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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Computer Modeling and Simulation, ICCMS 2018
PublisherAssociation for Computing Machinery
Pages157-161
Number of pages5
ISBN (Electronic)9781450363396
DOIs
StatePublished - 8 Jan 2018
Event10th International Conference on Computer Modeling and Simulation, ICCMS 2018 - Sydney, Australia
Duration: 8 Jan 201810 Jan 2018

Publication series

NameACM International Conference Proceeding Series

Conference

Conference10th International Conference on Computer Modeling and Simulation, ICCMS 2018
Country/TerritoryAustralia
CitySydney
Period8/01/1810/01/18

Bibliographical note

Publisher Copyright:
© 2018 Association for Computing Machinery.

Keywords

  • Convolutional neural network
  • Deep learning
  • Separable convolution
  • Spatiotemporal features
  • Traffic speed prediction
  • Transportation network

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