Examining driver injury severity in motor vehicle crashes: A copula-based approach considering temporal heterogeneity in a developing country context

Using data from a developing country, the current study develops a copula-based joint modeling framework to study crash type and driver injury severity as two dimensions of the severity process. To be specific, a copula-based multinomial logit model (for crash type) and generalized ordered logit model (for driver severity) is estimated in the study. The data for our analysis is drawn from Bangladesh for the years of 2000 to 2015. Given the presence of multiple years of data, the authors develop a novel spline variable generation approach that facilitates easy testing of variation in parameters across time in crash type and severity components. A comprehensive set of independent variables including driver and vehicle characteristics, roadway attributes, environmental and weather information, and temporal factors are considered for the analysis. The model results identify several important variables (such as driving under the influence of drug and alcohol, speeding, vehicle type, maneuvering, vehicle fitness, location type, road class, road geometry, facility type, surface quality, time of the day, season, and light conditions) affecting crash type and severity while also highlighting the presence of temporal instability for a subset of parameters. The superior model performance was further highlighted by testing its performance using a holdout sample. Further, an elasticity exercise illustrates the influence of the exogenous variables on crash type and injury severity dimensions. The study findings can assist policy makers in adopting appropriate strategies to make roads safer in developing countries.

• Record URL:
  • https://doi.org/10.1016/j.aap.2024.107721
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0001457524002665
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Pervaz, Shahrior
  • 0000-0001-7966-7083
  • Bhowmik, Tanmoy
  • 0000-0002-0258-1692
  • Eluru, Naveen
  • 0000-0003-1221-4113
• Publication Date: 2024-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 107721
• Serial:
  • Accident Analysis & Prevention
  • Volume: 206
  • 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 injuries; Crash types; Drivers; Injury severity; Mathematical models
• Geographic Terms: Bangladesh
• Subject Areas: Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01927817
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 22 2024 3:09PM