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Lagrangian sensing: traffic estimation with mobile devices

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4 Author(s)
Daniel B. Work ; Department of Civil and Environmental Engineering, University of California, Berkeley, 94720-1710, USA ; Olli-Pekka Tossavainen ; Quinn Jacobson ; Alexandre M. Bayen

An inverse modeling algorithm is developed to reconstruct the state of traffic (velocity field) on highways from GPS measurements gathered from mobile phones traveling on-board vehicles. The algorithm is based on ensemble Kalman filtering (EnKF), to overcome the nonlinearity and non-differentiability of a distributed highway traffic model for velocity. The algorithm is implemented in an architecture which includes GPS enabled phones and a privacy aware data collection infrastructure based on the novel concept of virtual trip lines (a technology developed by Nokia). The data collection infrastructure is connected to a traffic estimation server running the EnKF algorithm online, and the estimation results are broadcast in real time back to mobile phones and to the Internet. Results from the algorithm are presented using data collected during the February 8,2008 Mobile Century experiment, in which a shock wave from a five-car accident is captured. A prototype estimation algorithm and system were run during the experiment, and highlight that measurements from as few as 2% to 5% of the commuting public are sufficient to accurately reconstruct the highway traffic state.

Published in:

2009 American Control Conference

Date of Conference:

10-12 June 2009