NDLI: An ensemble Kalman filtering approach to highway traffic estimation using GPS enabled mobile devices

Content Provider	IEEE Xplore Digital Library
Author	Work, D.B. Tossavainen, O.-P. Blandin, S. Bayen, A.M. Iwuchukwu, T. Tracton, K.
Copyright Year	2008
Description	Author affiliation: Nokia Res. Center, Palo Alto, CA, USA (Iwuchukwu, T.; Tracton, K.) \|\| Dept. Ville Environnement Transp., Ecole Nat. des Ponts et Chaussees, Marne-la-Vallee, France (Blandin, S.) \|\| Dept. of Civil & Environ. Eng., Univ. of California, Berkeley, CA, USA (Work, D.B.; Tossavainen, O.-P.; Bayen, A.M.)
Abstract	Traffic state estimation is a challenging problem for the transportation community due to the limited deployment of sensing infrastructure. However, recent trends in the mobile phone industry suggest that GPS equipped devices will become standard in the next few years. Leveraging these GPS equipped devices as traffic sensors will fundamentally change the type and the quality of traffic data collected on large scales in the near future. New traffic models and data assimilation algorithms must be developed to efficiently transform this data into usable traffic information. In this work, we introduce a new partial differential equation (PDE) based on the Lighthill-Whitham-Richards PDE, which serves as a flow model for velocity. We formulate a Godunov discretization scheme to cast the PDE into a Velocity Cell Transmission Model (CTM-v), which is a nonlinear dynamical system with a time varying observation matrix. The Ensemble Kalman Filtering (EnKF) technique is applied to the CTM- v to estimate the velocity field on the highway using data obtained from GPS devices, and the method is illustrated in microsimulation on a fully calibrated model of I880 in California. Experimental validation is performed through the unprecedented 100-vehicle Mobile Century experiment, which used a novel privacy-preserving traffic monitoring system to collect GPS cell phone data specifically for this research.
Starting Page	5062
Ending Page	5068
File Size	325371
Page Count	7
File Format	PDF
ISBN	9781424431236
ISSN	01912216
DOI	10.1109/CDC.2008.4739016
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-09
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kalman filters Filtering Road transportation Global Positioning System Traffic control State estimation Mobile handsets Large-scale systems Data assimilation Transforms
Content Type	Text
Resource Type	Article

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