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Opposing-through crash risk forecasting using artificial intelligence-based video analytics for real-time
Md Mohasin Howlader1, Md Mazharul Haque1
1Queensland University of Technology (QUT), School of Civil and Environmental Engineering, Faculty of Engineering, Brisbane, QLD 4000, Australia.
Accident; Analysis and Prevention
|May 8, 2025
Summary
This study introduces a new AI framework for real-time crash risk forecasting at intersections. It accurately predicts crash risks using traffic conflicts and advanced models, enhancing road safety.
Area of Science:
- Traffic Engineering and Safety
- Artificial Intelligence in Transportation
- Computer Vision for Road Safety
Background:
- Traditional crash forecasting using historical data lacks real-time granularity.
- Traffic conflict techniques (TCTs) combined with AI offer granular crash risk estimation.
- Real-time applications require advanced methods beyond historical crash data analysis.
Purpose of the Study:
- To develop a unified framework for predicting opposing-through crash risks at signalized intersections.
- To integrate Generalized Extreme Value (GEV) theory with parametric and non-parametric forecasting models.
- To enable real-time, proactive safety management in intelligent transport systems.
Main Methods:
- Utilized a deep neural network-based computer vision technique to extract Post Encroachment Time (PET) traffic conflicts from video footage.
- Employed a non-stationary GEV model incorporating PET counts, speed variations, and signal timing for crash risk estimation.
- Forecasted crash risks using Autoregressive Integrated Moving Average (ARIMA), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) models.
Main Results:
- The developed EVT model adequately estimated opposing-through crashes, with mean crash frequency estimates within 95% confidence limits of observed crashes.
- ARIMA and recurrent neural network models demonstrated similar forecasting accuracy for crash risk.
- Reliable crash risk predictions were achieved up to 11 future signal cycles.
Conclusions:
- The integrated framework effectively estimates and forecasts real-time crash risks at signalized intersections.
- The proposed system serves as a crucial component for proactive safety management in intelligent transport systems.
- AI and traffic sensing technologies significantly advance the potential for real-time road safety applications.
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