Risk factors associated with the increase of injury severity of powered two wheelers road accidents victims in Portugal

Santos, Kenny; Dias, João P.; Amado, Conceição; Sousa, Joana; Francisco, Paulo

doi:10.1080/15389588.2021.1987421

Cited by 10 publications

(13 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with other regions or groups of regions of Portugal, Lisbon was associated with the lowest risk of more severe injuries in the results of the RF_Bal model. A similar result was obtained in Santos et al ( 4 ).…”

Section: Resultssupporting

confidence: 91%

“…The RF_Bal model allows us to conclude that there is a lower risk of a motorcyclist’s fatality when the driver of the other vehicle involved is unharmed after the accident. The same result was observed by Santos et al ( 4 ).…”

Section: Resultssupporting

confidence: 90%

“…Compared with streets, roads where it is legal to ride at a higher speed were associated with higher risks of more severe injuries, as concluded also in Montella et al ( 5 ). Rural areas were associated with more risk of fatalities or severe injuries when compared with urban areas (in the RF_Bal model) which was also concluded by other studies ( 4–6, 11 ). In rural areas, the speed limits are generally higher than in urban areas.…”

Section: Resultssupporting

confidence: 85%

“…In the present study, the above four machine learning algorithms presented as the “best” in Santos et al are going to be compared ( 12 ). Additionally, a classical statistical method, the ordered logistic regression (LR), will be used in this comparison, as in Santos et al, where an LR model was developed to determine risk factors associated with increased injury severity in powered two wheelers’ riders when involved in a road accident ( 4 ). Gradient boosting (GB) and extreme gradient boosting (XGBoost) models are going to be developed and compared too.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Analysis of Motorcycle Accident Injury Severity and Performance Comparison of Machine Learning Algorithms

Santos

Firme

Dias

et al. 2023

Transportation Research Record: Journal of the Transportation R

Self Cite

View full text Add to dashboard Cite

Motorcycle road traffic accidents represent a big concern, as vulnerable road users account for more than half of all road deaths worldwide. The estimation of the influential factors associated with the increase of injury severity of motorcyclists involved in a road accident is of extreme importance as it provides a necessary basis for the development of an appropriate and targeted sustainable prevention plan for improving road safety. This study adopted the decision tree, ordered logistic regression (LR), random forest (RF), gradient boosting, extreme gradient boosting, k-nearest neighbor, and support vector machine methods to predict the injury severity outcome of motorcycle accidents. All the developed models were compared with six different performance metrics. A 10-year (2010–2019) dataset with motorcycle accidents that occurred in Portugal was used for analysis. As usual in traffic accidents datasets, this dataset is class unbalanced which was dealt with by under-sampling. The developed models made it possible to determine the factors associated with the increase of injury severity of motorcyclists involved in road accidents. The interpretation of each factor is based on the Shapley additive explanations values. The RF and LR models (developed with the balanced dataset) outperformed the other models. Risk factors associated with alcohol consumption, road type, road conditions, location, motorcycle age, rider’s gender, and when the accident occurs were estimated. This study provides a suitable framework analysis to build a proper predictive model, allowing researchers and practitioners to evaluate more accurately the risk factors of motorcycle injury severity.

show abstract

Section: Resultssupporting

confidence: 91%

Section: Resultssupporting

confidence: 90%

Section: Resultssupporting

confidence: 85%

mentioning

confidence: 99%

See 2 more Smart Citations

Analysis of Motorcycle Accident Injury Severity and Performance Comparison of Machine Learning Algorithms

Santos

Firme

Dias

et al. 2023

Transportation Research Record: Journal of the Transportation R

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thirdly, the data contains a high level of unobserved heterogeneity, which may affect identifying the exact contributing factors, so the models accounting for heterogeneity in data analysis merit further deep investigation. Finally, the coupling effect of multiple factors on the severity of MME crashes was not focused on in this study, such as the existence of tunnels and bridges (Sun et al 2020), so one of the greatest future challenges is to unpack which combinations of factors produce the greatest risks (Boora et al 2018;Santos et al 2021), such as driver's risk driving behaviour and sharp curves coincided, and traffic flow and weather conditions interacted, etc.…”

Section: Discussionmentioning

confidence: 99%

What leads to severe multi-vehicle crashes on mountainous expressways in Western China?

Wang¹,

Wang²,

Sun³

2022

J. S. Afr. Inst. Civ. Eng.

View full text Add to dashboard Cite

This paper investigates the occurrence and severity of collisions involving multiple vehicles on mountain expressways (MMEs) in Western China. A total of 1 521 crash samples occurring on one typical mountain expressway in Shaanxi, China, between 2012 and 2017, were analysed through a partially constrained generalised ordered logit to identify the significant risk factors contributing to the severity of such crashes. Elasticity analysis was performed to quantify the effects of each independent explanatory variable on the collision severity outcomes. Fourteen total explanatory variables were found to have a significant and pronounced influence on the likelihood of MME crashes. These include the type of collision, the at-fault driver's age, driving while fatigued, cell phone use while driving, alcohol-impaired driving, speeding, risky following and dangerous overtaking behaviour, sharp curves in the roadway and slippery pavement conditions, seasons, day of the week, time of day, and adverse weather (rain/snow/fog). The impacts of the variables on the collision severity were also explored. Taken together, the findings may serve as a useful guide for developing legislation and technical countermeasures to ensure traffic safety on mountain expressways in Western China.

show abstract

Some Determinants for Road Accidents Severity in the District of Setúbal

Infante

Afonso

Jacinto

et al. 2022

Springer Proceedings in Mathematics &Amp; Statistics

View full text Add to dashboard Cite

Risk factors associated with the increase of injury severity of powered two wheelers road accidents victims in Portugal

Cited by 10 publications

References 19 publications

Analysis of Motorcycle Accident Injury Severity and Performance Comparison of Machine Learning Algorithms

Analysis of Motorcycle Accident Injury Severity and Performance Comparison of Machine Learning Algorithms

What leads to severe multi-vehicle crashes on mountainous expressways in Western China?

Some Determinants for Road Accidents Severity in the District of Setúbal

Contact Info

Product

Resources

About