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Comparative analysis of multiple techniques for developing and transferring safety performance functions.

Ahmed Farid1, Mohamed Abdel-Aty1, Jaeyoung Lee1

  • 1Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, 32816-2450, United States.

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Summary

This study developed and transferred safety performance functions (SPFs) for rural divided multilane highways across seven states. Results show that Tobit, Random Forest (RF), and hybrid models offer superior transferability compared to traditional negative binomial (NB) regression.

Keywords:
Data mining methodsHighway safety manualNegative binomial modelSafety performance functionsTobit modelTransferability

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Area of Science:

  • Transportation Engineering
  • Traffic Safety Analysis
  • Statistical Modeling

Background:

  • Safety Performance Functions (SPFs) are crucial for road safety analysis, including identifying high-risk locations and evaluating safety countermeasures.
  • Traditional SPFs, like those in the Highway Safety Manual (HSM), primarily use negative binomial (NB) regression, limiting the scope of safety modeling.
  • Developing and transferring SPFs across different geographical locations is less common than local calibration.

Purpose of the Study:

  • To develop and evaluate the transferability of various SPFs for rural divided multilane highway segments across seven U.S. states.
  • To compare the predictive performance of traditional NB models with alternative techniques, including machine learning and hybrid approaches, for SPF transferability.
  • To identify the most transferable SPF model types for enhancing road safety assessments in different jurisdictions.

Main Methods:

  • Developed and transferred rural divided multilane highway segment SPFs for Florida, Ohio, Illinois, Minnesota, California, Washington, and North Carolina.
  • Modeled crash counts using Negative Binomial (NB), Zero-Inflated NB, Poisson Lognormal (PLN), regression tree, Random Forest (RF), boosting, and Tobit models.
  • Proposed and developed a hybrid model combining Tobit and NB predictions, and evaluated the transferability of all developed SPFs.

Main Results:

  • No single model type demonstrated universal superiority across all transferability scenarios.
  • Tobit, RF, regression tree, NB, and the proposed hybrid models exhibited better predictive performance in a significant proportion of transferred SPFs.
  • The transferability of SPFs varies, indicating the need for careful model selection based on specific application contexts.

Conclusions:

  • Alternative modeling techniques beyond traditional NB regression show promise for developing transferable SPFs.
  • The Tobit, RF, and hybrid models are strong candidates for improving cross-jurisdictional road safety analysis.
  • Further research into SPF transferability can lead to more robust and widely applicable road safety tools.