Nonlinear stability of traffic models and the use of Lyapunov vectors for estimating the traffic state

Using current global positioning system (GPS) technology by monitoring position and velocity of vehicles, valuable information for estimating traffic flow is obtained. The authors present a proof of concept study in this paper that shows how the traffic state can be estimated using only partial and noisy data by assimilating them in a dynamical model. Designed to be equivalent but computationally much less demanding than the traditional extended Kalman filter, the authors' approach is based on a data assimilation algorithm, developed by the authors for chaotic geophysical models. The authors show here that the algorithm is even more efficient if the system is not chaotic and demonstrate by numerical experiments that an accurate reconstruction of the complete traffic state can be obtained by monitoring only a small percentage of vehicles at a very low computational cost.

• Record URL:
  • https://doi.org/10.1103/PhysRevE.88.022901
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/15393755
• Authors:
  • Luigi, Palatella
  • Anna, Trevisan
  • Sandro, Rambaldi
• Publication Date: 2013-8

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: pp 022901-022901
• Serial:
  • Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
  • Volume: 88
  • Issue Number: 2
  • Publisher: American Physical Society
  • ISSN: 1539-3755
  • EISSN: 1550-2376
  • Serial URL: http://pre.aps.org/

Subject/Index Terms

• TRT Terms: Dynamic models; Estimation theory; Global Positioning System; Nonlinear systems; Traffic flow; Traffic models
• Uncontrolled Terms: Proof of concept
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01496557
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 25 2013 11:30AM