A comprehensive analysis of factors that influence interstate highway crash severity in Alabama (original) (raw)
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Transportation Research Record, 1994
A statistical model was developed to explain the relationship between types of crashes and injuries sustained in motor vehicle accidents. By using techniques of categorical data analysis and comprehensive data on crashes in Hawaii during 1990, a model was built to relate the type of crash (e.g., rollover, head-on, sideswipe, rear-end, etc.) to a KABCO injury scale. An "odds multiplier" was developed that enabled comparison according to crash type of the odds of particular levels of injury relative to noninjury. The effects of seat belt use on injury level also were examined, and interactions among belt use, crash type, and injury level were considered. Differences between crash types and the effectiveness of seat belts are discussed along with how log-linear analysis, logit modeling, and estimation of "odds multipliers" may contribute to traffic safety research. Some implications of the findings for appropriate interventions and future research are presented in a...
International Journal of Injury Control and Safety Promotion, 2019
This study identifies and compares those risk factors affecting crash injuries and fatalities on rural freeways in Montana and West Virginia in the United States using the mixed logit model. Three-year crashes on rural freeway segments in both states are used. Higher annual average daily traffic (AADT) was associated with a reduction in injuries/fatalities in both states, with higher reduction in West Virginia (40%) than in Montana (25%). In both states, the impact of adverse road surface conditions (i.e., snowy/icy) was associated with a reduction in injuries/fatalities. The results show that separate injury severity models for individual states are suggested instead of lumping all crashes in one model. Enforcement of trucks' risky maneuvers (e.g., illegal traveling in the leftmost lane) and more education for older drivers are suggested in West Virginia. In Montana, it is recommended to monitor rural freeway segments with high sport utility vehicle (SUV) crash history.
Statistical analysis of accident severity on rural freeways
Accident Analysis & Prevention, 1996
The growing concern about the possible safety-related impacts of Intelligent Transportation Systems (ITS) has focused attention on the need to develop new statistical approaches to predict accident severity. This paper presents a nested logit formulation as a means for determining accident severity given that an accident has occurred. Four levels of severity are considered: (1) property damage only, (2) possible injury, (3) evident injury, and (4) disabling injury or fatality. Using 5-year accident data from a 61 km section of rural interstate in Washington State (which has been selected as an ITS demonstration site), we estimate a nested logit model of accident severity. The estimation results provide valuable evidence on the effect that environmental conditions, highway design, accident type, driver characteristics and vehicle attributes have on accident severity. Our findings show that the nested logit formulation is a promising approach to evaluate the impact that ITS or other safety-related countermeasures may have on accident severities.
Modeling Injury Outcomes of Crashes Involving Heavy Vehicles in Rural and Urban Settings in Texas
2014
Concern related to crashes involving large trucks in rural and urban settings has increased due to the potential level of sustained injuries and associated socioeconomic impacts. However, detailed studies on crashes involving large trucks in rural and urban areas have not been conducted for the Texas interstate system. This study analyzed the factors contributing to injury severity by utilizing Texas crash data, based on discrete outcome models, which account for possible unobserved effects or heterogeneity related to drivers, vehicles, and roadway environment. The study estimated random parameter logit (i.e., mixed logit) models specific to rural and urban areas separately to predict the likelihood of five severity levels used in the Crash Records Information System (CRIS) in Texas—fatality, incapacitating, non-incapacitating, possible, and no-injury or property-damage-only. The estimated models indicate that a complex interaction of factors lead to injury severities with a flexibi...
Evaluating adverse rural crash outcomes using the NHTSA State Data
The population-based rate of motor vehicle crash mortality is consistently higher in rural locations, but it is unclear how much of this disparity might be due to geographic barriers or deficiencies in emergency medical services (EMS). We sought to analyze separately factors associated with the occurrence of a severe injury and those associated with death after injury had occurred. Methods: Data from all police-reported crashes in 11 states from 2005-2007 were obtained through the National Highway Traffic Safety Administration (NHTSA) State Data System (SDS). Logistic regression was used to estimate factors associated with (1) death; (2) severe (incapacitating or fatal) injury; and (3) death given severe injury. Models included covariates related to the person, vehicle, and event; county location was specified using Rural-Urban Continuum Codes (RUCC). Results: Older age, not wearing a belt, ejection, alcohol involvement, high speed, and early morning times were associated with increased risk of both severe injury and death. Controlling for these factors, and restricting analysis to persons who had suffered a severe injury, the adjusted odds ratio (aOR) associated with death was higher for counties classified rural (RUCC 6-7, aOR 1.23, 95% CI 1.16-1.31) or very rural (RUCC 8-9, aOR 1.31, 95% CI 1.18-1.46). Conclusions: Persons severely injured in crashes are more likely to die if they are in rural locations, possibly due to EMS constraints. As NHTSA-SDS data become more available and more uniform, they may be useful to explore specific factors contributing to this increased risk.
Effects of Demographic and Driver Factors on Single-Vehicle and Multivehicle Fatal Crashes
Transportation Research Record: Journal of the Transportation Research Board, 2015
Human error is often considered the leading cause of motor vehicle crashes. Although some research has been conducted to assess the influence of human factors, full driver impacts on crashes are rarely analyzed, especially on a large scale in the United States. This study sought to identify the driver behavior and demographic factors that affected the likelihood of a multivehicle or single-vehicle fatal crash. A multinomial logistic regression framework, including odds ratios, was used to analyze the variables from several states. A tiered model approach was adopted to find the variable effects for combined, urban, rural, undivided urban, divided urban, undivided rural, and divided rural data sets. Each model produced different significant demographic or driver variables, many being unique or contradictory to the expected results of other research. Gender, often seen as a major contribution to crash outcome, was significant only for the full and urban models and likely not an import...
Identification of Factors Leading to High Severity of Crashes in Rural Areas
Journal of the Transportation Research Forum, 2010
This study made an effort to identify critical factors contributing to increased crash severities on rural highways. Crash data from the Kansas Accident Reporting System (KARS) database was analyzed and crash severity was modeled using ordered choice models. Many driver-related factors, such as alcohol involvement, lack of seat belt usage, excessive speed, and driver ejections because of the crash contribute to the increased severity of crashes in rural areas. Also, severities of singlevehicle crashes are higher than two-vehicle and animal-vehicle crashes. Factors related to roadway geometry such as sharp curves and steep grades are also found to contribute to the increased crash severity in rural areas.
Accident Analysis & Prevention, 2006
Introduction: Studies have demonstrated that the fatality risk for motor vehicle crashes (MVCs) is higher in rural than urban areas. The purpose of this study was to quantify the risk of a fatal outcome associated with a crash by the urban/rural classification of the driver's county of residence and the county of crash before and after adjusting for potentially confounding factors. Methods: County of crash and driver's county of residence were classified as urban or rural for 514,648 Utah crash participants. Multivariate regression analysis was used to assess the impact of rural versus urban crash location on fatality outcomes for both urban and rural drivers. Results: Before adjusting for confounding factors the relative risk of fatality in a rural versus urban crash was 9.7 (95% CI: 8.0-11.7) for urban drivers and their passengers compared to 1.8 (95% CI: 1.3-2.6) for rural residents. Adjustment for behavioral, road, and crash characteristics reduced risk estimates to 2.8 (95% CI: 2.2-3.5) and 1.2 (95% CI: 0.8-1.7), respectively. Conclusion: Urban and rural drivers may have distinct risk factors for MVC fatality in rural areas. Interventions to reduce the risk of fatality in rural areas should evaluate the needs of both urban and rural drivers.