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- W2321877452 abstract "This paper formulates a consensus filter-based distributed variational Bayesian (CFBDVB) algorithm for density approximation of traffic flow and average traffic speed in a freeway. This algorithm uses traffic measurements, including volume, occupancy rate, and average velocity, collected by some inductive loop sensors. These traffic sensors are settled in sealed spaces in the freeway network, such that they demonstrate a distributed sensor network. It is assumed that measurements of the sensors can be statistically modeled by a common Gaussian mixture model. Using the suggested algorithm, a challenging problem is solved: selection of the right number of components. The algorithm begins with a large number of initialized components. In this algorithm, each sensor client severally computes local sufficient statistics by using local observations. A consensus filter is then used to spread out local sufficient statistics to neighbors and approximate global sufficient statistics in each client. Then, using the global sufficient statistics, irrelevant components are determined and omitted. Then, the remaining components' parameters are estimated. The suggested CFBDVB algorithm is scalable and robust. It estimates the parameters of mixtures and simultaneously selects the number of components. Various simulations of sensor nets have been done to confirm the promising performance of the CFBDVB algorithm." @default.
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- W2321877452 date "2017-12-01" @default.
- W2321877452 modified "2023-09-25" @default.
- W2321877452 title "Consensus Filter-Based Distributed Variational Bayesian Algorithm for Flow and Speed Density Prediction With Distributed Traffic Sensors" @default.
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- W2321877452 doi "https://doi.org/10.1109/jsyst.2015.2399931" @default.
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