The influence of zonal configurations on macro-level crash modeling

Xiaoqi Zhai, Helai Huang, Pengpeng Xu, N. N. Sze

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)

Abstract

This study investigated the impacts of zonal configurations on macro-level traffic safety analysis for crashes of different severity levels. Bayesian multivariate Poisson-lognormal models with multivariate conditional auto-regressive priors were developed to account for the spatial autocorrelation between adjacent geographical units and correlations among crash types of four ordinal severity levels, i.e. fatality, severe injury, slight injury and no injury. For the purpose of evaluating the effects of zonal configurations on macro-level traffic safety analysis, the proposed model was calibrated using crash data of four types of geographical units, i.e. block group, traffic analysis zone, census tract and zip code tabulation area, in Hillsborough County of Florida. The study empirically revealed the extensive presence and the significance of MAUP in macro-level safety analysis based on the existing zonal configurations. It gave out a warning and encouraged more research efforts on rational application of macroscopic safety analysis with different zonal configurations.

Original languageEnglish
Pages (from-to)417-434
Number of pages18
JournalTransportmetrica A: Transport Science
Volume15
Issue number2
DOIs
Publication statusPublished - 29 Nov 2019

Keywords

  • crash severity
  • geographical configuration
  • macro-level traffic safety analysis
  • Modifiable areal unit problem

ASJC Scopus subject areas

  • Transportation
  • Engineering(all)

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