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Hybrid Approach for Short-Term Traffic State and Travel Time Prediction on Highways
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-8934-3821
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-1367-6793
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-5961-5136
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-9142-8464
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2016 (English)In: Transportation Research Record, ISSN 0361-1981, E-ISSN 2169-4052, Vol. 2554, 60-68 p.Article in journal (Refereed) Published
Abstract [en]

Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches combine the two prediction paradigms by using nonparametric methods for predicting boundary conditions used in a parametric method. In this paper, parametric and nonparametric traffic state prediction techniques are instead combined through assimilation in an ensemble Kalman filter. For nonparametric prediction, a neural network method is adopted; the parametric prediction is carried out with a cell transmission model with velocity as state. The results show that the hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 min into the future, with a prediction horizon of up to 50 min ahead in time to allow the journey to be completed

Place, publisher, year, edition, pages
Washington, DC, USA: The National Academies of Sciences, Engineering, and Medicine , 2016. Vol. 2554, 60-68 p.
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:liu:diva-132632DOI: 10.3141/2554-07OAI: oai:DiVA.org:liu-132632DiVA: diva2:1047552
Funder
TrenOp, Transport Research Environment with Novel Perspectives
Available from: 2016-11-17 Created: 2016-11-17 Last updated: 2016-11-23Bibliographically approved

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Allström, AndreasEkström, JoakimGundlegård, DavidRingdahl, RasmusRydergren, Clas
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Communications and Transport SystemsFaculty of Science & Engineering
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