Citas bibligráficas
Bazan, L., (2025). Método para predecir la probabilidad de siniestros viales en base a Crash Modification Factor en el tramo de Lima – La Oroya del km 109 al 112 de la Carretera Central [Trabajo de Suficiencia Profesional, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/683847
Bazan, L., Método para predecir la probabilidad de siniestros viales en base a Crash Modification Factor en el tramo de Lima – La Oroya del km 109 al 112 de la Carretera Central [Trabajo de Suficiencia Profesional]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2025. http://hdl.handle.net/10757/683847
@misc{renati/1300850,
title = "Método para predecir la probabilidad de siniestros viales en base a Crash Modification Factor en el tramo de Lima – La Oroya del km 109 al 112 de la Carretera Central",
author = "Bazan Alvarez, Leslie Karla",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2025"
}
The purpose of the research work presented is to develop and obtain a predictive model of road accidents on the central highway from km 109 to 112 of the Lima - La Oroya section, considering the high number of road accidents recorded in the aforementioned section. The predictive model uses Crash Modification Factors (CMF) according to the characteristics and road attributes of the section, as well as SRSS values that measure road risk based on a simulation carried out in IRAP software. The record of road accidents between 2020 and 2021 in the mentioned section is quite high with 48 records, as a result of these numbers we began to analyze the characteristics of the road to obtain the CMF and SRSS values With the values obtained a linear regression is performed in Excel software, where the dependent variable is the SRSS values and the independent variables are the CMF, The values given by this model have to be compared and validated with those of the simulation (SRSS) for which the statistical expression GEH is used, this expression has to be less than 5 to give reliability and validity to the model generated, for this work all values were less than 5 validating the model made. Finally, the final and validated predictive model was obtained, which can be used to implement improvements in road attributes and obtain lower road risks than those currently in place in order to improve road safety and reduce the number of accidents that have occurred in recent years.
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