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Improve Aggressive Driver Recognition Using Collision Surrogate Measurement and Imbalanced Class Boosting

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  • Ke Wang

    (College of Transportation Engineering, Tongji University, Shanghai 201804, China
    Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Tongji University, Shanghai 201804, China
    Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University, Shanghai 201804, China)

  • Qingwen Xue

    (College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Yingying Xing

    (College of Transportation Engineering, Tongji University, Shanghai 201804, China
    Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Tongji University, Shanghai 201804, China)

  • Chongyi Li

    (College of Transportation Engineering, Tongji University, Shanghai 201804, China)

Abstract

Real-time recognition of risky driving behavior and aggressive drivers is a promising research domain, thanks to powerful machine learning algorithms and the big data provided by in-vehicle and roadside sensors. However, since the occurrence of aggressive drivers in real traffic is infrequent, most machine learning algorithms treat each sample equally and prone to better predict normal drivers rather than aggressive drivers, which is our real interest. This paper aims to test the advantage of imbalanced class boosting algorithms in aggressive driver recognition using vehicle trajectory data. First, a surrogate measurement of collision risk, called Average Crash Risk (ACR), is proposed to calculate a vehicle’s crash risk. Second, the driver’s driving aggressiveness is determined by his/her ACR with three anomaly detection methods. Third, we train classification models to identify aggressive drivers using partial trajectory data. Three imbalanced class boosting algorithms, SMOTEBoost, RUSBoost, and CUSBoost, are compared with cost-sensitive AdaBoost and cost-sensitive XGBoost. Additionally, we try two resampling techniques with AdaBoost and XGBoost. Among all algorithms tested, CUSBoost achieves the highest or the second-highest Area Under Precision-Recall Curve (AUPRC) in most datasets. We find the discrete Fourier coefficients of gap as the key feature to identify aggressive drivers.

Suggested Citation

  • Ke Wang & Qingwen Xue & Yingying Xing & Chongyi Li, 2020. "Improve Aggressive Driver Recognition Using Collision Surrogate Measurement and Imbalanced Class Boosting," IJERPH, MDPI, vol. 17(7), pages 1-17, March.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:7:p:2375-:d:339460
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    References listed on IDEAS

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    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. Montanino, Marcello & Punzo, Vincenzo, 2015. "Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 82-106.
    3. Coifman, Benjamin & Li, Lizhe, 2017. "A critical evaluation of the Next Generation Simulation (NGSIM) vehicle trajectory dataset," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 362-377.
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    Cited by:

    1. Ke Wang & Qingwen Xue & Jian John Lu, 2021. "Risky Driver Recognition with Class Imbalance Data and Automated Machine Learning Framework," IJERPH, MDPI, vol. 18(14), pages 1-18, July.
    2. Md Jahidur Rahman & Hongtao Zhu, 2023. "Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(3), pages 3455-3486, September.
    3. Rahman, Md Jahidur & Zhu, Hongtao, 2024. "Detecting accounting fraud in family firms: Evidence from machine learning approaches," Advances in accounting, Elsevier, vol. 64(C).

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