Developing robust and reliable statistical models to estimate, analyze, and understand crash
data is a key element in various highway safety evaluation tasks. Crash data have …

Crash data are often highly dispersed; it may also include a large amount of zero
observations or have a long tail. The traditional Negative Binomial (NB) model cannot model …

This paper presents a negative binomial random parameter model with heterogeneity in
means and variance to capture the effect of heterogeneous effect of ramp type, alignment …

Abstract Part-time Shoulder Use (PTSU) is a traffic management and operation strategy that
allows the use of the left or right shoulder as a travel lane, typically during the peak hours of …

Current and past investigations into road accidents have mainly focused on high-volume
roads which have experienced a significant number of collisions. However, low-volume …

Identifying hazardous crash sites (or hotspots) is a crucial step in highway safety
management. The Negative Binomial (NB) model is the most common model used in safety …

Analyzing crash data is a complex and labor-intensive process that requires careful
consideration of multiple interdependent modeling aspects, such as functional forms …

Introduction: Speeding behavior is a major threat to road traffic safety, which can increase
crash risks and result in severe injury outcomes. Although several studies have been …

Estimating crash data count models poses a significant challenge which requires extensive
knowledge, experience, and meticulous hypothesis testing to capture underlying trends …

Unobserved heterogeneity of crashes remains a significant issue for freeways that influence
crash prediction, and therefore deserves much attention. Using a fusion data set of crash …