Understanding Network Traffic States using Transfer Learning

Panchamy Krishnakumari, Alan Perotti, Viviana Pinto, Oded Cats, Hans van Lint

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

4 Citations (Scopus)
20 Downloads (Pure)

Abstract

Large-scale network traffic analysis is crucial for many transport applications, ranging from estimation and prediction to control and planning. One of the key issues is how to integrate spatial and temporal analyses efficiently. Deep Learning is gaining momentum as a go-to approach for artificial vision, and transfer learning approaches allow to exploit pretrained models and apply them to new domains. In this paper, we encode traffic states as images and use a pretrained deep convolutional neural network as a feature extractor. Experimental results show how the extracted feature vectors cluster naturally into meaningful network traffic states and illustrate how these network states can be used for traffic state prediction.
Original languageEnglish
Title of host publicationProceedings of the 21st International Conference on Intelligent Transportation Systems (ITSC)
Subtitle of host publication4-7 Nov. 2018, Maui, HI, USA
EditorsWei-Bin Zhang, Alexandre Bayen, Javier Sanchez-Medina, Matthew Barth
PublisherIEEE
Pages1396-1401
ISBN (Electronic)978-1-7281-0323-5
DOIs
Publication statusPublished - 2018
Event21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018 - Maui, United States
Duration: 4 Nov 20187 Nov 2018
Conference number: 21
https://www.ieee-itsc2018.org/

Conference

Conference21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
Abbreviated titleITSC 2018
CountryUnited States
CityMaui
Period4/11/187/11/18
Internet address

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