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Multimodal crash frequency modeling: Multivariate space-time models with alternate spatiotemporal interactions.

Wen Cheng1, Gurdiljot Singh Gill1, John L Ensch2

  • 1Department of Civil Engineering, California State Polytechnic University, Pomona 3801 W. Temple Ave., Pomona, CA 91768, United States.

Accident; Analysis and Prevention
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PubMed
Summary

This study developed advanced space-time models to improve transportation safety analysis for all users. The best-performing model enhances crash prediction accuracy and site ranking in multimodal systems.

Keywords:
Mode-varying coefficientsMultimodal approachMultivariate space-time modelsSite rankingTime-varying spatial random effects

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Area of Science:

  • Transportation safety
  • Statistical modeling
  • Multimodal systems analysis

Background:

  • Safety is crucial for multimodal transportation systems, yet crash frequency models for these systems are underutilized.
  • Existing models often focus on motorized transport, leaving a gap in understanding multimodal crash dynamics.

Purpose of the Study:

  • To develop and evaluate three multivariate space-time models for multimodal transportation safety.
  • To address the scarcity of crash frequency models in multimodal transportation research.
  • To identify the most accurate model for predicting crash risks across different transportation modes.

Main Methods:

  • Development and comparison of three multivariate space-time models with varying temporal trends and spatiotemporal interactions.
  • Estimation of mode-varying coefficients to account for differing impacts of explanatory variables across crash modes.
  • Evaluation of models using criteria for predictive accuracy (training/test errors) and site ranking performance.

Main Results:

  • All three models identified a similar set of influential covariates, indicating robustness.
  • The model with time-varying spatial random effects showed superior predictive accuracy for training and test errors.
  • This superior model also demonstrated the best site ranking performance, improving consistency.

Conclusions:

  • Multivariate space-time models with mode-varying coefficients are effective for multimodal transportation safety analysis.
  • The time-varying spatial random effects model offers enhanced predictive accuracy and reliable site ranking.
  • Further research can leverage these advanced models to improve safety across all transportation modes.