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A non-parametric Bayesian framework for traffic-state estimation at signalized intersections
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. Enjoyor Co Ltd, Hangzhou 310030, Zhejiang, Peoples R China.ORCID iD: 0000-0002-1375-9054
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
2019 (English)In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 498, p. 21-40Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 498, p. 21-40
Keywords [en]
Traffic state estimation, Data-driven model, Non-parametric framework, Bayesian filters, Gaussian process regression
National Category
Transport Systems and Logistics
Identifiers
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
Note

QC 20190729

Available from: 2019-07-29 Created: 2019-07-29 Last updated: 2019-07-29Bibliographically approved

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Jin, JunchenMa, Xiaoliang

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf