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Prediction of Congested Traffic on the Critical Density Point Using Machine Learning and Decentralised Collaborating Cameras
Katholieke Univeristy Leuven.ORCID iD: 0000-0002-1162-0817
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2009 (English)In: New Trends in Artificial Intelligence: 14th Portuguese Conference on Artificial Intelligence pages, 2009, 15-26 p.Conference paper, (Refereed)
Abstract [en]

In this paper we discuss short term traffic congestion prediction, more specifically, prediction of the sudden speed drop when traffic resides at the critical density point. We approach this problem using standard machine learning techniques combining information from multiple sensors measuring density and average velocity. The model used for prediction is learned offline. Our goal is to implement (and possibly update) the predictive model in a multi-agent system, where coupled with each sensor, there is an agent that monitors the condition of traffic, starts to collect data from other sensors located nearby when necessary and is able to predict local sudden speed drops so that drivers can be warned ahead of time. We evaluate Gaussian processes, support vector machines and decision trees not only limited to predictive accuracy, but also the suitability of the learned model in the setup as described above, i.e., keeping in mind that we want the warning system to be decentralized and want to ensure scalability and robustness.

Place, publisher, year, edition, pages
2009. 15-26 p.
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:lnu:diva-19337OAI: oai:DiVA.org:lnu-19337DiVA: diva2:530442
Conference
14th Portuguese Conference on Artificial Intelligence, EPIA 12-15 Oct, 2009, Averio, Portugal
Available from: 2012-06-01 Created: 2012-06-01 Last updated: 2016-12-19Bibliographically approved

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