Fusing crash data and surrogate safety measures for safety assessment: Development of a structural equation model with conditional autoregressive spatial effect and random parameters

Di Yang, Kun Xie, Kaan Ozbay, Hong Yang

Research output: Contribution to journalArticlepeer-review

Abstract

Most existing efforts to assess safety performance require sufficient crash data, which generally takes a few years to collect and suffers from certain limitations (such as long data collection time, under-reporting issue and so on). Alternatively, the surrogate safety measure (SSMs) based approach that can assess traffic safety by capturing the more frequent “near-crash” situations have been developed, but it is criticized for the potential sampling and measurement errors. This study proposes a new safety performance measure—Risk Status (RS), by fusing crash data and SSMs. Real-world connected vehicle data collected in the Safety Pilot Model Deployment (SPMD) project in Ann Arbor, Michigan is used to extract SSMs. With RS treated as a latent variable, a structural equation model with conditional autoregressive spatial effect and corridor-level random parameters is developed to model the interrelationship among RS, crash frequency, risk identified by SSMs, and contributing factors. The modeling results confirm the proposed interrelationship and the necessity to account for both spatial autocorrelation and unobserved heterogeneity. RS can integrate both crash frequency and SSMs together while controlling for observed and unobserved factors. RS is found to be a more reliable criterion for safety assessment in an implementation case of hotspot identification.

Original languageEnglish (US)
Article number105971
JournalAccident Analysis and Prevention
Volume152
DOIs
StatePublished - Mar 2021

Keywords

  • Connected vehicles
  • Latent variable
  • Safety performance measures
  • Spatial autocorrelation
  • Structural equation model
  • Surrogate safety measures
  • Unobserved heterogeneity

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Safety, Risk, Reliability and Quality
  • Public Health, Environmental and Occupational Health

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