Short-term prediction of traffic flow status for online driver information

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorInnamaa, Satu 
dc.contributor.schoolInsinööritieteiden ja arkkitehtuurin tiedekuntafi
dc.date.accessioned2012-08-23T05:22:21Z
dc.date.available2012-08-23T05:22:21Z
dc.date.issued2009 
dc.description.abstractThe principal aim of this study was to develop a method for making a short-term prediction model of traffic flow status (i.e. travel time and a five-step travel-speed-based classification) and test its performance in the real world environment. Specifically, the objective was to find a method that can predict the traffic flow status on a satisfactory level, can be implemented without long delays and is practical for real-time use also in the long term. A sequence of studies shows the development process from offline models with perfect data to online models with field data. Models were based on MLP neural networks and self-organising maps. The purpose of the online model was to produce real-time information of the traffic flow status that can be given to drivers. The models were tested in practice. In conclusion, the results of online use of the prediction models in practice were promising and even a simple prediction model was shown to improve the accuracy of travel time information especially in congested conditions. The results also indicated that the self-adapting principle improved the performance of the model and made it possible to implement the model quite quickly. The model was practical for real-time use also in the long term in terms of the number of carry bits that it requires to restore the history of samples of traffic situations. As self-adapting this model performed better than as a static version i.e. without the self-adapting feature, as the proportion of correctly predicted traffic flow status increased considerably for the self-adapting model during the online trial.en
dc.format.extentVerkkokirja (2614 KB, 79, [90] s.) 
dc.format.mimetypeapplication/pdf 
dc.identifier.isbn978-951-38-7341-7 (electronic) 
dc.identifier.isbnpaperimuodossa (ISBN978-951-38-7340-0 (printed)#8195;
dc.identifier.issn1455-0849 
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/4627
dc.identifier.urnURN:ISBN:978-951-38-7341-7 
dc.language.isoenen
dc.publisherVTTen
dc.relation.haspart[Publication 1]: Satu Innamaa. 2005. Short-term prediction of travel time using neural networks on an interurban highway. Transportation, volume 32, number 6, pages 649-669.en
dc.relation.haspart[Publication 2]: Satu Innamaa. 2006. Effect of monitoring system structure on short-term prediction of highway travel time. Transportation Planning and Technology, volume 29, number 2, pages 125-140.en
dc.relation.haspart[Publication 3]: Satu Innamaa. 2007. Online prediction of travel time: Experience from a pilot trial. Transportation Planning and Technology, volume 30, numbers 2-3, pages 271-287.en
dc.relation.haspart[Publication 4]: Satu Innamaa and Iisakki Kosonen. 2004. Online traffic models – a learning experience. Traffic Engineering and Control (TEC), volume 45, number 9, pages 338-343. London, United Kingdom. Hemming Group Ltd.en
dc.relation.haspart[Publication 5]: S. Innamaa. 2009. Self-adapting traffic flow status forecasts using clustering. IET Intelligent Transport Systems, volume 3, number 1, pages 67-76.en
dc.relation.ispartofseriesVTT publications, 708en
dc.subject.keywordpredictionen
dc.subject.keywordtraffic flow statusen
dc.subject.keywordtravel timeen
dc.subject.otherTransport engineeringen
dc.titleShort-term prediction of traffic flow status for online driver informationen
dc.typeG5 Artikkeliväitöskirjafi
dc.type.dcmitypetexten
dc.type.ontasotVäitöskirja (artikkeli)fi
dc.type.ontasotDoctoral dissertation (article-based)en
local.aalto.digiauthask
local.aalto.digifolderAalto_67995
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