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Assessing safety in horizontal curves using surrogate safety measures and machine learning
Navid Nadimi1, Hojjat Fathipour2, Abbas Sheykhfard3
1Faculty of Engineering, Shahid Bahonar University, Pajoohesh Sq., Kerman, Iran. navidnadimi@uk.ac.ir.
Scientific Reports
|April 11, 2025
Summary
Road safety on horizontal curves (HCs) was analyzed using surrogate safety measures (SSM) and a decision tree algorithm. Curve radius and pre-curve speed significantly impact HC safety, with driver experience also playing a key role.
Area of Science:
- Road safety engineering
- Traffic engineering
- Transportation safety
Background:
- Horizontal curves (HCs) are critical road elements associated with a high risk of accidents.
- Analyzing rare crash events directly is challenging, necessitating the use of surrogate safety measures (SSM).
- Previous research has not comprehensively utilized machine learning and SSMs to assess HC safety.
Purpose of the Study:
- To assess the impact of various factors on the safety of horizontal curves.
- To identify critical variables influencing HC safety using a decision tree algorithm and SSMs.
- To establish critical thresholds for key safety-influencing variables on HCs.
Main Methods:
- Application of a decision tree algorithm to analyze HC safety data.
- Utilization of surrogate safety measures (SSM) as a proxy for crash events.
- Inclusion of diverse variables: driver characteristics, HC specifications, and environmental factors.
Main Results:
- The radius of the curve and the speed on the preceding tangent section were identified as the most significant factors affecting HC safety.
- Critical thresholds were determined: curve radius of 358 m and pre-curve speed of 23 m/s.
- Driver experience, crash history, and education level were found to be critical driver characteristics, while environmental conditions showed no significant impact.
Conclusions:
- Road geometry (curve radius) and approach conditions (speed) are paramount for horizontal curve safety.
- Driver-specific factors significantly influence the ability to navigate HCs safely.
- The developed methodology provides a robust framework for assessing and improving HC safety, particularly in data-scarce environments.
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