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A hybrid approach for short-term traffic state and travel time prediction on highways
Linköpings universitet, Institutionen för teknik och naturvetenskap, Kommunikations- och transportsystem. Linköpings universitet, Tekniska fakulteten. (Trafiksystem)
Linköpings universitet, Institutionen för teknik och naturvetenskap, Kommunikations- och transportsystem. Linköpings universitet, Tekniska fakulteten. (Trafiksystem)ORCID-id: 0000-0002-1367-6793
Linköpings universitet, Institutionen för teknik och naturvetenskap, Kommunikations- och transportsystem. Linköpings universitet, Tekniska fakulteten. (Trafiksystem)ORCID-id: 0000-0002-5961-5136
Linköpings universitet, Institutionen för teknik och naturvetenskap, Kommunikations- och transportsystem. (Trafiksystem)ORCID-id: 0000-0001-9142-8464
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2016 (engelsk)Inngår i: TRB 95th annual meeting compendium of papers, 2016Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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

sted, utgiver, år, opplag, sider
2016.
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-125386OAI: oai:DiVA.org:liu-125386DiVA, id: diva2:905252
Konferanse
Transportation Research Board 95th Annual Meeting, 2016-1-10 to 2016-1-14 Washington DC, United States
Prosjekter
Mobile Millenium Stockholm
Forskningsfinansiär
TrenOp, Transport Research Environment with Novel PerspectivesTilgjengelig fra: 2016-02-22 Laget: 2016-02-22 Sist oppdatert: 2016-06-03

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