Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers

Yuan, Yalong; Yang, Min; Ye, Guo; Rasouli, Soora; Gan, Zuoxian; Ren, Yifeng

doi:10.1016/j.jsr.2020.12.012

Cited by 43 publications

(30 citation statements)

References 53 publications

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“…Yuan and colleagues [ 24 ], using the Fatality Analysis Reporting System (FARS), analyzed risk factors associated with truck-involved fatal crash severity: among the whole sample of 15,506 truck-drivers, only 1.2% (N = 186) were under drug influence while driving.…”

Section: Resultsmentioning

confidence: 99%

A Systematic Review on the Role of Substance Consumption in Work-Related Road Traffic Crashes Reveals the Importance of Biopsychosocial Factors in Prevention

Frumento

Bufano

Zaccaro

et al. 2022

Behavioral Sciences

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Objective: Since many jobs imply driving, a relevant part of all road traffic crashes (RTC) is related to work. Statistics considering all crashes suggest that they are significantly associated with consumption of substances, but the root causes are not yet clear. The objective of the present paper was to systematically review the scientific literature concerning substances consumption and work-related RTC. We queried the PubMed and Scopus electronic databases according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Articles were included if they reported all necessary data and survived a quality assessment. We selected a final sample of 30 articles from an initial pool of 7113. As hypothesized, taking any of the considered substances was found to increase the risk of work-related RTC. Descriptive statistics on work-related RTC showed a higher average positivity rate for medicines (14.8%) than for alcohol (3.02%) and drugs (0.84%). Interestingly, the impact of some medications found an unconvincing explanation in the mere occurrence of side effects, and it suggests that psychosocial and/or medical conditions could be better predictors of RTC. We therefore propose an intervention and prevention model that also considers biopsychosocial factors, for which further studies are needed in future research.

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Section: Resultsmentioning

confidence: 99%

A Systematic Review on the Role of Substance Consumption in Work-Related Road Traffic Crashes Reveals the Importance of Biopsychosocial Factors in Prevention

Frumento

Bufano

Zaccaro

et al. 2022

Behavioral Sciences

View full text Add to dashboard Cite

show abstract

“…As aforementioned, the PPO model is used for analyzing different pedestrian crash injury severity levels since this model is an effective alternative model for ordinal response data and has the ability to correct for the violated parallel-lines assumption [ 25 , 27 , 55 , 58 ]. The PPO model may be specified in terms of the probability of an injury severity level j for a pedestrian crash i expressed by Equation (2) [ 68 , 69 ].…”

Section: Methodsmentioning

confidence: 99%

“…Studies have developed PPO models to study risk factors affecting crash injury severity concerning at-fault drivers and not at-fault drivers [ 53 ], heavy trucks [ 27 , 54 , 55 ], bicycles [ 56 ], work zones [ 57 ], rural highways [ 58 ], and wrong-way driving [ 59 ], among others. PPO models have also proven to outperform other statistical models when examining the effect of risk factors of pedestrian injuries and fatalities in traffic crashes [ 18 , 24 , 25 , 60 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating the Risk Factors Associated with Injury Severity in Pedestrian Crashes in Santiago, Chile

Rampinelli

Calderón

Blázquez

et al. 2022

IJERPH

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Pedestrians are vulnerable road users that are directly exposed to road traffic crashes with high odds of resulting in serious injuries and fatalities. Therefore, there is a critical need to identify the risk factors associated with injury severity in pedestrian crashes to promote safe and friendly walking environments for pedestrians. This study investigates the risk factors related to pedestrian, crash, and built environment characteristics that contribute to different injury severity levels in pedestrian crashes in Santiago, Chile from a spatial and statistical perspective. First, a GIS kernel density technique was used to identify spatial clusters with high concentrations of pedestrian crash fatalities and severe injuries. Subsequently, partial proportional odds models were developed using the crash dataset for the whole city and the identified spatial clusters to examine and compare the risk factors that significantly affect pedestrian crash injury severity. The model results reveal higher increases in the fatality probability within the spatial clusters for statistically significant contributing factors related to drunk driving, traffic signage disobedience, and imprudence of the pedestrian. The findings may be utilized in the development and implementation of effective public policies and preventive measures to help improve pedestrian safety in Santiago.

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“…Several authors have discussed accident severity analysis during the last decade (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). Statistical models are typically used to address accident severity prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Histogram-Based Gradient Boosting Approach for Accident Severity Prediction With Multisource Data

Kashifi

Ahmad

2022

Transportation Research Record: Journal of the Transportation R

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Many people lose their lives in road accidents because they do not receive timely treatment after the accident from emergency medical services; providing timely emergency services can decrease the fatality rate as well as the severity of accidents. In this study, we predicted the severity of car accidents for use by trauma centers and hospitals for emergency response management. The predictions of our model could be used to decide whether an ambulance unit should be dispatched to the crash site or not. This study used histogram-based gradient boosting (HistGBDT), a modification of the gradient boosting (GBDT) classifier that accelerates the learning process and increases a model’s prediction power. The HistGBDT model was compared with seven state-of-the-art machine learning models: logistic regression, multilayer perceptron, random forest, extremely randomized trees, bagging, AdaBoost, and GBDT. The experiments were conducted on French accident data from 2005 to 2018. The HistGBDT model, with an overall accuracy of 82.5%, recall of 76.7%, and precision of 81.9%, outperformed other models. An analysis of feature importance indicated that safety equipment was the most important feature and vehicle category, department, localization, and region were other significant features. The Fβ measure (i.e., the weighted harmonic mean of recall and precision) was optimized with different weights on recall for the four best performing models to compare the tradeoff between the two crucial performance measures.

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Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers

Cited by 43 publications

References 53 publications

A Systematic Review on the Role of Substance Consumption in Work-Related Road Traffic Crashes Reveals the Importance of Biopsychosocial Factors in Prevention

A Systematic Review on the Role of Substance Consumption in Work-Related Road Traffic Crashes Reveals the Importance of Biopsychosocial Factors in Prevention

Investigating the Risk Factors Associated with Injury Severity in Pedestrian Crashes in Santiago, Chile

Efficient Histogram-Based Gradient Boosting Approach for Accident Severity Prediction With Multisource Data

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