Publication
Title
Distributed big data analysis for mobility estimation in intelligent transportation systems
Author
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.
Language
English
Source (book)
High performance computing : third Latin American conference, CARLA 2016, August 29–September 2, 2016, Mexico City, Mexico
Publication
Berlin : 2017
ISBN
978-3-319-57971-9
978-3-319-57972-6
DOI
10.1007/978-3-319-57972-6_11
Volume/pages
697 (2017) , p. 146-160
ISI
000418418000011
Full text (Publisher's DOI)
UAntwerpen
Publication type
Subject
External links
Record
Identifier
Creation 26.03.2024
Last edited 15.10.2024
To cite this reference