Research Article
A distributed platform for big data analysis in smart cities: combining Intelligent Transportation Systems and socioeconomic data for Montevideo, Uruguay
@ARTICLE{10.4108/eai.19-12-2017.153478, author={Sergio Nesmachnow and Sebasti\^{a}n Ba\`{o}a and Renzo Massobrio}, title={A distributed platform for big data analysis in smart cities: combining Intelligent Transportation Systems and socioeconomic data for Montevideo, Uruguay}, journal={EAI Endorsed Transactions on Smart Cities}, volume={2}, number={5}, publisher={EAI}, journal_a={SC}, year={2017}, month={12}, keywords={Smart cities, big data, distributed computing, Intelligent Transportation Systems}, doi={10.4108/eai.19-12-2017.153478} }- Sergio Nesmachnow
Sebastián Baña
Renzo Massobrio
Year: 2017
A distributed platform for big data analysis in smart cities: combining Intelligent Transportation Systems and socioeconomic data for Montevideo, Uruguay
SC
EAI
DOI: 10.4108/eai.19-12-2017.153478
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.
Copyright © 2017 Sergio Nesmachnow, Sebastián Baña, and Renzo Massobrio, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/ licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
