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Background: Road safety is a significant public health problem because it causes negative consequences on victims and families. The objective was to analyze the most significant changes in traffic crashes in Ecuador during the period from 2000 to 2019. With data obtained from the National Institute of Statistics and Census, we performed the analysis to identify: the number of traffic crashes, the number of victims, and other study variables. Methods: Descriptive and analytical statistics and the contrast of proportions were used to analyze data from 2000 to 2019. Results: According to the ideal joinpoint analysis model, there was a significant decrease in the number of recorded traffic accidents from 2015 to 2019 of −8.54 per year, while the tendency to die increased in females (2.05 per year) and males (3.29 per year). The most common crash was a collision, and the automobile appeared as the most involved vehicle from 2015 to 2019. The hypothesis test contrast is used to determine if statistically significant differences exist between age groups by gender of the driver injured in the period 2017–2018. Conclusions: This study determines the most significant changes in the variables related to traffic crashes, where mortality due to this cause in the last four years has had a growth rate of 1.8% compared to collisions that presented a rate of −31.12%. The contrast of the hypothesis test shows significant differences in the injury level between males and female drivers, depending on the age group.
Fabricio Espinoza-Molina; Christian Ojeda-Romero; Henry Zumba-Paucar; Giovanny Pillajo-Quijia; Blanca Arenas-Ramírez; Francisco Aparicio-Izquierdo. Road Safety as a Public Health Problem: Case of Ecuador in the Period 2000–2019. Sustainability 2021, 13, 8033 .
AMA StyleFabricio Espinoza-Molina, Christian Ojeda-Romero, Henry Zumba-Paucar, Giovanny Pillajo-Quijia, Blanca Arenas-Ramírez, Francisco Aparicio-Izquierdo. Road Safety as a Public Health Problem: Case of Ecuador in the Period 2000–2019. Sustainability. 2021; 13 (14):8033.
Chicago/Turabian StyleFabricio Espinoza-Molina; Christian Ojeda-Romero; Henry Zumba-Paucar; Giovanny Pillajo-Quijia; Blanca Arenas-Ramírez; Francisco Aparicio-Izquierdo. 2021. "Road Safety as a Public Health Problem: Case of Ecuador in the Period 2000–2019." Sustainability 13, no. 14: 8033.
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.
Giovanny Pillajo-Quijia; Blanca Arenas-Ramírez; Camino González-Fernández; Francisco Aparicio-Izquierdo. Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods. Sustainability 2020, 12, 1324 .
AMA StyleGiovanny Pillajo-Quijia, Blanca Arenas-Ramírez, Camino González-Fernández, Francisco Aparicio-Izquierdo. Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods. Sustainability. 2020; 12 (4):1324.
Chicago/Turabian StyleGiovanny Pillajo-Quijia; Blanca Arenas-Ramírez; Camino González-Fernández; Francisco Aparicio-Izquierdo. 2020. "Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods." Sustainability 12, no. 4: 1324.