Citation

Palatella L, Trevisan A, Rambaldi S. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2013; 88(2): 022901.

Affiliation

Centro Interdipartimentale CIG L. Galvani Università di Bologna, Via Selmi n. 3, Bologna, Italy and Istituto di Scienze dell'Atmosfera e del Clima del CNR, U.O.S. di Lecce, Str. Prov. Lecce-Monteroni km 1200, Lecce, Italy.

Copyright

(Copyright © 2013, American Physical Society, Publisher American Institute of Physics)

DOI

unavailable

PMID

24032895

Abstract

Valuable information for estimating the traffic flow is obtained with current GPS technology by monitoring position and velocity of vehicles. In this paper, we present a proof of concept study that shows how the traffic state can be estimated using only partial and noisy data by assimilating them in a dynamical model. Our approach is based on a data assimilation algorithm, developed by the authors for chaotic geophysical models, designed to be equivalent but computationally much less demanding than the traditional extended Kalman filter. Here we show 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 at a very low computational cost by monitoring only a small percentage of vehicles.

Language: en