2026-06-09T00:09:20Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/70282024-05-17T09:56:54Zcom_10259.4_104com_10259_2604col_10259_6848

Alqatawna, Ali 0898f296-4d19-4bf8-bcff-4a8b4a82b571 600 Rivas Álvarez, Ana 7b1b2be2-6fcf-4687-9bea-88dbc7d3b604 600 Sánchez-Cambronero García-Moreno, Santos e75ab3db-2f8a-4995-8a1d-27b1d3bb9a0e 600 2022-09-22T11:10:26Z 2022-09-22T11:10:26Z 2021-07 978-84-18465-12-3 http://hdl.handle.net/10259/7028 10.36443/10259/7028 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 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. application/pdf eng Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional R-Evolucionando el transporte http://hdl.handle.net/10259/6490 https://doi.org/10.36443/9788418465123 Seguridad vial Tráfico Autopistas Road safety Traffic Highways Ingeniería civil Transportes Civil engineering Transportation Comparison of multivariate regression models and artificial neural networks for prediction highway traffic accidents in Spain: A case study info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/openAccess 3071 3082 THUMBNAIL Alqatawna_CIT2021_3071-3082.pdf.jpg Alqatawna_CIT2021_3071-3082.pdf.jpg IM Thumbnail image/jpeg 4065 https://riubu.ubu.es/bitstream/10259/7028/3/Alqatawna_CIT2021_3071-3082.pdf.jpg afd3a16c28e7ef0605831344fbbe84db MD5 3 LICENSE license.txt license.txt text/plain; charset=utf-8 1362 https://riubu.ubu.es/bitstream/10259/7028/2/license.txt 5d013bfa6e473ff0db22cd82a4d71a70 MD5 2 ORIGINAL Alqatawna_CIT2021_3071-3082.pdf Alqatawna_CIT2021_3071-3082.pdf application/pdf 630016 https://riubu.ubu.es/bitstream/10259/7028/1/Alqatawna_CIT2021_3071-3082.pdf e40e862b8caa709db98b20ca075ff759 MD5 1 10259/7028 oai:riubu.ubu.es:10259/7028 2024-05-17 11:56:54.967 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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