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Urban Network Travel Time Prediction via Online Multi-Output Gaussian Process Regression
KTH Royal Inst Technol, Sweden.
KTH Royal Inst Technol, Sweden.
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
2017 (English)In: 2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE , 2017Conference paper, Published paper (Refereed)
Abstract [en]

The paper explores the potential of Multi-Output Gaussian Processes to tackle network-wide travel time prediction in an urban area. Forecasting in this context is challenging due to the complexity of the traffic network, noisy data and unexpected events. We build on recent methods to develop an online model that can be trained in seconds by relying on prior network dependences through a coregionalized covariance. The accuracy of the proposed model outperforms historical means and other simpler methods on a network of 47 streets in Stockholm, by using probe data from GPS-equipped taxis. Results show how traffic speeds are dependent on the historical correlations, and how prediction accuracy can be improved by relying on prior information while using a very limited amount of current-day observations, which allows for the development of models with low estimation times and high responsiveness.

Place, publisher, year, edition, pages
IEEE , 2017.
Series
IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:liu:diva-148663ISI: 000432373000201ISBN: 978-1-5386-1526-3 (print)OAI: oai:DiVA.org:liu-148663DiVA, id: diva2:1219954
Conference
20th IEEE International Conference on Intelligent Transportation Systems (ITSC)
Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2018-06-18

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf