This talk will investigate the problem of real-time traffic monitoring using data obtained from GPS enabled smartphones. GPS velocity measurements, further degraded to preserve user anonymity, cannot be easily iterated into the well-known density-based network of partial differential equations typically used to describe traffic. This challenge is circumvented by transforming the density-based traffic model into a new but equivalent evolution equation for velocity, which retains the nonlinearity and non-differentiability of traffic due to shocks. An important consequence of the equivalent velocity model is that GPS velocity measurements become direct but noisy observations of the traffic state. The resulting state estimation problem can then be solved in real-time for large road networks with an ensemble Kalman filtering algorithm. This approach has been implemented and tested in two field experiments in Northern California, known as Mobile Century and Mobile Millennium respectively. Recent extensions of this work will also be discussed.
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