Variable categories influencing single-vehicle run-off-road crashes and their severity

Duddu, Venkata R.; Pulugurtha, Srinivas S.; Kukkapalli, Venu Madhav

doi:10.1016/j.treng.2020.100038

Cited by 15 publications

(10 citation statements)

References 6 publications

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“…Further, illumination conditions were divided into five categories to establish a logistic regression. Compared with the daylight condition, the probability of serious collision under dusk, dawn, dark lighted roadway, and dark unlighted roadway conditions increased by 1.57 times, 2.02 times, 1.63 times, and 2.61 times, respectively [32]. This shows that the possibility of serious injury in the early morning is of the most concern, and the provision of street lighting can reduce the occurrence of serious traffic collisions.…”

Section: Safety Covariates Of Rural Single-vehicle Crashesmentioning

confidence: 88%

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: 88%

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

et al. 2021

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“…Drivers are more prone to be fatigued or sleepy in the early morning, a risky behavior that will have a deleterious effect on traffic safety. Both the frequency and severity of crashes show an increased trend [17]. In addition, rural SV crashes occurring between 6:00 p.m. and 12:00 p.m. are unlikely to result in serious injury [29,31].…”

Section: Temporal Characteristicsmentioning

confidence: 99%

“…A partial proportionality odds model and a mixed logit model were established using SV run-off-road crashes to investigate the factors associated with crash severity. 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].…”

Section: Driver Characteristicsmentioning

confidence: 99%

“…Existing studies have shown that there is a negative correlation between adverse environmental conditions and crash severity [17,18,30]. It is believed that the reduction of injury severity is attributable to more careful driving in bad environments.…”

Section: Environmental Characteristicsmentioning

confidence: 99%

“…Some speed limit measures have been considered to reduce crash severity under inclement weather [32][33][34]. Meanwhile, in dark without streetlights conditions, SV crashes are more likely to cause serious injuries [17].…”

Section: Environmental Characteristicsmentioning

confidence: 99%

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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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Intersection Traffic Data Extractor—A Tool for Extracting Traffic Volume and Speed

Prakash,

Karuppanagounder

2024

Lecture Notes in Civil Engineering

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Variable categories influencing single-vehicle run-off-road crashes and their severity

Cited by 15 publications

References 6 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

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

Intersection Traffic Data Extractor—A Tool for Extracting Traffic Volume and Speed

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