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In the commonly used Kriging approaches, the covariance function depends only on the separation distance irrespective of the traffic at the considered locations. A key limitation of such an approach is its inability to capture well the traffic dynamics and transitions between different states. This paper proposes a Bayesian Kriging approach for the prediction of urban traffic. The approach can capture these dynamics and model changes via the covariance matrix. The main novelty consists in representing both stationary and nonstationary changes in traffic flows by a discriminative covariance function conditioned on the observation at each location. An advantage of the approach is that it can represent congested regions and interactions in both upstream and downstream areas. Experiment carried out with real data from Santander, Spain shows that RMSE of our method outperforms traditional Kriging method by 8.4%.
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", "description": "Open repository for EU-funded research outputs from Horizon Europe, Euratom and earlier Framework Programmes.", "organizations": [ { "id": "00k4n6c32" } ], "page": "The EU Open Research Repository is a Zenodo-community dedicated to fostering open science and enhancing the visibility and accessibility of research outputs funded by the European Union. The community is managed by CERN on behalf of the European Commission.
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