Ordered logistic models of influencing factors on crash injury severity of single and multiple-vehicle downgrade crashes: A case study in Wyoming

Rezapour, Mahdi; Moomen, Milhan; Ksaibati, Khaled

doi:10.1016/j.jsr.2018.12.006

Cited by 80 publications

(53 citation statements)

References 42 publications

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“…This study seeks to develop models to analyze risk factors and the characteristics of MV crashes on expressways. Since vehicles have different features, which influences the resulting type and number of vehicles involved in crashes [30,31], we developed separate models for bus, passenger car, and freight truck-involved crashes. The advantage of this is that it helps us distinguish the effects of MV crash features for each type of vehicles in detail.…”

Section: Literature Reviewsmentioning

confidence: 99%

A Comprehensive Analysis of Multi-Vehicle Crashes on Expressways: A Double Hurdle Approach

2019

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To maintain safe expressways, it is necessary to investigate the causes of severe traffic accidents and establish a strategy. This study aims to analyze crashes and identify the influence of crash-risk factors on multi-vehicle (MV) crashes. Crashes involving three types of vehicles namely passenger cars, buses, and freight trucks were analyzed using a seven-year data spanning 2011 to 2017 which consists of crashes that occurred on expressways in South Korea. We applied a double hurdle approach in which a model consists of two estimators: The first estimation, which is a binary logit model selects MV crashes from the dataset; and the second estimation which is a truncated regression model estimates the number of vehicles involved in the MV crash. We found that driver traffic violations such as the improper distance between vehicles, reversing and passing increases the probability of MV crashes occurring. MV crashes in tunnels and mainlines were found to be positively correlated with the number of vehicles involved in the crash, whereas fewer vehicles were involved in MV crashes at ramps and toll-booths. Further, we found that the hurdle model with an exponential form of conditional mean of the latent variable provides better estimation parameters.

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Section: Literature Reviewsmentioning

confidence: 99%

A Comprehensive Analysis of Multi-Vehicle Crashes on Expressways: A Double Hurdle Approach

2019

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“…The dependent variable (outcome) in most incident-related analyses is binary. Considering the binary classification of most injury outcomes, logistic regression (LR) is widely used in incident analysis research due to its capability in estimating and quantifying meaningful results in terms of odds ratio values [20]. Studies in occupational safety use LR to evaluate the relationship between the injury and predictor variables to make a meaningful interpretation about the measure of such effects (using odds ratios) and to identify statistically significant predictors of injury (using p-values).…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al [22] applied a hierarchical LR model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Rezapour et al [20] applied LR for classification and prediction of occupational injury risk in the shipbuilding industry and proved its efficiency in estimating risk probability from working conditions predictors of incident type, situation, and condition involved in the incident.…”

Section: Introductionmentioning

confidence: 99%

Use of Logistic Regression to Identify Factors Influencing the Post-Incident State of Occupational Injuries in Agribusiness Operations

2019

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Agribusiness industries are among the most hazardous workplaces for non-fatal occupational injuries. The term “post-incident state” is used to describe the health status of an injured person when a non-fatal occupational injury has occurred, in the post-incident period when the worker returns to work, either immediately with zero days away from work (medical state) or after a disability period (disability state). An analysis of nearly 14,000 occupational incidents in agribusiness operations allowed for the classification of the post-incident state as medical or disability (77% and 23% of the cases, respectively). Due to substantial impacts of occupational incidents on labor-market outcomes, identifying factors that influence the severity of such incidents plays a significant role in improving workplace safety, protecting workers, and reducing costs of the post-incident state of an injury. In addition, the average costs of a disability state are significantly higher than those of a medical state. Therefore, this study aimed to identify the contributory factors to such post-incident states with logistic regression using information from workers’ compensation claims recorded between 2008 and 2016 in the Midwest region of the United States. The logistic regression equation was derived to calculate the odds of disability post-incident state. Results indicated that factors influencing the post-incident state included the injured body parts, injury nature, and worker’s age, experience, and occupation, as well as the industry, and were statistically significant predictors of post-incident states. Specific incidents predicting disability outcomes included being caught in/between/under, fall/slip/trip injury, and strain/injury by. The methodology and estimation results provide insightful understanding of the factors influencing medical/disability injuries, in addition to beneficial references for developing effective countermeasures for prevention of occupational incidents.

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“…This is especially important for data with small sample sizes that may not adequately represent population characteristics [13]. More details about the ordered logistic model and Bayesian inference can be referred to [4,[11][12][13].…”

Section: Methodsmentioning

confidence: 99%

“…At the lower level, the influencing factors included vehicle speed, vehicle age, road alignment, surface status, road class and traffic light installation, while at the upper level, pavement, emergent medical environment, traffic rate of compliance, and ratio of elderly in the community were significant. The latest study by Rezapour et al [11] selected ordered logistic models on crash injury severities of downgrade crashes. The findings provided insights into contributing factors of downgrade crashes in mountainous areas.…”

Section: Introductionmentioning

confidence: 99%

Investigation of injury severity in urban expressway crashes: A case study from Beijing

Yuan

Zhao

et al. 2020

PLoS ONE

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Urban expressway is the main artery of traffic network, and an in-depth analysis of the crashes is crucial for improving the traffic safety level of expressways. This study intended to address the injury severity of expressways in Beijing by proposing Bayesian ordered logistic regression model. Crash data were collected from urban express rings and expressways in 2015 and 2016. The results showed that crash location, time and crash season are significant variables influencing injury severity. The findings revealed that the proposed model can address the ordinal feature of injury severity, while accommodating the data with small sample sizes that may not adequately represent population characteristics. The conclusions can provide the management departments with valuable suggestions for the injury prevention and safety improvement on the urban expressways.

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Ordered logistic models of influencing factors on crash injury severity of single and multiple-vehicle downgrade crashes: A case study in Wyoming

Cited by 80 publications

References 42 publications

A Comprehensive Analysis of Multi-Vehicle Crashes on Expressways: A Double Hurdle Approach

A Comprehensive Analysis of Multi-Vehicle Crashes on Expressways: A Double Hurdle Approach

Use of Logistic Regression to Identify Factors Influencing the Post-Incident State of Occupational Injuries in Agribusiness Operations

Investigation of injury severity in urban expressway crashes: A case study from Beijing

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