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Traffic State Estimation Using Connected Vehicles and Stationary Detectors
Statens väg- och transportforskningsinstitut, Samhälle, miljö och transporter, SAMT, Trafikanalys och logistik, TAL.ORCID-id: 0000-0001-5531-0274
Statens väg- och transportforskningsinstitut, Samhälle, miljö och transporter, SAMT, Trafikanalys och logistik, TAL.ORCID-id: 0000-0001-9635-5233
2018 (engelsk)Inngår i: Journal of Advanced Transportation, ISSN 0197-6729, E-ISSN 2042-3195, artikkel-id UNSP 4106086Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Real-time traffic state estimation is of importance for efficient traffic management. This is especially the case for traffic management systems that require fast detection of changes in the traffic conditions in order to apply an effective control measure. In this paper, we propose a method for estimating the traffic state and speed and density, by using connected vehicles combined with stationary detectors. The aim is to allow fast and accurate estimation of changes in the traffic conditions. The proposed method does only require information about the speed and the position of connected vehicles and can make use of sparsely located stationary detectors to limit the dependence on the infrastructure equipment. An evaluation of the proposed method is carried out by microscopic traffic simulation. The traffic state estimated using the proposed method is compared to the true simulated traffic state. Further, the density estimates are compared to density estimates from one detector-based method, one combined method, and one connected-vehicle-based method. The results of the study show that the proposed method is a promising alternative for estimating the traffic state in traffic management applications.

sted, utgiver, år, opplag, sider
WILEY-HINDAWI , 2018. artikkel-id UNSP 4106086
Emneord [en]
Traffic flow, Real time, Traffic control, Traffic concentration, Speed, Estimation, Connected vehicle, Detector
HSV kategori
Forskningsprogram
20 Road: Traffic engineering, 23 Road: ITS och traffic
Identifikatorer
URN: urn:nbn:se:vti:diva-12818DOI: 10.1155/2018/4106086ISI: 000423031600001Scopus ID: 2-s2.0-85042641207OAI: oai:DiVA.org:vti-12818DiVA, id: diva2:1195481
Tilgjengelig fra: 2018-04-05 Laget: 2018-04-05 Sist oppdatert: 2018-06-11bibliografisk kontrollert

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Grumert, EllenTapani, Andreas

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