In the last fifty years, researchers have developed statistical, data-driven, analytical, and
algorithmic approaches for designing and improving emergency response management …

This paper presents a study aimed at comparing the outcome of two geostatistical-based
approaches, namely kernel density estimation (KDE) and kriging, for identifying crash …

Accident reduction factors are used to predict the change in accident occurrence which a
countermeasure can be expected to cause. Since ethical and legal obstacles preclude the …

Recent years have seen considerable progress in techniques for establishing relationships
between accidents, flows and road or junction geometry. It is becoming increasingly …

The identification of sites requiring investigation for possible safety treatment is one of the
most important aspects of infrastructure safety management and has been the subject of …

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 …

This paper presents a spatial clustering method for macro-level traffic crash analysis based
on open source point-of-interest (POI) data. Traffic crashes are discrete and non-negative …

Identification of hot spots, also known as the sites with promise, black spots, accident-prone
locations, or priority investigation locations, is an important and routine activity for improving …

Procedures for the identification of black spots, or hazardous locations, are attempts to select
some sites out of many to improve safety. These are sites with promise. The historical and …

This paper develops a GIS-based Bayesian approach for intra-city motor vehicle crash
analysis. Five-year crash data for Harris County (primarily the City of Houston), Texas are …