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A new spatial count data model with Bayesian additive regression trees for accident hot spot identification.

Rico Krueger1, Prateek Bansal2, Prasad Buddhavarapu3

  • 1Transport and Mobility Laboratory, Ecole Polytechnique Fédérale de Lausanne, Switzerland.

Accident; Analysis and Prevention
|June 21, 2020
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Summary

This study introduces a novel spatial negative binomial model using Bayesian additive regression trees to improve accident hot spot identification. The new model enhances predictive power and ranking ability for road safety management.

Keywords:
Accident analysisBayesian additive regression treesNegative binomial modelPólya-Gamma data augmentationSite rankingSpatial count data modelling

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Area of Science:

  • Road safety
  • Statistical modeling
  • Machine learning

Background:

  • Accurate identification of accident hot spots is crucial for road safety management.
  • Bayesian count data models are standard but limited by simple link functions and complexity.
  • Existing machine learning methods struggle with estimation uncertainty and spatial correlations.

Purpose of the Study:

  • To propose a new spatial negative binomial model for improved accident hot spot identification.
  • To overcome limitations of traditional models and machine learning approaches in road safety analysis.
  • To enhance the predictive power and site ranking ability in road networks.

Main Methods:

  • Development of a novel spatial negative binomial model.
  • Integration of Bayesian additive regression trees for endogenous link function selection.
  • Application of Pólya-Gamma data augmentation for feasible posterior inference.
  • Testing on a metropolitan highway network crash count data set.

Main Results:

  • The proposed model demonstrates competitive performance compared to baseline spatial count models.
  • Achieved comparable goodness of fit and site ranking ability.
  • Successfully addressed limitations in predictive power and model specification.

Conclusions:

  • The new spatial negative binomial model offers a powerful tool for road safety analysis.
  • Bayesian additive regression trees effectively automate link function selection.
  • The model provides a robust approach for identifying hazardous road sites.