This study aimed to investigate the relative performance of two models (negative binomial (NB) model and two-component finite mixture of negative binomial models (FMNB-2)) in terms of developing crash modification factors (CMFs). Crash data on rural multilane divided highways in California and Texas were modeled with the two models, and crash modification functions (CMFunctions) were derived. T...

BACKGROUND Daytime running lights (DRLs) are a safety feature intended to reduce crashes by increasing the contrast between vehicles and the background. AIMS The purpose of this study was to determine whether there is an association between vehicles in the USA being equipped with DRLs and crash rates. METHODS This was a retrospective study using the Minnesota Department of Transportation (M...

The most common probabilistic structure of the models used by transportation safety analysts for modeling motor vehicle crashes are the traditional Poisson and Poissongamma (or Negative Binomial) distributions. Since crash data have been shown to exhibit over-dispersion, Poisson-gamma models are usually preferred over Poisson regression models. Up until recently, the dispersion parameter of Poi...

This paper studies the intraday spillovers of 2010 U.S. Flash Crash to international equity markets. We document a substantial and almost immediate echo crash in Latin America. Using data for 148 firms trading Argentina, Brazil, Chile, or Mexico, we estimate price declines up 10% within minutes after crash. Estimates two different factor models indicate that this followed from normal interdepen...

Truck-involved crashes have higher crash severity than non-truck-involved crashes. There have been many studies about the frequency of crashes and the development of severity models, but those studies only analyzed the relationship between observed variables. To identify why more people are injured or killed when trucks are involved in the crash, we must examine to quantify the complex causal r...

Abstract Background Urban safety performance functions are used to predict crash frequencies, mostly based on Negative Binomial (NB) count models. They could be differentiated for considering homogeneous subsets of segments/intersections and different predictors. Materials methods The main research questions concerned: a) finding the best possible segments intersections modelling, by discussing...

Purpose An innovative methodology using naturalistic driving data was used to examine the association between visual sensory and visual-cognitive function and rates of future crash or near-crash involvement among older drivers. Methods The Strategic Highway Research Program (SHRP2) Naturalistic Driving Study was used for this prospective analysis. The sample consisted of N = 659 drivers aged ...

Human body models (HBMs) have the potential to provide significant insights into the pediatric response to impact. This study describes a scalable/posable approach to perform child accident reconstructions using the Position and Personalize Advanced Human Body Models for Injury Prediction (PIPER) scalable child HBM of different ages and in different positions obtained by the PIPER tool. Overall...

Abstract Machine learning compilers are large software containing complex transformations for deep models, and any buggy transformation may cause a crash or silently bring regression to the prediction accuracy performance. This paper proposes an SMT-based translation validation framework Multi-Level IR (MLIR), compiler used by many compilers. It SMT encoding tailored that is over-approximation ...