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Modeling and identification of nonlinear dynamics for freeway traffic by using information from a mobile cellular network (Articolo in rivista)
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- Modeling and identification of nonlinear dynamics for freeway traffic by using information from a mobile cellular network (Articolo in rivista) (literal)
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- 2009-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1109/TCST.2009.2014242 (literal)
- Alternative label
A. Alessandri; R. Bolla; M. Gaggero; M. Repetto (2009)
Modeling and identification of nonlinear dynamics for freeway traffic by using information from a mobile cellular network
in IEEE transactions on control systems technology (Print); IEEE-Institute Of Electrical And Electronics Engineers Inc., Piscataway (Stati Uniti d'America)
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- A. Alessandri; R. Bolla; M. Gaggero; M. Repetto (literal)
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- Journal Q1 in Control and Systems Engineering (literal)
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- 1. Department of Production Engineering, Thermoenergetics, and Mathematical Models (DIPTEM), University of Genoa, I-16129 Genoa, Italy
2. Department of Communications, Computer and System Sciences (DIST), University of Genoa, I-16145 Genoa, Italy
3. Department of Production Engineering, Thermoenergetics, and Mathematical Models (DIPTEM), University of Genoa, I-16129 Genoa, Italy
4. Department of Communications, Computer and System Sciences (DIST), University of Genoa, I-16145 Genoa, Italy (literal)
- Titolo
- Modeling and identification of nonlinear dynamics for freeway traffic by using information from a mobile cellular network (literal)
- Abstract
- The high coverage of the territory by cellular networks and the widespread diffusion of mobile terminals aboard vehicles allow one to collect information on the traffic behavior. The problem of selecting a dynamic model to describe the freeway traffic by using the information available from a wireless cellular network is addressed by assuming the distribution of mobile terminals aboard vehicles to be uniform along the carriageway. Two different nonlinear parametrized models of freeway traffic are investigated: the first is an extension to a well-established macroscopic model, while the second is based on a black-box approach and consists in using a neural network to approximate the traffic dynamics. The parameters of such models are identified off line by a least-squares technique. Traffic measurements obtained from a cellular network are employed to identify and validate the proposed models, as shown by means of simulations. (literal)
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