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A multivariate-based variable selection framework for clustering traffic conflicts in a brazilian freeway.

Miriam Rocha1, Michel Anzanello2, Felipe Caleffi3

  • 1Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre, RS 90035-180, Brazil; Center of Engineering, Federal Rural University of Semi-Arid, Mossoró, RN 59.625-900, Brazil.

Accident; Analysis and Prevention
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Summary

Road accidents cause over a million deaths yearly. This study uses self-organizing maps (SOM) and nonlinear principal component analysis (NLPCA) to group traffic conflicts, identifying lower average speeds and higher speed variability during congestion as key contributing factors.

Keywords:
ClusteringCollision riskNLPCASOMTraffic conflictsvariable selection

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Area of Science:

  • Traffic Safety
  • Data Science
  • Transportation Engineering

Background:

  • Road accidents result in over a million global fatalities and injuries annually.
  • Understanding traffic conflict causes is crucial for developing effective mitigation strategies.

Purpose of the Study:

  • To propose a framework for grouping traffic conflicts based on similar profiles and contributing factors.
  • To enhance clustering quality by identifying the most informative variables for collision event grouping.

Main Methods:

  • Utilized self-organizing maps (SOM) for traffic conflict grouping.
  • Developed a novel variable importance index based on nonlinear principal component analysis (NLPCA).
  • Employed a backward variable selection procedure guided by the Davies-Bouldin (DB) index to assess clustering quality.

Main Results:

  • Applied the framework to a Brazilian highway dataset to allocate traffic conflicts into similar profile groups.
  • Identified lower average speeds, common during congestion, as a factor contributing to conflict occurrence.
  • Found higher speed variability (standard deviation, coefficient of variation) near congestion periods also contributes to conflicts.

Conclusions:

  • The proposed framework effectively groups traffic conflicts by identifying key contributing factors.
  • Traffic congestion, characterized by lower average speeds and increased speed variability, significantly increases collision risks.
  • Findings can inform traffic authorities in developing targeted safety interventions.