Temporal instability and the analysis of highway accident data

• Published in: Analytic methods in accident research Vol. 17; pp. 1 - 13
• Main Author: Mannering, Fred
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2018
• Subjects: Accident likelihood; Accident severity; Attitudes; Cognitive science; Highway safety; Statistical and econometric methods; Temporal stability; Unobserved heterogeneity; Statistical and econometric methods; Unobserved heterogeneity; Attitudes; Accident severity; Temporal stability; Cognitive science; Highway safety; Accident likelihood
• ISSN: 2213-6657; 2213-6657
• DOI: 10.1016/j.amar.2017.10.002

•Temporal instability in statistical models of accident data is explored.•Statistical models of accident data may not be stable over time.•The effect of possible temporal instability on specific accident-modeling approaches is discussed.•The consequences of potential temporal instability are discussed. Virtually every statistical analysis of highway safety data is predicated on the assumption that the estimated model parameters are temporally stable. That is, the assumption that the effect of the determinants of accident likelihoods and resulting accident-injury severities do not change over time. This paper draws from research previously conducted in fields such as psychology, neuroscience, economics, and cognitive science to build a case for why we would not necessarily expect the effects of explanatory variables to be stable over time. The review of this literature suggests that temporal instability is likely to exist for a number of fundamental behavioral reasons, and this temporal instability is supported by the findings of several recent accident-data analyses. The paper goes on to discuss the implications of this temporal instability for contemporary accident-data modeling methods (unobserved heterogeneity, data driven, traditional, and causal inference methods) and concludes with a discussion of how temporal instability might be addressed and how its likely presence can be accounted for to better interpret accident data-analysis findings.