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- W2184325519 abstract "Application of Gaussian Processes is an appealing state-of-the-art method that outperforms recent methods (Liebig et. al., 2013). The method bases on a covariance matrix that denotes the correlations among the traffic flux values at various locations. Due to the computational complexity of Gaussian Process Regression, application to urban areas were restricted either to small sites or a sample of locations (Artikis et. al., 2014). This paper introduces and discusses the application of a speed-up heuristic to Gaussian process regression for the traffic flow estimation problem. The computational complexity results from the kernel inversion which is part of the Gaussian Process Regression. We relax this global function to a focal one which incorporates not all data at once, but iteratively incorporates the data of the neighborhood. The neighborhood, however, has to be defined in advance. This neighborhood definition should be consistent to the correlation expressed by the kernel function. I.e., if a kernel models correlation based on the spatial closeness, the spatial closest neighbors are most likely the important locations for estimation of the unknown neighbor. Intuitively, this heuristic introduces a Markov assumption, whereas the traffic flow at one location is fully defined by the flow situation of its neighbors. Furthermore, as we want the Gaussian Process Regression to be applicable we may want to fix the kernel size and thereof the number of neighbors being incorporated for traffic flow prediction. This step seems to be LBS 2014" @default.
- W2184325519 created "2016-06-24" @default.
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- W2184325519 date "2014-01-01" @default.
- W2184325519 modified "2023-09-24" @default.
- W2184325519 title "Speed-Up Heuristics for Traffic Flow Estimation with Gaussian Process Regression" @default.
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