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Bivariate Bayesian hierarchical extreme value modeling using multi-type traffic conflict for crash estimation on
Zhenlin Hu1, Pengru Wei1, Lin Sheng1
1School of Transportation Science and Engineering, Harbin Institute of Technology, 73 Huanghe Street, Nangang District, Harbin 150090, PR China.
Accident; Analysis and Prevention
|March 30, 2025
Summary
A new bivariate Bayesian model accurately estimates freeway curve crashes by considering correlations between rear-end and side-impact events. This approach improves crash prediction compared to models that analyze risks separately.
Area of Science:
- Traffic Engineering
- Road Safety
- Statistical Modeling
Background:
- Freeway horizontal curves present significant driving safety challenges due to alignment and visibility issues.
- Vehicle interactions on curves can lead to correlated, multi-type crash risks, making separate risk modeling inaccurate.
Purpose of the Study:
- To develop and validate a bivariate Bayesian hierarchical extreme value model (BBHMS) for estimating correlated crash risks on freeway curves.
- To compare the performance of BBHMS against univariate models for rear-end and side crashes.
Main Methods:
- Developed a bivariate extreme value model integrating two conflict indicators and their correlation.
- Implemented a Bayesian hierarchical structure to combine traffic conflicts across sites, including covariates and heterogeneity.
- Applied univariate Bayesian hierarchical extreme value models (UBHMS) and BBHMS to Yinkun freeway data.
Main Results:
- The BBHMS demonstrated superior accuracy and precision in crash estimation, with smaller parameter standard deviations compared to UBHMS.
- Identified key covariates influencing crash risk: increased large vehicles and speed variation elevate risks, while car-following and lane-changing vehicles impact specific crash types.
- Overspeed and lane space occupancy were found to reduce rear-end crash risk.
Conclusions:
- Bivariate modeling is crucial for accurately assessing correlated crash risks on freeway curves.
- Specific traffic and geometric factors significantly influence crash likelihood, providing insights for safety improvements.
- The interaction between vertical and horizontal curves affects crash risks, with sag curves posing higher risks than crest curves.
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