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Hybrid Approach for...
Hybrid Approach for Short-Term Traffic State and Travel Time Prediction on Highways
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- Allström, Andreas, 1978- (författare)
- Linköpings universitet,Kommunikations- och transportsystem,Tekniska fakulteten
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- Ekström, Joakim, 1980- (författare)
- Linköpings universitet,Kommunikations- och transportsystem,Tekniska fakulteten
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- Gundlegård, David, 1978- (författare)
- Linköpings universitet,Kommunikations- och transportsystem,Tekniska fakulteten
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- Ringdahl, Rasmus (författare)
- Linköpings universitet,Kommunikations- och transportsystem,Tekniska fakulteten
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- Rydergren, Clas, 1972- (författare)
- Linköpings universitet,Kommunikations- och transportsystem,Tekniska fakulteten
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- Bayen, Alexandre M. (författare)
- Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA
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- Patire, Anthony D. (författare)
- Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA
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(creator_code:org_t)
- 2016-01-01
- 2016
- Engelska.
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Ingår i: Transportation Research Record. - Washington, DC, USA : The National Academies of Sciences, Engineering, and Medicine. - 0361-1981 .- 2169-4052. ; 2554, s. 60-68
- Relaterad länk:
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https://liu.diva-por... (primary) (Raw object)
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http://liu.diva-port...
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- 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
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
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- art (ämneskategori)
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