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A non-parametric Bayesian framework for traffic-state estimation at signalized intersections
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Byggvetenskap, Transportplanering. Enjoyor Co Ltd, Hangzhou 310030, Zhejiang, Peoples R China.ORCID-id: 0000-0002-1375-9054
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Byggvetenskap, Transportplanering.
2019 (Engelska)Ingår i: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 498, s. 21-40Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

An accurate and practical traffic-state estimation (TSE) method for signalized intersections plays an important role in real-time operations to facilitate efficient traffic management. This paper presents a generalized modeling framework for estimating traffic states at signalized intersections. The framework is non-parametric and data-driven, without any requirement on explicit modeling of traffic flow. The Bayesian filter (BF) approach is the core of the framework and introduces a recursive state estimation process. The required transition and measurement models of the BFs are trained using Gaussian process (GP) regression models with respect to a historical dataset. In addition to the detailed derivation of the integration of BFs and GP regression models, an algorithm based on the extended Kalman filter is presented for real-time traffic estimation. The effectiveness of the proposed framework is demonstrated through several numerical experiments using data generated in microscopic traffic simulations. Both fixed-location data (i.e., loop detector) and mobile data (i.e., connected vehicle) are examined with the framework. As a result, the method shows good performance under the different traffic conditions in the experiment. In particular, the approach is suitable for short-term estimation, a challenging task in traffic control and operations.

Ort, förlag, år, upplaga, sidor
Elsevier, 2019. Vol. 498, s. 21-40
Nyckelord [en]
Traffic state estimation, Data-driven model, Non-parametric framework, Bayesian filters, Gaussian process regression
Nationell ämneskategori
Transportteknik och logistik
Identifikatorer
URN: urn:nbn:se:kth:diva-255294DOI: 10.1016/j.ins.2019.05.032ISI: 000473122500002Scopus ID: 2-s2.0-85065890487OAI: oai:DiVA.org:kth-255294DiVA, id: diva2:1339375
Anmärkning

QC 20190729

Tillgänglig från: 2019-07-29 Skapad: 2019-07-29 Senast uppdaterad: 2019-07-29Bibliografiskt granskad

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Jin, JunchenMa, Xiaoliang

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