Hot spot identification (HSID) aims to identify potential sites—roadway segments,
intersections, crosswalks, interchanges, ramps, etc.—with disproportionately high crash risk …

Identification of test scenarios for Automated Driving Systems (ADSs) remains a key
challenge for the Verification & Validation of ADSs. Various approaches including data …

Highway agencies continue to show interest in measuring pavement condition effects on
safety. This paper estimates univariate negative binomial (UNB) and random-parameters …

Although cycling has benefits to environment and physical health, bicyclists are vulnerable
road users. Prior studies have identified the environment, traffic and road user factors that …

Although several studies have used logit or probit models and their variants to fit data of
accident severity on roadway segments, few have investigated accident severity at a railroad …

This study applies mixture components in a multivariate random parameters spatial model
for zonal crash counts. Three different modeling formulations are employed to demonstrate …

Right-turn movements (equivalent to left turn movements for countries that drive on the right)
at intersections are among the most complex driving maneuvers and require a high level of …

Modeling highway traffic crash frequency is an important approach for identifying high crash
risk areas that can help transportation agencies allocate limited resources more efficiently …

Many road safety researchers have used crash prediction models, such as Poisson and
negative binomial regression models, to investigate the associations between crash …

This study develops methods to obtain maps to determine traffic Hot Spots in Konya, Turkey,
by applying linear analysis supported by Geographical Information Systems (GIS). Hot Spot …