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Dominique Lord
Researcher at Texas A&M University
Publications - 226
Citations - 12815
Dominique Lord is an academic researcher from Texas A&M University. The author has contributed to research in topics: Poison control & Crash. The author has an hindex of 46, co-authored 216 publications receiving 11248 citations. Previous affiliations of Dominique Lord include Ryerson University & University of Washington.
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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.
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The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives.
TL;DR: This paper summarizes the evolution of research and current thinking as it relates to the statistical analysis of motor-vehicle injury severities, and provides a discussion of future methodological directions.
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Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory
TL;DR: In this article, it is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials, and that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process.
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Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and bayes versus empirical bayes methods
Shaw-Pin Miaou,Dominique Lord +1 more
TL;DR: It is demonstrated that, for a given data set, a large number of plausible functional forms with almost the same overall statistical goodness of fit (GOF) is possible, and an alternative class of logical formulations that may enable a richer interpretation of the data is introduced.
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Safety Effect of Roundabout Conversions in the United States: Empirical Bayes Observational Before-After Study
TL;DR: A before-after study was conducted using the empirical Bayes procedure, which accounts for regression to the mean and traffic volume changes that usually accompany conversion of intersections to roundabouts and suggests that roundabout installation should be strongly promoted as an effective safety treatment for intersections.