Ability of youth operators to reach agricultural all-terrain vehicles controls

Araújo, Guilherme De Moura; Kouhanestani, Farzaneh Khorsandi; Fathallah, Fadi A.

doi:10.1016/j.jsr.2022.11.010

Cited by 1 publication

(1 citation statement)

References 45 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Schools could also provide resources and information to parents to ensure that they are aware of the risks associated with ATV use and the steps they can take to protect their children. Lastly, youth are particularly vulnerable to ATV-related incidents due to their physical, cognitive, and emotional immaturity [40,[46][47][48]. To address this issue, implementing stricter laws and regulations around the use of ATVs by minors could help mitigate the risk of ATV-related incidents.…”

Section: Insights and Recommendations For Developing Effective Safety...mentioning

confidence: 99%

Riding into Danger: Predictive Modeling for ATV-Related Injuries and Seasonal Patterns

Ferreira Lima dos Santos,

Khorsandi

2024

Forecasting

View full text Add to dashboard Cite

All-Terrain Vehicles (ATVs) are popular off-road vehicles in the United States, with a staggering 10.5 million households reported to own at least one ATV. Despite their popularity, ATVs pose a significant risk of severe injuries, leading to substantial healthcare expenses and raising public health concerns. As such, gaining insights into the patterns of ATV-related hospitalizations and accurately predicting these injuries is of paramount importance. This knowledge can guide the development of effective prevention strategies, ultimately mitigating ATV-related injuries and the associated healthcare costs. Therefore, we performed an in-depth analysis of ATV-related hospitalizations from 2010 to 2021. Furthermore, we developed and assessed the performance of three forecasting models—Neural Prophet, SARIMA, and LSTM—to predict ATV-related injuries. The performance of these models was evaluated using the Root Mean Square Error (RMSE) accuracy metric. As a result, the LSTM model outperformed the others and could be used to provide valuable insights that can aid in strategic planning and resource allocation within healthcare systems. In addition, our findings highlight the urgent need for prevention programs that are specifically targeted toward youth and timed for the summer season.

show abstract

Section: Insights and Recommendations For Developing Effective Safety...mentioning

confidence: 99%