Virtual traffic simulation with neural ...
Type de document :
Article dans une revue scientifique: Article original
Titre :
Virtual traffic simulation with neural network learned mobility model.
Auteur(s) :
Zhang, Jian [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
El Kamel, Abdelkader [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
El Kamel, Abdelkader [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Titre de la revue :
Advances in Engineering Software
Pagination :
103-111
Éditeur :
Elsevier
Date de publication :
2018-01
ISSN :
0965-9978
Mot(s)-clé(s) en anglais :
Modeling and simulation transportation system
Neural networks
Highway traffic
Mobility model
Neural networks
Highway traffic
Mobility model
Discipline(s) HAL :
Informatique [cs]
Résumé en anglais : [en]
Virtual traffic simulation plays an important role in easing traffic congestion and reducing traffic pollution. As the transportation network expands, the former rule-based mobility models showed several limitations in ...
Lire la suite >Virtual traffic simulation plays an important role in easing traffic congestion and reducing traffic pollution. As the transportation network expands, the former rule-based mobility models showed several limitations in producing convincing virtual vehicles. A more realistic model with example-based method is in demand. In this paper, a neural network is employed with carefully selected traffic trajectory data. The virtual vehicle production is driven by the proposed mobility model and organized by a specified structure. Then, the virtual traffic simulation could be given for an indicated scenario.Lire moins >
Lire la suite >Virtual traffic simulation plays an important role in easing traffic congestion and reducing traffic pollution. As the transportation network expands, the former rule-based mobility models showed several limitations in producing convincing virtual vehicles. A more realistic model with example-based method is in demand. In this paper, a neural network is employed with carefully selected traffic trajectory data. The virtual vehicle production is driven by the proposed mobility model and organized by a specified structure. Then, the virtual traffic simulation could be given for an indicated scenario.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :