Probability hypothesis density filtering for real-time traffic state estimation and prediction

DOI10.3934/NHM.2013.8.825zbMATH Open1277.60116OpenAlexW1964149476MaRDI QIDQ380254

Nour-Eddin El Faouzi, Lyudmila Mihaylova, Matthieu Canaud, Jacques Sau

Publication date: 13 November 2013

Published in: Networks and Heterogeneous Media (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/nhm.2013.8.825

zbMATH Keywords

particle filtersequential Monte Carlo methodstraffic controlprobability hypothesis density filtertraffic state estimation

Mathematics Subject Classification ID

Probability distributions: general theory (60E05) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Sequential estimation (62L12)

Related Items (3)

A model of federated evidence fusion for real-time urban traffic state estimation ⋮ Gaussian mixture probability hypothesis density filter with dynamic probabilities: application to road traffic surveillance ⋮ Big data-driven based real-time traffic flow state identification and prediction

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