A comprehensive analysis of factors that influence interstate highway crash severity in Alabama

Abstract

This paper identifies factors that influence the severity of interstate crash outcomes and how they vary depending on the location and manner of collision. Four separate injury severity models were developed to explore the differences and similarities in crash factors between single-and multi-vehicle crashes that occurred in rural and urban areas of the state. Random parameters multinomial logit with heterogeneity in means and variances modeling approach was used to account for unobserved heterogeneity in the crash data. The model estimation results show that some driver behavioral factors such as speeding, aggressive driving, failure to use seatbelt, and driving without a valid license were found to significantly contribute to some form of injury outcome. The influence of roadway features such as type of opposing lane separation, collision type, temporal and lighting conditions on crash outcomes were also explored. Some differences and similarities in the associations between these factors and crash injury severity based on the manner and location of crash were unraveled. These findings are expected to guide the implementation of crash countermeasures on interstates. The findings of this study further support the evidence for the analysis of subsets of crash data to unravel underlying complex relationships within factors that influence crash injury severity.

Understanding the Factors That Are Associated with Motorcycle Crash Severity in Rural and Urban Areas of Ghana

“…Separate mixed multinomial logit injury severity models were estimated for rural and urban motorcycle crashes in Ghana. Like previous crash injury severity studies [28,46,61], during model estimation, variables were included in the specification if they had t-statistics corresponding to the 90% confidence interval on a two-tailed ttest. e random parameters, however, were included if their standard deviations had t-statistics corresponding to the 90% confidence interval.…”

“…[8] used cross-sectional observations and chi-square tests to investigate the prevalence of helmet use among motorcycle riders and pillion riders in Ghana. Due to the classification of crash severity into discrete outcomes, discrete outcome models, ordered (probit and logit models) and unordered (such as the multinomial logit model) models, have been used to analyze crash severity (see [15,17,27,28,[44][45][46] for injury severity methodology reviews). For instance, Ref.…”

“…For instance, Ref. [46] developed various forms of mixed multinomial logit models to investigate factors associated with injury outcomes for interstate crashes. Previous motorcycle injury severity studies have also used mixed multinomial logit [20,26,31], mixed multinomial logit with heterogeneity in means and variances [29], logistic regression [10,11,18,22,24,34], and generalized ordered logit models [12,25].…”

Leveraging probe data to model speeding on urban limited access highway segments: Examining the impact of operational performance, roadway characteristics, and COVID-19 pandemic

“…Upon the onset of the COVID-19 pandemic, various states across the United States (U.S.) issued stay-at-home orders. The unprecedented orders in turn caused a tremendous reduction in vehicle trips, and consequently the volume of traffic on roads; at the same time, roadway fatality rates increased across the country ( Doucette et al, 2021 , Stiles et al, 2021 , Adanu et al, 2021 , Dong et al, 2022 ). For instance, in Connecticut, despite a 43% reduction in Vehicle Miles Traveled (VMT), the rate of fatal single vehicle crashes increased by 4.1 times in the early stages of the pandemic ( Doucette et al, 2021 ).…”

“…Speeding is a contributing factor in many fatal and serious injury crashes ( Cooper, 1997 , Elvik, 2005 , Abegaz et al, 2014 , Adanu et al, 2021 , Wang and Cicchino, 2023 ). Researchers found that several factors impact speeding, including but not limited to roadway geometry and functional class ( Afghari et al, 2018 , Eluru et al, 2013 ), posted speed limit ( Yokoo and Levinson, 2019 ), time of day, time of week and holidays ( Jun, 2010 , Heydari et al, 2020 ), light conditions ( De Bellis et al, 2018 ), vehicle classification ( Afghari et al, 2018 ), weather conditions ( Kurte et al, 2019 ), enforcement ( Hauer et al, 1982 , Tay, 2005 ), and driver psychology or perceived risk ( Tucker and Marsh, 2021 ).…”