Comparing the Future Trend of the Number of Road Accidents in NonMotorized Vehicles Using a Predictive Mathematical Method.

• Autores: Chávez, Joaquín, Castro, Luis, Bravo, Aldo
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/676162
• Enlace del recurso: http://hdl.handle.net/10757/676162
• Nivel de acceso: acceso abierto
• Materia: bicycle; cycle path; forecast; non-motorized; Road safety

The article proposes an innovative approach to address the problem of traffic accidents involving non-motorized vehicles through the application of the predictive mathematical method Gray GM (1,1). The study is based on an analysis of historical accident data, considering variables such as location and characteristics of the road. The methodology used to apply the forecast model is described, highlighting the collection and preparation of data, the selection of relevant variables and the construction of the model. Real data was used to predict accident occurrence and underlying trends. The results of the study demonstrated the effectiveness of the proposed infrastructure model using the mathematical prediction model in non-motorized vehicle traffic accidents. Finally, it is concluded that the use of this predictive mathematical model contributes to the implementation of prevention strategies that would be effective in the future. Likewise, a new perspective could be provided to address road safety of non-motorized vehicles, highlighting the importance of anticipating and preventing accidents through the application of predictive mathematical models, which offers a significant contribution to improving safety. on public roads.