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

Fernando Ferreira Lima dos Santos, Farzaneh Khorsandi () and Guilherme De Moura Araujo
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Fernando Ferreira Lima dos Santos: Agricultural Health and Safety Laboratory, Department of Agricultural and Biological Engineering, University of California, Davis, One Shields Ave, Davis, CA 95616, USA
Farzaneh Khorsandi: Agricultural Health and Safety Laboratory, Department of Agricultural and Biological Engineering, University of California, Davis, One Shields Ave, Davis, CA 95616, USA
Guilherme De Moura Araujo: Agricultural Health and Safety Laboratory, Department of Agricultural and Biological Engineering, University of California, Davis, One Shields Ave, Davis, CA 95616, USA

Forecasting, 2024, vol. 6, issue 2, 1-13

Abstract: 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.

Keywords: quad bikes; machine learning; safety; forecast; seasonality (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2024
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