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An advanced real-time crash prediction framework for combined hard shoulder running and variable speed limits system
Mohamed Abdel-Aty1, Tarek Hasan2, B M Tazbiul Hassan Anik1
1Department of Civil, Environmental, and Construction Engineering, University of Central Florida (UCF), 12800 Pegasus Drive, Suite 211, P.O. Box 162450, Orlando, FL, 32816-2450, USA.
Scientific Reports
|November 3, 2024
Summary
This study introduces a real-time crash prediction framework for freeways using advanced Transformer models. It improves proactive traffic management by accurately forecasting crash risks during peak hours.
Area of Science:
- Traffic Engineering
- Transportation Safety
- Artificial Intelligence in Transportation
Background:
- Proactive traffic management requires real-time crash risk evaluation.
- Variable Speed Limits (VSL) and Hard Shoulder Running (HSR) are key freeway management strategies.
- Accurate prediction of crash likelihood is crucial for dynamic decision-making.
Purpose of the Study:
- To develop an integrated real-time crash risk prediction framework for VSL and HSR freeways.
- To identify significant crash contributing factors using statistical methods.
- To enhance freeway safety through improved crash prediction models.
Main Methods:
- Utilized statistical methods to identify crash contributing factors (traffic, roadway, weather).
- Proposed Time-Embedded Transformer models for real-time crash event likelihood prediction.
- Incorporated freeway segment safety conditions into prediction models.
Main Results:
- Achieved sensitivity of 0.76 (AM peak) and 0.78 (PM peak) with low false alarm rates.
- Demonstrated significant performance improvements (7.04% AM, 8.33% PM sensitivity increase) by including safety conditions.
- Identified key relationships between contributing factors and crash occurrences.
Conclusions:
- The proposed framework offers accurate real-time crash likelihood estimation for freeways.
- Incorporating general safety conditions substantially enhances prediction model performance.
- This method aids practitioners in dynamic traffic management and safety enhancement.
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