An integrated methodology for the scientific research of road accidents. General overview

Aparicio, Francisco; Arenas, Blanca

doi:10.1007/s12615-016-9095-9

Cited by 1 publication

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Spain, the national project of van accidents Furgoseg [10] carried out several research and development activities on the methods and tools used for statistical analysis, testing and experimentation, simulation, and calculation in the framework of "Integrated Accident Investigation Methodology (MIICA)" [11]. A time series analysis was done by Dadashova et al [13] to study the frequency and severity of van accidents: a linear regression with variables transformed from Box-Cox and their autoregressive errors (DRAG, Demand for Road use, Accidents and their Gravity), as well as the Unobserved Components Model (UCM).…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

et al. 2020

View full text Add to dashboard Cite

The study of road accidents and the adoption of measures to reduce them is one of the most important targets of the Sustainable Development Goals for 2030. To further progress in the improvement of road safety, it is necessary to focus studies on specific groups, such as light trucks and vans. Since 2013 in Spain, there has been an upturn in accidents in these two categories of vehicles and a renewed interest to deepen our understanding of the causes that encourage this behavior. This paper focuses on using machine learning methods to explain driver-injury severity in run-off-roadway and rollover types of accidents. A Random Forest (RF)-classification tree (CART) approach is used to select the relevant categorical variables (driver, vehicle, infrastructure, and environmental factors) to obtain models that classify, explain, and predict the severity of such accidents with good accuracy. A support vector machine and binomial logit models were applied in order to contrast the variable importance ranking and the performance analysis, and the results are convergent with the RF+CART approach (more than 70% accuracy). The resulting models highlight the importance of using safety belts, as well as psychophysical conditions (alcohol, drugs, or sleep deprivation) and injury localization for the two accident types.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…These vehicles include G1 Pick-up, G2 Chassis-cabin truck, G3 Van and combi, and G4 Passenger car-derived vehicles. This classification was defined in the Furgoseg Project [10,11] (see the definition in references [12,13]).…”

Section: Introductionmentioning

confidence: 99%