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Updated: Sep 21, 2025

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Lane-Level Regional Risk Prediction of Mainline at Freeway Diverge Area.

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  • 1Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China.

International Journal of Environmental Research and Public Health
|May 28, 2022
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Summary

This study developed a lane-level real-time regional risk prediction model using machine learning to enhance traffic safety. The model accurately identifies high-risk traffic conditions, enabling proactive accident prevention strategies.

Keywords:
catastrophe theoryfeature analysisregional risk predictionroadside observation datasurrogate safety measure

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Area of Science:

  • Traffic Engineering
  • Transportation Safety
  • Machine Learning Applications

Background:

  • Real-time traffic accident prevention is critical.
  • Existing models often lack lane-level granularity.
  • Accurate regional risk prediction is needed for proactive safety measures.

Purpose of the Study:

  • To establish a lane-level real-time regional risk prediction model.
  • To identify key features influencing traffic regional risk.
  • To provide a basis for individualized traffic control strategies.

Main Methods:

  • Least Squares-Support Vector Machines (LS-SVM) for lane region identification.
  • Mutual Information (MI) and binary logit regression for feature selection.
  • Catastrophe theory for model construction and verification.
  • Extraction and aggregation of traffic parameters and surrogate safety measures (SSMs).

Main Results:

  • Lane difference significantly reduces regional risk uncertainty (Odds Ratio = 16.30).
  • Key risk factors include modified time to collision (MTTC) inverse, speed difference, and headway (DHW).
  • The model achieved 86.50% overall accuracy, predicting 84.78% of risks and 86.63% of normal traffic.

Conclusions:

  • The developed lane-level model effectively predicts regional traffic risk.
  • Lane-specific features are crucial for accurate risk assessment.
  • The model supports the development of targeted active traffic control strategies.