research-article

SMAFramework: Urban Data Integration Framework for Mobility Analysis in Smart Cities

Authors:

Diego O. Rodrigues,

Azzedine Boukerche,

Thiago H. Silva,

Antonio A.F. Loureiro,

Leandro A. VillasAuthors Info & Claims

MSWiM '17: Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems

Pages 227 - 236

https://doi.org/10.1145/3127540.3127569

Published: 21 November 2017 Publication History

Abstract

Smart cities emerge in computer science as a topic to cover how the technology of information and communication can be used in the urban centers to monitor its dynamics and allow the improvement of services for the citizens. In these urban centers, different methodologies are used in order to collect data and provide them to applications. These data come from several heterogeneous sources, thus there is an effort to integrate and standardize them before their use. Also, a significant amount of this data has spatio-temporal annotations, which may be used to analyze the city dynamics, such as the mobility flow. Due to these characteristics of the data generated in urban centers, and also the possibilities brought by their use and analyses, this work presents a novel approach to collect, integrate and perform some analysis tasks in mobility data from smart cities. Thus, the SMAFramework can analyze mobility patterns based on a Multi-Aspect Graph (MAG) data structure. To show the potential of the framework, it is proposed a method to analyze the saptio-temporal correlation between data from two different data sources in the same city. Real data collected from social media and a taxi system of the city of New York are used to evaluate this method. The obtained results allowed to understand some of the applicabilities of the framework and also provided some insights on how to use the framework to resolve specific problems when analyzing mobility in urban environments.

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Cited By

Silva TSilver D(2024)Using graph neural networks to predict local cultureEnvironment and Planning B: Urban Analytics and City Science10.1177/23998083241262053Online publication date: 14-Jun-2024
https://doi.org/10.1177/23998083241262053
Gilman EBugiotti FKhalid AMehmood HKostakos PTuovinen LYlipulli JSu XFerreira D(2024)Addressing Data Challenges to Drive the Transformation of Smart CitiesACM Transactions on Intelligent Systems and Technology10.1145/366348215:5(1-65)Online publication date: 7-Nov-2024
https://dl.acm.org/doi/10.1145/3663482
Younes MBoukerche ADe Rango F(2023)SmartLight: A smart efficient traffic light scheduling algorithm for green road intersectionsAd Hoc Networks10.1016/j.adhoc.2022.103061140(103061)Online publication date: Mar-2023
https://doi.org/10.1016/j.adhoc.2022.103061
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Index Terms

SMAFramework: Urban Data Integration Framework for Mobility Analysis in Smart Cities
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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Published In

cover image ACM Conferences

MSWiM '17: Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems

November 2017

340 pages

ISBN:9781450351621

DOI:10.1145/3127540

General Chair:
Antonio Loureiro
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Hsiao-Chun Wu
Louisiana State University, USA
,
Richard Yu
Carleton University, Canada

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 21 November 2017

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São Paulo Research Foundation (FAPESP)

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MSWiM '17

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SIGSIM

MSWiM '17: 20th ACM Int'l Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems

November 21 - 25, 2017

Florida, Miami, USA

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MSWiM '17 Paper Acceptance Rate 29 of 142 submissions, 20%;

Overall Acceptance Rate 398 of 1,577 submissions, 25%

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Cited By

Silva TSilver D(2024)Using graph neural networks to predict local cultureEnvironment and Planning B: Urban Analytics and City Science10.1177/23998083241262053Online publication date: 14-Jun-2024
https://doi.org/10.1177/23998083241262053
Gilman EBugiotti FKhalid AMehmood HKostakos PTuovinen LYlipulli JSu XFerreira D(2024)Addressing Data Challenges to Drive the Transformation of Smart CitiesACM Transactions on Intelligent Systems and Technology10.1145/366348215:5(1-65)Online publication date: 7-Nov-2024
https://dl.acm.org/doi/10.1145/3663482
Younes MBoukerche ADe Rango F(2023)SmartLight: A smart efficient traffic light scheduling algorithm for green road intersectionsAd Hoc Networks10.1016/j.adhoc.2022.103061140(103061)Online publication date: Mar-2023
https://doi.org/10.1016/j.adhoc.2022.103061
Gubert FSantin PFonseca MMunaretto ASilva T(2023)On strategies to help reduce contamination on public transit: a multilayer network approachApplied Network Science10.1007/s41109-023-00562-78:1Online publication date: 23-Jun-2023
https://doi.org/10.1007/s41109-023-00562-7
Pereira JBatista TCavalcante ESouza ALopes FCacho N(2022)A platform for integrating heterogeneous data and developing smart city applicationsFuture Generation Computer Systems10.1016/j.future.2021.10.030128:C(552-566)Online publication date: 9-Apr-2022
https://dl.acm.org/doi/10.1016/j.future.2021.10.030
Rodrigues DMaia GBraun TLoureiro APeixoto MVillas L(2021)Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban TripsApplied Sciences10.3390/app1110452311:10(4523)Online publication date: 15-May-2021
https://doi.org/10.3390/app11104523
Silva TViana ABenevenuto FVillas LSalles JLoureiro AQuercia D(2019)Urban Computing Leveraging Location-Based Social Network DataACM Computing Surveys10.1145/330128452:1(1-39)Online publication date: 13-Feb-2019
https://dl.acm.org/doi/10.1145/3301284
Meneguette RRodrigues Dda Costa JRosario DVillas L(2019)A Virtual Machine Migration Policy Based on Multiple Attribute Decision in Vehicular Cloud ScenarioICC 2019 - 2019 IEEE International Conference on Communications (ICC)10.1109/ICC.2019.8761248(1-6)Online publication date: May-2019
https://doi.org/10.1109/ICC.2019.8761248

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