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Pedestrian crash analysis with latent class clustering method.

Ming Sun1, Xiaoduan Sun1, Donghui Shan2

  • 1Department of Civil Engineering, University of Louisiana at Lafayette, Madison Hall 254Q, 131 Rex Street, Lafayette, LA 70504, United States.

Accident; Analysis and Prevention
|January 10, 2019
PubMed
Summary
This summary is machine-generated.

Pedestrian deaths are rising nationally and in Louisiana. This study uses Latent Class Cluster (LCC) and Multinomial Logit (MNL) models to uncover hidden factors in 14,236 pedestrian crashes from 2006-2015.

Keywords:
Cluster analysisContributing factorsLatent classPedestrian safetySeverity

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Area of Science:

  • Traffic Safety
  • Transportation Engineering
  • Public Health

Background:

  • Pedestrian fatalities are increasing in the U.S., with Louisiana's rate exceeding the national average.
  • In 2015, Louisiana reported 110 pedestrian fatalities, representing 14.6% of total traffic fatalities.
  • Identifying key causes of pedestrian crashes is challenging due to data heterogeneity.

Purpose of the Study:

  • To investigate the causes of pedestrian crashes in Louisiana.
  • To segment pedestrian crashes using the Latent Class Cluster (LCC) model.
  • To identify factors influencing pedestrian crash severity using Multinomial Logit (MNL) models on LCC-derived clusters.

Main Methods:

  • Latent Class Cluster (LCC) analysis to segment 14,236 pedestrian crashes (2006-2015).
  • Multinomial Logit (MNL) models to analyze crash severity within identified clusters.
  • Combined data analysis to reveal previously hidden crash characteristics.

Main Results:

  • LCC model successfully segmented pedestrian crashes, enabling deeper analysis.
  • MNL models identified significant factors contributing to crash severity within specific clusters.
  • Previously unrecognized significant variables include pedestrian crossing behavior, time of day (midnight-6 PM), lighting conditions (dark-unlighted, dark-lighted), and non-intersection locations.

Conclusions:

  • The combined LCC and MNL approach effectively reveals hidden relationships in traffic safety data.
  • This methodology can identify specific risk factors for different pedestrian crash segments.
  • Findings offer targeted insights for developing effective pedestrian safety interventions in Louisiana.