Injury severity analysis of familiar drivers and unfamiliar drivers in single-vehicle crashes on the mountainous highways

Wen, Huiying; Xue, Gang

doi:10.1016/j.aap.2020.105667

Cited by 27 publications

(20 citation statements)

References 87 publications

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“…Numerous studies have shown that driver-related characteristics have a significant influence on the severity of rural SV crashes, including lack of seat belt use, drunk driving, speeding, fatigue, driver age, and driver gender. For example, the probability of fatal crashes in rural areas due to seat belts not being used and drunk driving will increase 15.3% and 36.3%, respectively [12]. It was also suggested that fatigue is associated with severe rural SV crashes and that increasing rest breaks could reduce crash risk [13,14].…”

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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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 impact of distracted driving on crash risk was analyzed, and a positive correlation between distracted driving and crash risk was found [20]. Further, the importance of using seatbelts and maintaining a safe speed has been widely emphasized, as they can effectively prevent drivers from being involved in fatal crashes [14,21]. A latent class logit function was established, and three risk variablesseatbelt not used, driving under the influence of alcohol, and fatigue driving-are expected to result in a significant increase in the probability of severe crashes [22].…”

Section: Safety Covariates Of Rural Single-vehicle Crashesmentioning

confidence: 99%

“…Furthermore, drivers are fatigued after a day's work and may not be able to avoid a crash under such conditions. This phenomenon is particularly significant for drivers who are not familiar with the local traffic environment [14].…”

Section: Other Characteristicsmentioning

confidence: 99%

“…In the existing research on rural SV crashes, low visibility has been exhibited as a variable of weather factors; there is a lack of separate research on rural SV crashes under such conditions. For example, Wen et al [14] investigated the risk factors of SV crash severity in mountainous areas and suggested that compared with sunny weather, the probability of serious and fatal crashes in foggy weather increased by 21.2% and 15.7%, respectively. Visibility conditions were divided into four categories-greater than 200 m, 100 -200 m, 50 -100 m, and less than 50 m-to evaluate the safety performance of rural areas.…”

Section: Introductionmentioning

confidence: 99%

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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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“…In the existing literature, foggy and clear weather are usually analyzed and modeled as categorical variables of weather factors. For example, Wen and Xue [10] classified weather factors into four variables-sunny, rainfall, snowfall, and foggy. e study found that compared with sunny weather, the probability of fatal accidents increased by 3.4%, 3.3%, and 15.7%, respectively, for rainfall, snowfall, and fog.…”

Section: Introductionmentioning

confidence: 99%

Exploring Driver Injury Severity in Single-Vehicle Crashes under Foggy Weather and Clear Weather

Fan

Cai

Liu

et al. 2021

Journal of Advanced Transportation

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The purpose of this study is to investigate and compare the significant influencing factors of driver injury severity in single-vehicle (SV) crashes under foggy and clear weather conditions. Based on data for SV crashes in Shandong Province, China, the mixed logit model (MLM) was employed to interpret driver injury severity for SV crashes in clear and foggy weather. The results showed that there are significant differences in the influencing factors of the severity of SV crashes in foggy and clear weather. Specifically, 15 factors are significantly associated with the severity of SV crashes in clear weather, and 18 factors are significantly associated with the severity of SV crashes in foggy weather. In addition, young drivers (age < 30), non-dry road surfaces, and signal control significantly influence the severity of foggy weather crashes but not clear weather crashes. Self-employment and weekends have significant effects on the severity of crashes only in clear weather. Interestingly, drivers whose occupation is farming showed opposite trends in the effect of crash severity in foggy and clear weather. Based on the findings of this research, some potential countermeasures can be adopted to reduce crash severity in foggy and clear weather.

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Injury severity analysis of familiar drivers and unfamiliar drivers in single-vehicle crashes on the mountainous highways

Cited by 27 publications

References 87 publications

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

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

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

Exploring Driver Injury Severity in Single-Vehicle Crashes under Foggy Weather and Clear Weather

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