Crash prediction models still constitute one of the primary tools for estimating traffic safety. These statistical models play a vital role in various types of safety studies. With a few exceptions, they have often been employed to estimate the number of crashes per unit of time for an entire highway segment or intersection, without distinguishing the influence different sub-groups have on cras...

The Highway Safety Manual (HSM) prediction models are fitted and validated based on crash data collected from a selected number of states in the United States. Therefore, for a jurisdiction to be able to fully benefit from applying these models, it is necessary to calibrate or recalibrate them to local conditions. The first edition of the HSM recommends calibrating the models using a one-size-f...

The identification of crash hotspots is the critical component of the highway safety management process. Errors in hotspot identification (HSID) may result in the inefficient use of resources for safety improvements. One HSID method that is based on the empirical Bayesian (EB) method has been widely used as an effective approach for identifying crash-prone sites. For the EB method, the negative...

The aim of the present study was to evaluate mortality and functional outcome in old and very old patients with severe traumatic brain injury (TBI) and compare to the predicted outcome according to the internet based CRASH (Corticosteroid Randomization After Significant Head injury) model based prediction, from the Medical Research Council (MRC). Methods. Prospective, national multicenter study...

Traditionally, transportation safety analysts have used the empirical Bayes (EB) method to improve the estimate of the long-term mean of individual sites; to correct for the regression-to-the-mean (RTM) bias in before-after studies; and to identify hotspots or high risk locations. The EB method combines two different sources of information: (1) the expected number of crashes estimated via crash...

The Highway Safety Manual [HSM, 2010] recommends safety evaluations be performed before implementing any roadway treatment to predict the expected safety consequences. Safety consequences can be measured using crash prediction models, Crash Modification Factor (CMFs), or both. This paper develops a CMF to show the expected impact of red-light cameras (RLCs) on safety at signalized intersections...

BACKGROUND External validation on different TBI populations is important in order to assess the generalizability of prognostic models to different settings. We aimed to externally validate recently developed models for prediction of six month unfavourable outcome and six month mortality. METHODS The International Neurotrauma Research Organization - Prehospital dataset (INRO-PH) was collected ...

The IC industry is built on a foundation of simulation and prediction. We use models for frequency, power, yield, cost... We rarely prototype, or crash test! Models are derived from “characterization” which is done at multiple nested levels. Manufacturing, Devices, Gates, Units, Chips. Technology complexity beyond the 90nm node is making this characterization and representation increasingly dif...

Pavement rehabilitation could affect the accident severity index (ASI) since restoration measures means more safety for road users. No research or project has been carried out to identify hazard points to build a linear model based on crash severity index. One of the very popular accident severity index models used in all countries is based on linear models to rehabilitate pavements and this pa...