Investigating occupant injury severity of truck-involved crashes based on vehicle types on a mountainous freeway: A hierarchical Bayesian random intercept approach

Haq, Muhammad Tahmidul; Zlatkovic, Milan; Ksaibati, Khaled

doi:10.1016/j.aap.2020.105654

Cited by 44 publications

(34 citation statements)

References 74 publications

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“…It was found that these variables are negatively correlated with crash severity. However, this finding could not be supported by Haq et al [24]. A hierarchical Bayesian random inter-cept approach was established to link collision with fixed object and crash severity.…”

Section: Safety Covariates Of Rural Single-vehicle Crashesmentioning

confidence: 89%

Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

et al. 2021

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Accident analysis and prevention are helpful to ensure the sustainable development of transportation. The aim of this research was to investigate the factors associated with the severity of low-visibility-related rural single-vehicle crashes. Firstly, a latent class clustering model was implemented to partition the whole-dataset into a relatively homogeneous sub-dataset. Then, a spatial random parameters logit model was established for each dataset to capture unobserved heterogeneity and spatial correlation. Analysis was conducted based on the crash data (2014–2019) from 110 two-lane road segments. The results show that the proposed method is a superior crash severity modeling approach to accommodate the unobserved heterogeneity and spatial correlation. Three variables—seatbelt not used, motorcycle, and collision with fixed object—have a stable positive correlation with crash severity. Motorcycle leads to a 12.8%, 23.8%, and 12.6% increase in the risk of serious crashes in the whole-dataset, cluster 3, and cluster 4, respectively. In the whole-dataset, cluster 2, and cluster 3, the risk of serious crashes caused by seatbelt not used increased by 5.5%, 0.1%, and 30.6%, respectively, and caused by collision with fixed object increased by 33.2%, 1.2%, and 13.2%, respectively. The results can provide valuable information for engineers and policy makers to develop targeted measures.

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Section: Safety Covariates Of Rural Single-vehicle Crashesmentioning

confidence: 89%

Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

et al. 2021

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“…Injury severity levels are often condensed to form only two categories, and as a result, binary logit or probit models were used (19). Apart from the traditional logit or probit models, Bayesian statistics are also popular in traffic safety analysis because of their satisfactory performance (20)(21)(22)(23). For this study, binary logit models with a Bayesian inference approach were applied to explore the effects of the vehicle, driver, crash, roadway, and environmental factors contributing to fatalities or any other injuries in commercial truck-related crashes, broken down by various driving actions.…”

Section: Methodsmentioning

confidence: 99%

“…The driver residency was defined as whether a driver is a Wyoming resident or from another state. The crash characteristics included the day of week, time of day, lighting When disaggregate (i.e., driver-level) injury severity crash data are used, it is reasonable to assume that the injury severity levels sustained by the occupants involved in the same vehicle or the same multi-vehicle crash are correlated (7,23). Ignoring such intra-crash and intravehicle correlation in crash data may result in biases in parameter estimates.…”

Section: Data Preparation and Descriptionmentioning

confidence: 99%

Assessment of Commercial Truck Driver Injury Severity as a Result of Driving Actions

Haq

Zlatkovic

Ksaibati

2021

Transportation Research Record

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The disaggregate modeling approach is a new trend in the literature to analyze the injury severity of truck-involved crashes. The assessment of truck driver injury severity based on driver action is still missing in the literature. This paper presents an extensive exploratory analysis that highlights significant variability in the severity of truck drivers’ injuries based on various action types (i.e., aggressive driving, failure to keep proper lane, driving too fast, and no improper driving). Binary logistic regression with the Bayesian random intercept approach was developed to examine the factors contributing to fatal or any injuries of truck drivers using 10 years (2007–2016) of historical crash data in Wyoming. Log-likelihood ratio tests were performed to justify that separate models by various driving action types are warranted. The results demonstrated the effects of various vehicle, driver, crash, and roadway characteristics, combined with truck driver-specific action, on the corresponding severity of driver injury. The gross vehicle weight, age and gender of the driver, time of day, lighting condition, and the presence of junctions were found to have significantly different impacts on the severity of truck driver injury in various driving action-related crashes. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (27%–33%) of intra-crash correlation in driver injury severity within the same crash. Finally, based on the findings of this study, several recommendations are made.

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“…It was found that male drivers were more likely to be involved in fatal or severe crashes than female drivers [17,18]. Subsequently, a hierarchical Bayesian random intercept approach and a random parameter hierarchical ordered probit approach were established and found that female drivers were more likely to be involved in serious crashes than males [19,20]. Further, weather conditions were divided into five categories-sun, rain, snow, fog, and overcast-to capture risk factors.…”

Section: Driver Characteristicsmentioning

confidence: 99%

Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

et al. 2021

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The effect of risk factors on crash severity varies across vehicle types. The objective of this study was to explore the risk factors associated with the severity of rural single-vehicle (SV) crashes. Four vehicle types including passenger car, motorcycle, pickup, and truck were considered. To synthetically accommodate unobserved heterogeneity and spatial correlation in crash data, a novel Bayesian spatial random parameters logit (SRP-logit) model is proposed. Rural SV crash data in Shandong Province were extracted to calibrate the model. Three traditional logit approaches—multinomial logit model, random parameter logit model, and random intercept logit model—were also established and compared with the proposed model. The results indicated that the SRP-logit model exhibits the best fit performance compared with other models, highlighting that simultaneously accommodating unobserved heterogeneity and spatial correlation is a promising modeling approach. Further, there is a significant positive correlation between weekend, dark (without street lighting) conditions, and collision with fixed object and severe crashes and a significant negative correlation between collision with pedestrians and severe crashes. The findings can provide valuable information for policy makers to improve traffic safety performance in rural areas.

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Investigating occupant injury severity of truck-involved crashes based on vehicle types on a mountainous freeway: A hierarchical Bayesian random intercept approach

Cited by 44 publications

References 74 publications

Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

Assessment of Commercial Truck Driver Injury Severity as a Result of Driving Actions

Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

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