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

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Innamaa, Satu
dc.date.accessioned 2012-08-23T05:22:21Z
dc.date.available 2012-08-23T05:22:21Z
dc.date.issued 2009
dc.identifier.isbn 978-951-38-7341-7 (electronic)
dc.identifier.isbn paperimuodossa (ISBN978-951-38-7340-0 (printed) #8195;
dc.identifier.issn 1455-0849
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/4627
dc.description.abstract The 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.extent Verkkokirja (2614 KB, 79, [90] s.)
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher VTT en
dc.relation.ispartofseries VTT publications, 708 en
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.subject.other Transport engineering en
dc.title Short-term prediction of traffic flow status for online driver information en
dc.type G5 Artikkeliväitöskirja fi
dc.contributor.school Insinööritieteiden ja arkkitehtuurin tiedekunta fi
dc.subject.keyword prediction en
dc.subject.keyword traffic flow status en
dc.subject.keyword travel time en
dc.identifier.urn URN:ISBN:978-951-38-7341-7
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en


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