Abstract
This article describes the application of distributed computing techniques for the analysis of big data information from Intelligent Transportation Systems. Extracting useful mobility information from large volumes of data is crucial to improve decision-making processes in smart cities. We study the problem of estimating demand and origin-destination matrices based on ticket sales and location of buses in the city. We introduce a framework for mobility analysis in smart cities, including two algorithms for the efficient processing of large mobility data from the public transportation in Montevideo, Uruguay. Parallel versions are proposed for distributed memory (e.g., cluster, grid, cloud) infrastructures and a cluster implementation is presented. The experimental analysis performed using realistic datasets demonstrate that significatively speedup values, up to 16.41, are obtained.
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Fabbiani, E., Vidal, P., Massobrio, R., Nesmachnow, S. (2017). Distributed Big Data Analysis for Mobility Estimation in Intelligent Transportation Systems. In: Barrios Hernández, C., Gitler, I., Klapp, J. (eds) High Performance Computing. CARLA 2016. Communications in Computer and Information Science, vol 697. Springer, Cham. https://doi.org/10.1007/978-3-319-57972-6_11
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DOI: https://doi.org/10.1007/978-3-319-57972-6_11
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