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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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Evaluating the double Poisson generalized linear model
TL;DR: The modeling results indicate that the double Poisson (DP) generalized linear model (GLM) with its normalizing constant approximated by the new method can handle crash data characterized by over- and under-dispersion.
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Investigating the safety and operational benefits of mixed traffic environments with different automated vehicle market penetration rates in the proximity of a driveway on an urban arterial
TL;DR: In this article, the authors evaluated the impact of various AV Market Penetration Rates (MPR) on the safety and operation of urban arterials in proximity of a driveway under different traffic levels of service (LOS).
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Comparison of Sichel and Negative Binomial Models in Hot Spot Identification
TL;DR: In this paper, the authors compared the performance of the two crash prediction models in identifying hot spots with the empirical Bayesian (EB) method, and found that the NB model yielded better EB estimates than the SI model.
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Horizontal Curve Accident Modification Factor with Consideration of Driveway Density on Rural Four-Lane Highways in Texas
Kay Fitzpatrick,Kay Fitzpatrick,Dominique Lord,Dominique Lord,Byung-Jung Park,Byung-Jung Park +5 more
TL;DR: In this article, the authors developed a horizontal curve accident modification factor (AMF) for rural four-lane divided and undivided highways and determined if the effect of driveway density is different for horizontal curves as compared to tangent sections.
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Estimating Dispersion Parameter of Negative Binomial Distribution for Analysis of Crash Data: Bootstrapped Maximum Likelihood Method
TL;DR: In this article, a bootstrapped maximum likelihood method is proposed to improve the estimation of the dispersion parameter of the negative binomial distribution for modeling motor vehicle collisions by combining the technique of bootstrap resampling with the maximum likelihood estimation method.