Network screening for large urban road networks: Using GPS data and surrogate measures to model crash frequency and severity

Crash frequency and injury severity are independent dimensions defining crash risk in road safety management and network screening. Traditional screening techniques model crashes using regression and historical crash data, making them intrinsically reactive. In response, surrogate measures of safety have become a popular proactive alternative. The purpose of this paper is to develop models for crash frequency and severity incorporating GPS-derived surrogate safety measures (SSMs) as predictive variables. SSMs based on vehicle manoeuvres and traffic flow were extracted from data collected in Quebec City. The mixed multivariate outcome is estimated using two models; a Full Bayes Spatial Negative Binomial model for crash frequency estimated using the Integrated Nested Laplace Approximation approach and a fractional Multinomial Logit model for crash severity. Model outcomes are combined to generate posterior expected crash frequency at each severity level and rank sites based on crash cost. The crash frequency model was accurate at the network scale, with the majority of proposed SSMs statistically significant at 95% confidence and the direction of their effect generally consistent with previous research. In the crash severity model, fewer variables were significant, yet the direction of the effect of all significant variables was again consistent with previous research. Correlations between rankings predicted by the mixed multivariate model and by the crash data were adequate for intersections (0.46) but were poorer for links (0.25). The ability to prioritize sites based on GPS data and SSMs rather than historical crash data represents a substantial contribution to the field of road safety.

• Record URL:
  • https://doi.org/10.1016/j.aap.2019.02.016
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0001457518303403
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Stipancic, Joshua
  • Miranda-Moreno, Luis
  • Saunier, Nicolas
  • 0000-0003-0218-7932
  • Labbe, Aurélie
• Publication Date: 2019-4

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 290-301
• Serial:
  • Accident Analysis & Prevention
  • Volume: 125
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Crash rates; Crash severity; Global Positioning System; Highway safety; Multinomial logits; Networks; Urban highways
• Uncontrolled Terms: Bayesian models; Crash models; Surrogate safety measures
• Geographic Terms: Quebec (Canada)
• Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01703164
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 26 2019 4:59PM