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Segment length optimization for crash frequency modelling: Evaluating power spectral segment length in safety
Parveen Kumar1, Geetam Tiwari1, Sourabh Bikas Paul2
1TRIP Centre, IIT Delhi, New Delhi 110016, India.
Accident; Analysis and Prevention
|May 28, 2025
Summary
This study introduces a data-driven method for selecting road segment lengths using Power Spectral Segment Length (PSSL) analysis. PSSL improves crash prediction accuracy and identifies key factors contributing to fatal crashes on rural highways.
Area of Science:
- Road Safety Engineering
- Traffic Analysis
- Statistical Modeling
Background:
- Accurate road segment length selection is crucial for effective road safety analysis and crash prediction.
- Traditional methods lack standardized metrics and rely on subjective judgment.
- Existing approaches struggle with accuracy in identifying hazardous locations and evaluating safety performance.
Purpose of the Study:
- To introduce and evaluate the Power Spectral Segment Length (PSSL) method for optimizing road segment length selection.
- To enhance the accuracy of fatal crash prediction models using a data-driven approach.
- To compare the performance of PSSL-based segmentation against traditional methods in road safety analysis.
Main Methods:
- Spatial Frequency Domain Analysis (SFDA) was employed to determine Power Spectral Segment Length (PSSL).
- Power Spectral Percentage (PSP) was utilized as a key metric for evaluating segmentation performance.
- Random Parameters Negative Binomial (RPNB) models were developed to analyze crash data on rural two-lane highways.
Main Results:
- PSSL-based segmentation demonstrated superior performance compared to traditional methods, confirmed by CURE plots and Goodness-of-Fit statistics.
- Roadside service areas, population density, minor access points, and traffic heterogeneity were identified as significant predictors of fatal crashes.
- The study established an optimized, data-driven framework for segment length selection in crash modeling.
Conclusions:
- The PSSL method offers an accurate, reliable, and scalable approach to road segment length selection.
- This framework improves the precision of crash modeling and road safety assessments.
- Understanding key crash predictors enhances targeted safety interventions on rural highways.
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