Multilevel analysis in road safety research

Hierarchical structures in road safety data are receiving increasing attention in the literature and multilevel (ML) models are proposed for appropriately handling the resulting dependences among the observations. However, so far no empirical synthesis exists of the actual added value of ML modelling techniques as compared to other modelling approaches. This paper summarizes the statistical and conceptual background and motivations for multilevel analyses in road safety research. It then provides a review of several ML analyses applied to aggregate and disaggregate (accident) data. In each case, the relevance of ML modelling techniques is assessed by examining whether ML model formulations (i) allow improving the fit of the model to the data, (ii) allow identifying and explaining random variation at specific levels of the hierarchy considered, and (iii) yield different (more correct) conclusions than single-level model formulations with respect to the significance of the parameter estimates. The evidence reviewed offers different conclusions depending on whether the analysis concerns aggregate data or disaggregate data. In the first case, the application of ML analysis techniques appears straightforward and relevant. The studies based on disaggregate accident data, on the other hand, offer mixed findings: computational problems can be encountered, and ML applications are not systematically necessary. The general recommendation concerning disaggregate accident data is to proceed to a preliminary investigation of the necessity of ML analyses and of the additional information to be expected from their application.

• Record URL:
  • https://doi.org/10.1016/j.aap.2013.04.035
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0001457513001863
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • Abstract reprinted with permission from Elsevier.
• Authors:
  • Dupont, Emmanuelle
  • Papadimitriou, Eleonora
  • Martensen, Heike
  • Yannis, George
• Publication Date: 2013-11

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 402-411
• Serial:
  • Accident Analysis & Prevention
  • Volume: 60
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Data collection; Highway safety; Mathematical models; Research; Statistical analysis; Traffic crashes
• Uncontrolled Terms: Multilevel statistical models
• Subject Areas: Highways; Safety and Human Factors; I80: Accident Studies;

Filing Info

• Accession Number: 01502592
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 30 2013 11:05AM