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A Crash Prediction Method Based on Artificial Intelligence Techniques and Driving Behavior Event Data

Author

Listed:
  • Yunjong Kim

    (Department of Transportation and Logistics Engineering, Hanyang University, Ansan 15588, Korea)

  • Juneyoung Park

    (Department of Transportation and Logistics Engineering, Hanyang University, Ansan 15588, Korea
    Department of Smart City Engineering, Hanyang University, Ansan 15588, Korea)

  • Cheol Oh

    (Department of Transportation and Logistics Engineering, Hanyang University, Ansan 15588, Korea
    Department of Smart City Engineering, Hanyang University, Ansan 15588, Korea)

Abstract

Various studies on how to prevent and deal with traffic accidents are ongoing. In the past, the key research emphasis was on passive accident response measures that analyzed roadway-based historical data to identify road sections with high crash risk. Through assessing crash risks by analyzing simulation data and actual vehicle driving trajectory data, this study suggests a method of effectively preventing accidents before they happen. In this analysis, using digital tachograph (DTG) data, which is the vehicle trajectory data for commercial vehicles running on Korean highways, hazardous and normal traffic flows were identified and extracted. Driving behavior event data for both types of traffic flow was processed by measuring safety indicators through the extracted data. Safety indicators with a high impact on traffic flow classification were then extracted using gradient boosting, a representative ensemble technique. A neural network analysis was performed using the extracted safety indicators as independent variables to create a traffic flow classifier, which had a high accuracy of 94.59%. The DTG data set was also classified based on the severity of each accident that occurred in the studied roadway, the time of the accident, and the weather; the results were compiled to enable comprehensive accident prediction. It is expected that proactive crash prevention will be possible in the future by evaluating real-time accident risks using the findings and ensemble-based methodologies of this paper.

Suggested Citation

  • Yunjong Kim & Juneyoung Park & Cheol Oh, 2021. "A Crash Prediction Method Based on Artificial Intelligence Techniques and Driving Behavior Event Data," Sustainability, MDPI, vol. 13(11), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:6102-:d:564490
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    References listed on IDEAS

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    1. Xiaoxia Xiong & Long Chen & Jun Liang, 2018. "Vehicle Driving Risk Prediction Based on Markov Chain Model," Discrete Dynamics in Nature and Society, Hindawi, vol. 2018, pages 1-12, January.
    2. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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    Cited by:

    1. Xianbin Wang & Yuqi Zhao & Weifeng Li, 2023. "Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model," Sustainability, MDPI, vol. 15(3), pages 1-21, February.

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