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A bi-level framework for real-time crash risk forecasting using artificial intelligence-based video analytics.

Fizza Hussain1, Yasir Ali2, Yuefeng Li3

  • 1School of Civil and Environmental Engineering, Faculty of Engineering, Queensland University of Technology, Brisbane, 4001, Australia.

Scientific Reports
|February 20, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel bi-level framework for real-time crash risk forecasting at signalized intersections. The system accurately predicts potential rear-end collisions minutes in advance, enhancing traffic safety.

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Area of Science:

  • Traffic Engineering
  • Transportation Safety
  • Artificial Intelligence in Transportation

Background:

  • Signalized intersections are critical nodes in road networks, yet prone to crashes.
  • Real-time crash risk forecasting (RTCF) is essential for proactive traffic safety management.
  • Existing methods often lack the temporal resolution and accuracy needed for dynamic traffic conditions.

Purpose of the Study:

  • To develop and validate a bi-level framework for real-time crash risk forecasting at signalized intersections.
  • To leverage temporal dependencies in crash risks for improved prediction accuracy.
  • To provide a tool for proactive safety management in dynamic traffic environments.

Main Methods:

  • A non-stationary generalised extreme value (GEV) model was developed for real-time rear-end crash risk estimation at the signal cycle level.
  • Artificial intelligence (AI) techniques, including YOLO and DeepSort, were employed to extract traffic conflicts and covariates from video data.
  • A recurrent neural network (RNN) was utilized at the second level to predict future crash risks based on estimated signal cycle risks.

Main Results:

  • The non-stationary GEV model demonstrated a close match between estimated and historical crash frequencies.
  • Estimated mean crashes fell within the confidence intervals of observed crashes, validating the GEV model's accuracy.
  • The framework successfully predicted crash risk for subsequent signal cycles up to 20-25 minutes ahead.

Conclusions:

  • The proposed bi-level RTCF framework offers a robust method for real-time crash risk assessment at signalized intersections.
  • The integration of AI and extreme value theory provides accurate and temporally relevant safety insights.
  • This framework opens new avenues for proactive safety interventions and traffic management strategies.