In recent years, there has been a renewed interest in applying statistical ranking criteria to
identify sites on a road network, which potentially present high traffic crash risks or are over …

The use of statistical models for analyzing traffic safety (crash) data has been well-
established. However, time series techniques have traditionally been underrepresented in …

The identification of crash hotspots is the first step of the highway safety management
process. Errors in hotspot identification may result in the inefficient use of resources for …

This study aims to explore the potential of using big data in advancing the pedestrian risk
analysis including the investigation of contributing factors and the hotspot identification …

Traditionally, highway safety analyses have used univariate Poisson or negative binomial
distributions to model crash counts for different levels of crash severity. Because …

Sammendrag: The report describes state-of-the-art approaches (ie Rapporten beskriver"
state-of-the-art" tilnærmingsmåter ideal approaches) to road accident black spot(dvs de best …

Developing sound or reliable statistical models for analyzing motor vehicle crashes are very
important in highway safety studies. However, a significant difficulty associated with the …

This paper conducted a comprehensive study on the injury severity of motor vehicle–
pedestrian crashes at 489 urban intersections across a dense road network based on high …

This study proposes a Bayesian spatio-temporal interaction approach for hotspot
identification by applying the full Bayesian (FB) technique in the context of macroscopic …

This study proposes a framework of a model-based hot spot identification method by
applying full Bayes (FB) technique. In comparison with the state-of-the-art approach [ie …