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

• Published in: 2008 47th IEEE Conference on Decision and Control pp. 5062 - 5068
• Main Authors: Work, D.B., Tossavainen, O.-P., Blandin, S., Bayen, A.M., Iwuchukwu, T., Tracton, K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.01.2008
• Subjects: Data assimilation; Filtering; Global Positioning System; Kalman filters; Large-scale systems; Mobile handsets; Road transportation; State estimation; Traffic control; Transforms
• ISBN: 9781424431236; 1424431239
• ISSN: 0191-2216
• DOI: 10.1109/CDC.2008.4739016

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.