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    • Congreso de Ingeniería del Transporte CIT 2021 (14º. 2021. Burgos)
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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/7028

    Título
    Comparison of multivariate regression models and artificial neural networks for prediction highway traffic accidents in Spain: A case study
    Autor
    Alqatawna, Ali
    Rivas Álvarez, Ana
    Sánchez-Cambronero García-Moreno, Santos
    Publicado en
    R-Evolucionando el transporte
    Editorial
    Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional
    Fecha de publicación
    2021-07
    ISBN
    978-84-18465-12-3
    DOI
    10.36443/10259/7028
    Descripción
    Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgos
    Resumen
    In recent years Spain shows the great reduction in the accident rate that has been achieved and the improvement of the behavior of road users, despite this, there is still a need to improve many areas. In 2016 for the first time since the last 13 years, the number of fatalities increased by 7% concerning to the previous year. In this paper, analysis and prediction of road traffic accidents (RTAs) of high accident locations highways in Spain, were undertaken using Artificial Neural Networks (ANNs), which can be used for policymakers, this paper contributes to the area of transportation safety and researchers. ANN is a powerful technique that has demonstrated considerable success in analyzing historical data to forecast future trends. There are many ANN models for predicting the number of accidents on highways that were developed using 4 years of data for accident counts on the Spain freeway roads from 2014 to 2017. The best ANN model was selected for this task and the model variables involved highway sections, years, section length ,annual average daily traffic (AADT), the average horizontal curve radius, Slope gradient, traffic accidents with the number of heavy vehicles. In the ANN model development, the sigmoid activation function was employed with the Levenberg-Marquardt algorithm and the different number of neurons. The model results indicate the estimated traffic accidents, based on appropriate data are close enough to actual traffic accidents and so are dependable to forecast traffic accidents in Spain. However, it demonstrates that ANNs provide a potentially powerful tool in analyzing and predicting traffic accidents. The performance of the model was in comparison to the multivariate regression model developed for the same purpose. The results prove that the ANN model stronger forecasted model which produced estimates fairly close to forecast future highway traffic accidents with Spanish conditions.
    Palabras clave
    Seguridad vial
    Road safety
    Tráfico
    Traffic
    Autopistas
    Highways
    Materia
    Ingeniería civil
    Civil engineering
    Transportes
    Transportation
    URI
    http://hdl.handle.net/10259/7028
    Versión del editor
    https://doi.org/10.36443/9788418465123
    Relacionado con
    http://hdl.handle.net/10259/6490
    Aparece en las colecciones
    • Congreso de Ingeniería del Transporte CIT 2021 (14º. 2021. Burgos)
    Ficheros en este ítem
    Nombre:
    Alqatawna_CIT2021_3071-3082.pdf
    Tamaño:
    615.2Kb
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