Investigation of driver injury severities in rural single-vehicle crashes under rain conditions using mixed logit and latent class models

Li, Zhenning; Ci, Yusheng; Chen, Cong; Zhang, Guohui; Wu, Qiong; Qian, Sean; Prevedouros, Panos D.; David, Thomas J.

doi:10.1016/j.aap.2018.12.020

Cited by 90 publications

(34 citation statements)

References 62 publications

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“…Regarding the first type, there are regression model studies with bi-variable responses using the Binary Logit Model (BLM) [19], Logistic regression methods (LRM) [20], or the Ordered Probit Model (OPM) [21]. Other authors have developed Multinomial Logit Models (MLMs) [22][23][24][25] and Dynamic Macroeconomic models [13]. Among the non-traditional methods with two or multiple answer variables, there are the Artificial Neuronal Network (ANN) [26] and Classification And Regression Trees (CARTs) [27,28].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Li et al [25] studied driver injury severity in single-vehicle collisions with road characteristics in rural areas (straight and curved locations, slopes, signals, and lane numbers) and risky driver behavior due to alcohol and drug consumption and the non-use of seatbelts. The severity is higher if both conditions are present at the moment of the crash.…”

Section: Literature Reviewmentioning

confidence: 99%

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Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

et al. 2020

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The study of road accidents and the adoption of measures to reduce them is one of the most important targets of the Sustainable Development Goals for 2030. To further progress in the improvement of road safety, it is necessary to focus studies on specific groups, such as light trucks and vans. Since 2013 in Spain, there has been an upturn in accidents in these two categories of vehicles and a renewed interest to deepen our understanding of the causes that encourage this behavior. This paper focuses on using machine learning methods to explain driver-injury severity in run-off-roadway and rollover types of accidents. A Random Forest (RF)-classification tree (CART) approach is used to select the relevant categorical variables (driver, vehicle, infrastructure, and environmental factors) to obtain models that classify, explain, and predict the severity of such accidents with good accuracy. A support vector machine and binomial logit models were applied in order to contrast the variable importance ranking and the performance analysis, and the results are convergent with the RF+CART approach (more than 70% accuracy). The resulting models highlight the importance of using safety belts, as well as psychophysical conditions (alcohol, drugs, or sleep deprivation) and injury localization for the two accident types.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

et al. 2020

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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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“…As the estimated coefficients may not always directly represents the effects that a contributory factor has on the indicator, an elasticity analysis is necessary for quantifying the effect of each independent variable based on the observed and estimated information [ 28 , 35 , 36 ]. The elasticity for a continuous independent variable

on the probability of a severe crash is calculated from the partial derivative of each observations [ 28 ]:

where the

is the elasticity outcome for continuous variable

of crash observation

.…”

Section: Methodsmentioning

confidence: 99%

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

Meng

Song

et al. 2020

IJERPH

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A consecutive crash series is composed by a primary crash and one or more subsequent secondary crashes that occur immediately within a certain distance. The crash mechanism of a consecutive crash series is distinctive, as it is different from common primary and secondary crashes mainly caused by queuing effects and chain-reaction crashes that involve multiple collisions in one crash. It commonly affects a large area of road space and possibly causes congestions and significant delays in evacuation and clearance. This study identified the influential factors determining the severity of primary and secondary crashes in a consecutive crash series. Basic, random-effects, random-parameters, and two-level binary logistic regression models were established based on crash data collected on the freeway network of Guizhou Province, China in 2018, of which 349 were identified as consecutive crashes. According to the model performance metrics, the two-level logistic model outperformed the other three models. On the crash level, double-vehicle primary crash had a negative association with the severity of secondary consecutive crashes, and the involvement of trucks in the secondary consecutive crash had a positive contribution to its crash severity. On a road segment level, speed limit, traffic volume, tunnel, and extreme weather conditions such as rainy and cloudy days had positive effects on consecutive crash severity, while the number of lanes was negatively associated with consecutive crash severity. Policy suggestions are made to alleviate the severity of consecutive crashes by reminding the drivers with real-time potential hazards of severe consecutive crashes and providing educative programs to specific groups of drivers.

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Investigation of driver injury severities in rural single-vehicle crashes under rain conditions using mixed logit and latent class models

Cited by 90 publications

References 62 publications

Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

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

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

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