2024-03-29T11:53:27Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/70282022-10-05T12:40:36Zcom_10259.4_104com_10259_2604col_10259_6848
Alqatawna, Ali
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Rivas Álvarez, Ana
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Sánchez-Cambronero García-Moreno, Santos
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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
Transporte
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
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Alqatawna_CIT2021_3071-3082.pdf.jpg
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https://riubu.ubu.es/bitstream/10259/7028/2/license.txt
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Alqatawna_CIT2021_3071-3082.pdf
Alqatawna_CIT2021_3071-3082.pdf
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https://riubu.ubu.es/bitstream/10259/7028/1/Alqatawna_CIT2021_3071-3082.pdf
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10259/7028
oai:riubu.ubu.es:10259/7028
2022-10-05 14:40:36.071
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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