Toward a cost-effective motorway traffic state estimation from sparse speed and GPS data - Télécom SudParis Accéder directement au contenu
Article Dans Une Revue IEEE Access Année : 2021

Toward a cost-effective motorway traffic state estimation from sparse speed and GPS data

Résumé

In this paper, we propose a new data-driven traffic state estimation model that estimates traffic flow based on average speed data only. The model is devised to implement a cost-effective framework that aggregates heterogeneous sources of vehicles' GPS and speed measurements to infer traffic flow using a novel triplet system called Conditionally Gaussian Observed Markov Fuzzy Switching Systems (CGOMFSM). Unlike its hard counterpart, CGOMFSM allows for a transient and gradual representation of traffic state transition and hence improves the estimation performance using a tractable scheme. The potential of the proposed model is illustrated through an application to the problem of traffic incident detection, particularly sporadic traffic congestion caused by unexpected road conditions. The performance of the proposed model is assessed using real traffic datasets from England highways. A simulation of traffic in the city of Salalah in Oman was conducted to evaluate the efficacy of the CGOMFSM-based traffic estimation and incident detection schemes with different penetration rates.
Fichier principal
Vignette du fichier
IEEE_Access_2021.pdf (4.04 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-03181894 , version 1 (26-03-2021)

Licence

Paternité

Identifiants

Citer

Zied Bouyahia, Hedi Haddad, Stéphane Derrode, Wojciech Pieczynski. Toward a cost-effective motorway traffic state estimation from sparse speed and GPS data. IEEE Access, 2021, 9, pp.44631 - 44646. ⟨10.1109/access.2021.3066422⟩. ⟨hal-03181894⟩
80 Consultations
67 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More