Abstract
This article proposes a platform for distributed big data analysis in the context of smart cities. Extracting useful mobility information from large volumes of data is crucial to improve decision-making processes in smart cities. This article introduces a framework for mobility analysis in smart cities combining Intelligent Transportation Systems and socioeconomic data for the city of Montevideo, Uruguay. The efficiency of the proposed system is analyzed over a distributed computing infrastructure, demonstrating that the system scales properly for processing large volumes of data for both off-line and on-line scenarios. Applications of the proposed platform and case studies using real data are presented, as examples of the valuable information that can be offered to both citizens and authorities. The proposed model for big data processing can also be extended to allow using other distributed (e.g. grid, cloud, fog, edge) computing infrastructures.
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