A non-parametric Bayesian framework for traffic-state estimation at signalized intersections

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Sep 2019 Sweden English Publisher:Elsevier BVJournal:Information Sciences, volume 498, pages 21-40 (issn: 0020-0255,

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Authors: Junchen Jin; Xiaoliang Ma;

doi: 10.1016/j.ins.2019.05.032

A non-parametric Bayesian framework for traffic-state estimation at signalized intersections

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Abstract

An accurate and practical traffic-state estimation (TSE) method for signalized intersections plays an important role for real-time operations to facilitate efficient traffic management. This paper ...

Country

Sweden

Related Organizations

Royal Institute of Technology
Sweden
KUNGLIGA TEKNISKA HOEGSKOLAN
Sweden

Keywords

Transportteknik och logistik, data-driven model, Programvaruteknik, Computer Sciences, Traffic state estimation, Software Engineering, Datavetenskap (datalogi), Bayesian filters, non-parametric framework, Gaussian process regression, Transport Systems and Logistics

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