In crash frequency studies, correlated multivariate data are often obtained for each roadway entity longitudinally. The multivariate models would be a potential useful method for analysis, since they can account for the correlation among the specific crash types. However, one issue that arises with this correlated multivariate data is the number of zero counts increases as crash counts have man...

Because of the fact that vehicle crash tests are complex and complicated experiments it is advisable to establish their mathematical models. This paper contains an overview of the kinematic and dynamic relationships of a vehicle in a collision. There is also presented basic mathematical model representing a collision together with its analysis. The main part of this paper is devoted to methods ...

Numerous efforts have been devoted to investigating crash occurrence as related to roadway design features, environmental factors and traffic conditions. However, most of the research has relied on univariate count models; that is, traffic crash counts at different levels of severity are estimated separately, which may neglect shared information in unobserved error terms, reduce efficiency in p...

Although speed is considered to be one of the main crash contributory factors, research findings are inconsistent. Independent of the robustness of their statistical approaches, crash frequency models typically employ crash data that are aggregated using spatial criteria (e.g., crash counts by link termed as a link-based approach). In this approach, the variability in crashes between links is e...

Any reader of the paper by Laloux et al. [1] would have been left utterly confused after consulting the time series for the price of Japanese Government Bonds (JGB) around May 1995 looking for a log-periodic power law acceleration in the data. The authors of [1] now confess their error and admit that the prediction date given in [1] was incorrect and that the correct prediction was for August 1...

There has been a considerable amount of work devoted by transportation safety analysts to the development and application of new and innovative models for analyzing crash data. One important characteristic about crash data that has been documented in the literature is related to datasets that contained a large amount of zeros and a long or heavy tail (which creates highly dispersed data). For s...

Statistical modeling of traffic crashes has been of interest to researchers for decades. Over the most recent decade many crash models have accounted for extra-variation in crash counts--variation over and above that accounted for by the Poisson density. The extra--variation--or dispersion--is theorized to capture unaccounted for variation in crashes across sites. The majority of studies have a...

One of the most important needs of pedestrians is safety at crossing points, especially at intersections. Intersections are important parts of the urban road network because any disruption in them reduces the capacity of the entire network. The main objective of this research is to propose an appropriate method for prioritizing urban intersections with considering the important factors affectin...

Recently, a finite mixture of negative binomial (NB) regression models has been proposed to address the unobserved heterogeneity problem in vehicle crash data. This approach can provide useful information about features of the population under study. For a standard finite mixture of regression models, previous studies have used a fixed weight parameter that is applied to the entire dataset. How...

As part of the Wisconsin road weather safety initiative, the objective of this study is to assess the effects of rainfall on the severity of single-vehicle crashes on Wisconsin interstate highways utilizing polychotomous response models. Weather-related factors considered in this study include estimated rainfall intensity for 15 min prior to a crash occurrence, water film depth, temperature, wi...