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A process convolution model for crash count data on a network.

Hassan Rezaee1, Alexandra M Schmidt2, Joshua Stipancic3

  • 1Department of Decision Sciences, HEC Montréal, Montréal, QC, Canada.

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The network process convolution (NPC) model effectively captures spatial correlations in road network crash data. This novel approach improves prediction accuracy for unobserved locations compared to existing methods.

Keywords:
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Area of Science:

  • Transportation Science
  • Statistical Modeling
  • Geospatial Analysis

Background:

  • Crash data on road networks often show spatial correlations due to unobserved factors.
  • Existing models struggle to fully incorporate the road network's inherent structure into spatial correlation analysis.

Purpose of the Study:

  • Introduce the network process convolution (NPC) model to address spatial correlation in road network crash data.
  • Improve prediction of crash frequency at unobserved locations by accounting for road network structure.

Main Methods:

  • Utilize a Gaussian Process (GP) approximated via kernel convolution to model spatial correlation.
  • Employ path distance on the road network for the GP's covariance function.
  • Implement Bayesian inference efficiently using R-INLA.

Main Results:

  • The NPC model demonstrates superior predictive performance for unobserved locations compared to the pCAR model.
  • Achieved lower mean absolute errors for crash counts and latent variables.
  • Provided shorter interval scores for singletons, indicating improved precision.

Conclusions:

  • The NPC model naturally integrates road network structure for spatially structured latent effects in crash data modeling.
  • Offers enhanced predictive capabilities for road network crash data analysis.
  • Provides a more efficient inference procedure than traditional Markov Chain Monte Carlo methods.