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Bivariate extreme value modeling for road safety estimation.

Lai Zheng1, Karim Ismail2, Tarek Sayed3

  • 1School of Transportation Science and Engineering, Harbin Institute of Technology, China; Department of Civil Engineering, University of British Columbia, Canada.

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Summary
This summary is machine-generated.

This study integrates surrogate safety measures like post encroachment time (PET) and length proportion of merging (LPM) into a bivariate extreme value model. This approach significantly reduces crash estimate uncertainty compared to univariate methods.

Keywords:
Bivariate extreme value modelFreeway entranceSafety estimationSurrogate safety measureThreshold excess

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Area of Science:

  • Transportation Engineering
  • Traffic Safety Analysis
  • Statistical Modeling

Background:

  • Traditional road safety assessment relies heavily on crash data, which can be sparse and infrequent.
  • Surrogate safety measures offer a complementary approach to evaluate safety from a broader perspective.
  • Freeway entrance merging areas are critical zones with unique safety challenges.

Purpose of the Study:

  • To develop a unified framework for road safety estimation using surrogate safety measures within a bivariate extreme value theory context.
  • To characterize the severity of merging events using post encroachment time (PET) and length proportion of merging (LPM).
  • To compare the performance of bivariate models against univariate models for crash estimation.

Main Methods:

  • Application of a bivariate threshold excess model incorporating PET and LPM.
  • Utilizing extreme value theory for safety estimation.
  • Comparison of seven distribution functions for the bivariate model, selecting the logistic distribution.
  • Evaluation against univariate Generalized Pareto distribution models.

Main Results:

  • Bivariate models produced crash estimates closer to observed crashes than univariate models.
  • Incorporating two surrogate safety measures significantly reduced the uncertainty in crash estimates.
  • The logistic distribution function was identified as the best fit for the bivariate model.

Conclusions:

  • The proposed bivariate framework enhances road safety estimation by reducing uncertainty.
  • This study advances the development of multivariate safety hierarchy models.
  • Further data collection over extended periods may improve bivariate model efficiency.