Does Space Influence on the Frequency and Severity of the Distraction-Affected Vehicle Crashes? An Empirical Evidence from the Central Ohio

This study investigates spatial dependencies between frequency and within severity of vehicle crashes caused by distracted driving, along with the role of the built and socio-demographic environments in the Columbus Metropolitan Area, Ohio. The authors adopt a full Bayesian hierarchical framework with Multivariate Conditional Autoregressive Priors to account for the complex spatial correlation structure as well as the unobserved heterogeneity. Using aggregated crash count data (Property Damage Only and Bodily Injuries) for the 414 census tracts, the analysis outcomes reveal that census tracts providing more jobs and having a higher proportion of commercial land use would have higher likelihood of relative crash risks in both severity levels. Inclusion of correlation structure between frequency as well as within crash-severity-level has proven a significant increase on the performance of the model, verifying influences of space on the frequency and severity of distraction-affected vehicle crashes. In addition, this research presents areas of higher relative risks (spatial clusters) that have 1.5 times elevated risk of collision than other census tracts. The identification of areas of excessive risks informs us to devise policies to mitigate negative consequences of distraction-affected crashes.

• Record URL:
  https://doi.org/10.1016/j.aap.2020.105606
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0001457519312904
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • © 2020 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Lym, Youngbin
  • Chen, Zhenhua
• Publication Date: 2020-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • Accident Analysis & Prevention
  • Volume: 144
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Crash characteristics; Crash rates; Crash risk forecasting; Crash severity; Distraction; High risk locations; Human factors in crashes
• Geographic Terms: Columbus (Ohio)
• Subject Areas: Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01747994
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 17 2020 9:38AM