Modeling crash frequency and severity with spatiotemporal dependence

Yu-Chiun Chiou*, Chiang Fu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

This study proposes a novel multinomial generalized Poisson model with error components and spatiotemporal dependence (ST-EMGP) to analyze multi-period crash frequency and severity data. The proposed model not only simultaneously models crash frequency and severity, but also accommodates spatial and temporal dependence (spatiotemporal dependence) by specifying a spatiotemporal function. To demonstrate the applicability of the proposed model, a case study is conducted on five consecutive years' (2004-2008) crash data of Taiwan's Freeway No. 1. Estimation results show that ST-EMGP model performs better than the models without considering spatiotemporal dependence in terms of adjusted likelihood ratio index, consistent Akaike information criterion and log-likelihood test. Additionally, the estimated ST-EMGP model shows that spatial and temporal dependences exist and correlate mutually. Spatial dependence may overstate its impact magnitude, but underestimate its impact range when temporal dependence is ignored. According to the distribution and regression results of spatiotemporal effects, temporal effects are higher in crash frequency and are mainly affected by traffic characteristics; while spatial effects are higher in severe crash severity levels and are mainly affected by geometric configuration. Obviously, the proposed model can successfully elucidate the sources of spatiotemporal dependence as well as their effects on crash frequency and severity.

Original languageEnglish
Pages (from-to)43-58
Number of pages16
JournalAnalytic Methods in Accident Research
Volume5-6
DOIs
StatePublished - 1 Jan 2015

Keywords

  • Crash frequency and severity modeling
  • Multi-period accident data
  • Multinomial generalized Poisson
  • Spatiotemporal dependence

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