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

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  • Chen Wang

    (Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, China
    Intelligent Transportation Research Center, Southeast University, Nanjing 210096, China)

  • Lin Liu

    (Jiangsu Intelligent Transportation Systems Co., Ltd., Nanjing 210096, China)

  • Chengcheng Xu

    (Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, China)

  • Weitao Lv

    (Jiangsu Intelligent Transportation Systems Co., Ltd., Nanjing 210096, China)

Abstract

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.

Suggested Citation

  • Chen Wang & Lin Liu & Chengcheng Xu & Weitao Lv, 2019. "Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework," IJERPH, MDPI, vol. 16(3), pages 1-18, January.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:3:p:334-:d:200807
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    References listed on IDEAS

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    1. Liu, Zhiyuan & Chen, Xinyuan & Meng, Qiang & Kim, Inhi, 2018. "Remote park-and-ride network equilibrium model and its applications," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 37-62.
    2. Feng Chen & Suren Chen & Xiaoxiang Ma, 2016. "Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models," IJERPH, MDPI, vol. 13(6), pages 1-16, June.
    3. Wang, Shuaian & Chen, Weijie & Zheng, Yuan, 2016. "Willingness to board: A novel concept for modeling queuing up passengersAuthor-Name: Liu, Zhiyuan," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 70-82.
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    Cited by:

    1. Shuaiming Chen & Haipeng Shao & Ximing Ji, 2021. "Insights into Factors Affecting Traffic Accident Severity of Novice and Experienced Drivers: A Machine Learning Approach," IJERPH, MDPI, vol. 18(23), pages 1-20, December.
    2. Muhammad Zahid & Yangzhou Chen & Sikandar Khan & Arshad Jamal & Muhammad Ijaz & Tufail Ahmed, 2020. "Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter?," IJERPH, MDPI, vol. 17(11), pages 1-21, June.

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