Predicting downgrade crash frequency with the random-parameters negative binomial model: Insights into the impacts of geometric variables on downgrade crashes in Wyoming

• Published in: IATSS research Vol. 44; no. 2; pp. 94 - 102
• Main Authors: Moomen, Milhan, Rezapour, Mahdi, Raja, Mustaffa, Ksaibati, Khaled
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2020; Elsevier
• Subjects: Crash frequency; Downgrades; Geometric factors; Random-parameters; Downgrades; Random-parameters; Crash frequency; Geometric factors
• ISSN: 0386-1112; 2210-4240
• DOI: 10.1016/j.iatssr.2019.09.002

Road deaths, injuries and property damage place a huge burden on the economy of most nations. Wyoming has a high crash rate on mountain passes. The crash rates observed in the state is as a result of many factors mainly related to the challenging mountainous terrain in the state, which places extra burden on drivers in terms of requiring higher levels of alertness and driving skill. This study was conducted to investigate factors leading to crashes on Wyoming downgrades, with a focus on geometric variables. Traditionally, crash frequency analysis is conducted using count models such as Poisson or negative binomial models. However, factors that affect crash frequency are known to vary across observations. The use of a methodology that fails to take into account heterogeneity in observed and unobserved effects relating to roadway characteristics can lead to biased and inconsistent estimates. Inferences made from such parameter estimates may be misleading. This study employed the random-parameters negative binomial regression models to evaluate the impact of geometric variables on crash frequency. Five separate models were estimated for total, fatal/injury, property damage only (PDO), truck, and non-truck crash frequencies. Several geometric and traffic variables were found to influence the frequency of crashes on downgrades. These included segment length, vertical grade, shoulder width, lane width, presence of downgrade warning sign, vertical curve length, presence of a passing lane, percentage of trucks, number of lanes and AADT. The results suggest that segment length, lane width, presence of a passing lane, presence of a downgrade warning sign, vertical grade, and percentage of trucks are best modeled as random parameters. The findings of this study will provide transportation agencies with a better understanding of the impact of geometric variables on downgrade crashes. •Crash risks on downgrades are greater due to difficult terrain and roadway geometry.•Random parameters negative binomial regression models were used for the effect of road geometry on crash frequency.•Five separate models were estimated for crash frequency models including total, fatal/injury, and truck crashes.•The random parameters models provided better insights on the influence of geometric variables on crash frequency.