risk; traffic violation behavior; machine learning; temporal transferability

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Wang, C.; Liu, L.; Xu, C.; Lv, W. Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework. Int. J. Environ. Res. Public Health 2019, 16, 334.

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