A note on modeling vehicle accident frequencies with random-parameters count models.

By Panagiotis Ch. Anastasopoulos, Fred L. Mannering. Accident Analysis and Prevention, 2009

728 citations recorded by the source corpus.

Subfields: Building and Construction, Civil and Structural Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/ab963b2b9e0449b7518197f18197ce2706eaffa6

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Citations in the corpus

Model of Relationship between Interstate Crash Occurrence and Geometrics (2011, methodology): “Anastasopoulos and Mannering used random parameter NB regression to account and correct for heterogeneity, which can arise from a number of factors that relate to road geometrics, pavement, and traffic characteristics, driver behavior, vehicle types, socioeconomic factors, variations in police recor”
Modeling over-dispersed crash data with a long tail: Examining the accuracy of the dispersion parameter in Negative Binomial models (2015, methodology): “Besides the mixed-Poisson models, the random parameters count models (Anastasopoulos and Mannering, 2009; Chen and Tarko, 2014), finite mixture and Markov switching models (Park and Lord, 2009; Malyshkina et al., 2009; Zou et al., 2013b; Zou et al., 2014), generalized ordered-response models…”
A latent variable representation of count data models to accommodate spatial and temporal dependence: application to predicting crash frequency at intersections (2011, methodology): “However, unlike most other studies, we also are able to consider random coefficients in a way that is simpler and different from the usual mixing approach used in traditional count models (see Anastasopoulos and Mannering, 2009 and the discussion in Section 2.1).”
Developing a Random Parameters Negative Binomial-Lindley Model to analyze highly over-dispersed crash count data (2018, background): “The normal distribution was found to provide the best statistical fit (Li et al. 2008, Anastasopoulos and Mannering 2009).”
Analyzing different functional forms of the varying weight parameter for finite mixture of negative binomial regression models (2014, methodology): “2009; Anastasopoulos and Mannering, 2009; EIBasyouny and Sayed, 2010). Recently, a quantile regression method was introduced by Qin and Reyes (2011) to analyze the heterogeneous crash data.”
Safety impacts of pavement surface roughness at two-lane and multi-lane highways: accounting for heterogeneity and seemingly unrelated correlation across crash severities (2019, background): “It may be noted that a random parameter should be used only if its estimated σ 2 is significantly greater than zero, otherwise, a fixed parameter is preferred (Anastasopoulos and Mannering 2009).”