Dominique Lord

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.

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.

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.

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.

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.