This study aims to assess the relationship between county-level fatal crash injuries and road environmental characteristics at all times of the day and during the rush and non-rush hour periods. We merged eleven-year (2010 - 2020) data from the Fatality Analysis Reporting System. The outcome variable was the county-level fatal crash injury counts. The predictor variables were measures of road types, junction types and work zone, and weather types. We tested the predictiveness of two nested negative binomial models and adjudged that a nested spatial negative binomial regression model outperformed the non-spatial negative binomial model. The median county crash mortality rates at all times of the day and during the rush and non-rush hour periods were 18.4, 7.7, and 10.4 per 100,000 population, respectively. Fatal crash injury rate ratios were significantly elevated on interstates and highways at all times of the day - rush and non-rush hour periods inclusive. Intersections, driveways, and ramps on highways were associated with elevated fatal crash injury rate ratios. Clusters of high fatal crash injury rates were observed in counties located in Montana, Nevada, Colorado, Kansas, New Mexico, Oklahoma, Texas, Arkansas, Mississippi, Alabama, Georgia, and Nevada. The built and natural road environment factors are associated with county-level fatal crash injuries during the rush and non-rush hour periods. Understanding the association of road environment characteristics and the cluster distribution of fatal crash injuries may inform areas in need of focused intervention.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.sste.2022.100562 | DOI Listing |
Cureus
December 2024
Anesthesiology, University of Maryland Medical Center, Baltimore, USA.
J Pediatr Surg
December 2024
Yale New Haven Children's Hospital, Division of Pediatric Surgery, New Haven, CT, USA.
Purpose: Previous research on pediatric motor vehicle collisions (MVC) and fatalities has primarily focused on patient demographics and crash specific information. This study evaluates whether various measures of local infrastructure, including the National Walk Index (NWI), population density, and public school density, or macroeconomic forces, encapsulated in Social Vulnerability Index (SVI) and food area deprivation (PFA) can predict which counties are most at risk for pediatric traffic fatalities.
Methods: Counties with more than 100,000 children in the most recent US census and ≥1 pediatric traffic fatality as identified in the Fatality Analysis Reporting System (FARS) between 2017 and 2021 were included in the study.
Accid Anal Prev
December 2024
Department of Civil Engineering and Management, University of Twente, Enschede 7522 NB, the Netherlands.
In the Netherlands and all over the world, traffic safety problem has been growing particularly for cyclists over the last decades with more people shifting to cycling as a healthy and sustainable mode of transport. Literature shows that age is an important factor in crash involvement and consequences; however, few studies identify the risk factors for cyclists from across different age groups. Therefore, this study aims to identify and understand the effects of traffic, infrastructure, and land use factors on vehicle-to-bike injury and fatal crashes involving cyclists from different age groups.
View Article and Find Full Text PDFJAMA Pediatr
December 2024
Department of Research, American Academy of Pediatrics, Itasca, Illinois.
Importance: Injuries from firearms and motor vehicle crashes (MVCs) are the leading causes of death among US children and youths aged 0 to 19 years. Examining the intersections of age group, sex, race, and ethnicity is essential to focus prevention efforts.
Objective: To examine firearm and motor vehicle fatality rates by population subgroups and analyze changes over time.
Accid Anal Prev
December 2024
Queensland University of Technology, School of Civil and Environmental Engineering, Brisbane, Australia. Electronic address:
Time series analysis plays a vital role in modeling historical crash trends and predicting the possible changes in future crash trends. In existing safety literature, earlier studies employed multiple approaches to model long-term crash risk profiles, such as integer-valued autoregressive Poisson regression model, integer-valued generalized autoregressive conditional heteroscedastic model, and generalized linear autoregressive and moving average models. However, these modeling frameworks often fail to fully capture several key properties of crash count data, especially negative serial correlation, and nonlinear dependence structures across temporal crash counts.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!