Identification of Factors Leading to High Severity of Crashes in Rural Areas (original) (raw)
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Identification of Countermeasures to Reduce Severity of Rural Highway Crashes
This report presents the details of an investigation aimed at finding potential countermeasures to enhance safety of rural highways by identifying critical factors contributing towards higher severity of crashes. Crash data from KARS (Kansas Accident Reporting System) database was analyzed and crash severity was modeled using several statistical modeling approaches. These approaches comprised of ordered choice (ordered probit and ordered logit) and loglinear models.
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.
Journal of Traffic and Transportation Engineering, Elsevier, 2017
In 2014, 32,675 deaths were recorded in vehicle crashes within the United States. Out of these, 51% of the fatalities occurred in rural highways compared to 49% in urban highways. No specific crash data are available for the built-up areas along rural highways. Due to high fatalities in rural highways, it is important to identify the factors that cause the vehicle crashes. The main objective of this study is to determine the factors associated with severities of crashes that occurred in built-up areas along the rural highways of Nevada. Those factors could aid in making informed decisions while setting up speed zones in these built-up areas. Using descriptive statistics and binary logistic regression model, 337 crashes that occurred in 11 towns along the rural highways from 2002 to 2010 were analyzed. The results showed that more crashes occurred during favorable driving conditions, e.g., 87% crashes on dry roads and 70% crashes in clear weather. The binary logistic regression model showed that crashes occurred from midnight until 4 a.m. were 58.3% likely to be injury crashes rather than property damage only crashes, when other factors were kept at their mean values. Crashes on weekdays were three times more likely to be injury crashes than that occurred on weekends. When other factors were kept at their mean value, crashes involving motorcycles had an 80.2% probability of being injury crashes. Speeding was found to be 17 times more responsible for injury crashes than mechanical defects of the vehicle. As a result of this study, the Nevada Department of Transportation now can take various steps to improve public safety, including steps to reduce speeding and encourage the use of helmets for motorcycle riders.
A comprehensive analysis of factors that influence interstate highway crash severity in Alabama
2021
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.
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.
Journal of Modern Transportation, 2014
This paper presents a current investigation into crash experience along a 15.7-mile rural corridor in southwest Montana with the aim of better understanding crash causal factors along the corridor. The study utilized ten years of crash data, geometric data, and observed freeflow speed data along the corridor. A systematic approach was used where every tenth of a mile was described in term of the crash experience, speed, alignment, and roadside features. Using bivariate and multivariate statistical analyses, the study investigated the crash experience along the corridor as well as some of the underlying relationships which could explain some of the crash causal factors. Results show a strong association between crash rates and horizontal curvatures even for flat curves that can be negotiated at speeds above the posted speed limit, per the highway design equations. Higher crash rates were also found to be associated with the difference between the observed free-flow speeds and the speed dictated by the curve radius or sight distance as per the design equations. Further, results strongly support the safety benefits of guardrails as evidenced by the lower crash rates and severities. The presence of fixed objects and the steepness of side slopes were also found to have an effect on crash rates and severities.
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...
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.
Journal of Society for Transportation and Traffic Studies, 2014
A Run-Off-Road (ROR) crash occurs when a vehicle leaves the travel lane resulting in a collision. ROR crashes have become a major cause of serious injuries and fatalities in the United States. Data from Kansas Crash and Analysis Reporting System database during the period 2007 to 2011 were used in this study to examine ROR crashes. Identification of various characteristics related to environment, roadway, driver, and vehicle as well as factors contributing to rural ROR and urban ROR crashes is important because potential countermeasures can be developed to improve roadside safety. It was found that avoidance/evasive actions; driver being ill, falling asleep or fatigued; or animal at the road are more common on rural roadways than urban roadways, leading to ROR crashes.