Using a separable convolutional neural network for large-scale transportation network speed prediction

F. A. Loaiza, Jose Alfredo Herrera Quispe, Luis Mantilla Sc

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

This paper proposes the reduction of the convergence time on a Convolutional Neural Network (CNN) method for traffic speed prediction, without reducing the performance of speed prediction method. The proposed method contains two procedures: The first one is to convert the traffic network data to images; in this case the speed variable will be transformed. The second step of the procedure presents a modification of the CNN method for speed prediction in which a separable convolution is used to reduce the number of parameters. This separable convolution helps to reducing the convergence time of speed predictions for large-scale transportation network. The proposal is evaluated with real data from the Caltrans Performance Measurement System (PeMS), obtained through sensors. The results show that Separable Convolutional Neural Network (SCNN) reduces convergence time of CNN method without losing the performance of the predictions of traffic speed in a large-scale transportation network.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 10th International Conference on Computer Modeling and Simulation, ICCMS 2018
EditorialAssociation for Computing Machinery
Páginas157-161
Número de páginas5
ISBN (versión digital)9781450363396
DOI
EstadoPublicada - 8 ene. 2018
Evento10th International Conference on Computer Modeling and Simulation, ICCMS 2018 - Sydney, Australia
Duración: 8 ene. 201810 ene. 2018

Serie de la publicación

NombreACM International Conference Proceeding Series

Conferencia

Conferencia10th International Conference on Computer Modeling and Simulation, ICCMS 2018
País/TerritorioAustralia
CiudadSydney
Período8/01/1810/01/18

Nota bibliográfica

Publisher Copyright:
© 2018 Association for Computing Machinery.

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