Modélisation stochastique macroscopique d'ordre supérieur du trafic sur les réseaux routiers : implications managériales
DOI :
https://doi.org/10.53102/2023.37.02.1156Mots-clés :
Gestion de trafic , Modélisation stochastique du trafic , Modèles GSOM stochastique lagrangienRésumé
Les systèmes de transport jouent un rôle primordial dans le développement de la croissance économique des pays. Cependant, l'apparition des véhicules autonomes et électriques et les restrictions mises en place pour limiter la diffusion et les impacts du Covid-19 dans les transports en commun ont eu un impact important sur l’augmentation des problèmes de transport notamment aux intersections. Le présent papier aide à résoudre ces problèmes. Cet article s'intéresse à la modélisation stochastique des flux du trafic sur les réseaux routiers, grâce à des modèles macroscopiques génériques de second ordre : la famille GSOM. Il a été montré que de tels modèles d'ordre supérieur peuvent être résolus dans un cadre lagrangien dont les coordonnées lagrangiennes se déplacent avec le trafic. La difficulté d'utiliser cette solution de résolution sur un réseau est de traiter les discontinuités eulériennes – fixes – telles que les jonctions. L'objectif de ce travail est double : d'une part, proposer des modèles d’intersection adaptés aux modèles stochastiques macroscopiques de flux de trafic de second ordre, et d'autre part, résoudre le modèle résultant dans le cadre d’un réseau routier. Quelques exemples numériques sont fournis pour montrer l'efficacité de l'approche proposée.
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