Journal ArticleDOI
Full Bayes Poisson gamma, Poisson lognormal, and zero inflated random effects models: Comparing the precision of crash frequency estimates
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TLDR
It was found that the rankings of the fixed-over-time random effects models are very consistent among them, and the standard errors of the crash frequency estimates are significantly reduced for the majority of the segments on the top of the ranking.About:
This article is published in Accident Analysis & Prevention.The article was published on 2013-01-01. It has received 84 citations till now. The article focuses on the topics: Random effects model & Poisson distribution.read more
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Analytic methods in accident research: Methodological frontier and future directions
TL;DR: A review of the evolution of methodological applications and available data in highway-accident research can be found in this article, where fruitful directions for future methodological developments are identified and the role that new data sources will play in defining these directions is discussed.
Journal ArticleDOI
Unobserved heterogeneity and the statistical analysis of highway accident data
TL;DR: In this article, a detailed discussion of the unobserved heterogeneity in highway accident data and analysis is presented along with their strengths and weaknesses, as well as a summary of the fundamental issues and directions for future methodological work that address this problem.
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Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data
TL;DR: The model estimation results indicate that the traffic speed, traffic volume, curvature and chemically wet road surface indicator are better modeled as random parameters, while 5-am indicator and number of merging ramps per lane per mile are found to decrease crash likelihood.
Journal ArticleDOI
Built environment factors in explaining the automobile-involved bicycle crash frequencies: a spatial statistic approach
TL;DR: In this article, a Poisson lognormal random effects model using hierarchal Bayesian estimation was employed to understand the relationship between built environment factors and bicycle crashes with motor vehicles involved in Seattle.
References
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Book
Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Journal ArticleDOI
Bayesian measures of model complexity and fit
TL;DR: In this paper, the authors consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined and derive a measure pD for the effective number in a model as the difference between the posterior mean of the deviances and the deviance at the posterior means of the parameters of interest, which is related to other information criteria and has an approximate decision theoretic justification.
Book
Bayes and Empirical Bayes Methods for Data Analysis
TL;DR: Approaches for Statistical Inference: The Bayes Approach, Model Criticism and Selection, and Performance of Bayes Procedures.
Journal ArticleDOI
The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives
Dominique Lord,Fred L. Mannering +1 more
TL;DR: In the absence of detailed driving data that would help improve the identification of cause and effect relationships with individual vehicle crashes, most researchers have addressed this problem by framing it in terms of understanding the factors that affect the frequency of crashes -the number of crashes occurring in some geographical space (usually a roadway segment or intersection) over some specified time period as mentioned in this paper.
Journal ArticleDOI
Bayes and Empirical Bayes Methods for Data Analysis
TL;DR: In this article, Bayes and empirical Bayes methods for data analysis are presented for Data Analysis. But, they do not consider the use of data augmentation in data analysis.
Related Papers (5)
The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives
Dominique Lord,Fred L. Mannering +1 more