Temporal instability and the analysis of highway accident data

By Fred L. Mannering. 2018

464 citations recorded by the source corpus.

Subfields: Safety, Risk, Reliability and Quality, Speech and Hearing, Transportation

View Paper: https://www.semanticscholar.org/paper/0e4f8f0379a55897bff33a9a858315b358d78978

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Citations in the corpus

Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes (2018, background): “, 2017), and the effects of explanatory variables may also be instable over space and time (Mannering, 2018), which should be considered in future studies.”
Temporal stability of driver injury severities in animal-vehicle collisions: A random parameters with heterogeneity in means (and variances) approach (2020, background): “Above it all, the way that the crash data being aggregated over time (weeks, months or years) to obtain sufficient observations may arise concern with temporal instability in the crash data analyses (Mannering, 2018).”
Analysis of accident injury-severity outcomes: The zero-inflated hierarchical ordered probit model with correlated disturbances (2018, methodology): “…formulations of the traditional ordered and discrete outcome frameworks, which are typically used in the analysis of accident injury-severity data (for a detailed review, see: Lord and Mannering, 2010; Savolainen et al., 2011; Mannering and Bhat, 2014, Mannering et al., 2016; Mannering, 2018).”
Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis (2020, background): “However, the generalizability of the ﬁxed-effects results can be questionable, and even in a truly randomized trial likely temporal shifts in observed behavior can make prediction problematic with ongoing temporal variations inducing unknown errors in ﬁxed effects (Mannering, 2018).”