Special Issue "Big Data in a Sustainable Smart City"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: 30 April 2021.

Special Issue Editors

Dr. Abdellah Chehri
Website
Guest Editor
Département des Sciences Appliquées, Université de Québec à Chicoutimi, 555, boul. de l’Université, Chicoutimi, Québec G7H 2B1, Canada
Interests: big data; smart and sustainable cities; urban innovation system; urban knowledge and innovation spaces; knowledge-based development
Special Issues and Collections in MDPI journals
Dr. Gwanggil Jeon

Guest Editor

Special Issue Information

Dear Colleagues,

Smart cities are meant to supervise available resources sustainably to enhance the economy and societal outcomes. Therefore, data from assorted resources are measured to be the most scalable property of a smart city. Research results on big data have been focused on enhancing the latter stages of processing colossal amounts of data. In smart cities, different villages and cities produce heterogeneous data with minimal or no coordination. The existing methods to address these problems in big data analytics for smart cities are still immature. Most of these methods are also time consuming, as they use traditional tools of data mining. In order to solve this issue, new and elegant methods are required to effectively control the big data generated from the sensors deployed in existing cities. This Special Issue aims to report the latest advances and trends concerning advanced machine learning techniques and time series remote sensing data processing issues. Papers addressing both theory and application are welcome, as well as contributions regarding new advanced machine learning techniques for the remote sensing research community. Major topics of interest include, but are not limited to, the following:

  • Smart cities based on big data;
  • Big data analytics;
  • Embedded sensing;
  • System design, modeling, and evaluation within a smart city;
  • Smart systems for sustainability.

Dr. Gwanggil Jeon
Dr. Abdellah Chehri
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart city
  • big data analytics
  • embedded sensing
  • smart system
  • system design
  • sustainability

Published Papers (2 papers)

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Research

Open AccessArticle
Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning
Sustainability 2021, 13(1), 266; https://doi.org/10.3390/su13010266 - 30 Dec 2020
Abstract
Traffic congestion is becoming a critical problem in urban traffic planning. Intelligent transportation systems can help expand the capacity of urban roads to alleviate traffic congestion. As a key concept in intelligent transportation systems, urban traffic networks, especially dynamic traffic networks, can serve [...] Read more.
Traffic congestion is becoming a critical problem in urban traffic planning. Intelligent transportation systems can help expand the capacity of urban roads to alleviate traffic congestion. As a key concept in intelligent transportation systems, urban traffic networks, especially dynamic traffic networks, can serve as potential solutions for traffic congestion, based on the complex network theory. In this paper, we build a traffic flow network model to investigate traffic congestion problems through taxi GPS trajectories. Moreover, to verify the effectiveness of the traffic flow network, an actual case of identifying the congestion areas is considered. The results indicate that the traffic flow network is reliable. Finally, several key problems related to traffic flow networks are discussed. The proposed traffic flow network can provide a methodological reference for traffic planning, especially to solve traffic congestion problems. Full article
(This article belongs to the Special Issue Big Data in a Sustainable Smart City)
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Open AccessArticle
Comparing Human Activity Density and Green Space Supply Using the Baidu Heat Map in Zhengzhou, China
Sustainability 2020, 12(17), 7075; https://doi.org/10.3390/su12177075 - 30 Aug 2020
Abstract
Rapidly growing cities often struggle with insufficient green space, although information on when and where more green space is needed can be difficult to collect. Big data on the density of individuals in cities collected from mobile phones can estimate the usage intensity [...] Read more.
Rapidly growing cities often struggle with insufficient green space, although information on when and where more green space is needed can be difficult to collect. Big data on the density of individuals in cities collected from mobile phones can estimate the usage intensity of urban green space. Taking Zhengzhou’s central city as an example, we combine the real-time human movement data provided by the Baidu Heat Map, which indicates the density of mobile phones, with vector overlays of different kinds of green space. We used the geographically weighted regression (GWR) method to estimate differentials in green space usage between weekdays and weekends, utilizing the location and the density of the aggregation of people with powered-up mobile phones. Compared with weekends, the aggregation of people in urban green spaces on workdays tends to vary more in time and be more concentrated in space, while the highest usage is more stable on weekends. More importantly, the percentage of weekday green space utilization is higher in small parks and green strips in the city, with the density increasing in those small areas, while the green space at a greater distance to the city center is underutilized. This study validates the potential of applying Baidu Heat Map data to provide a dynamic perspective of green space use, and highlights the need for more green space in city centers. Full article
(This article belongs to the Special Issue Big Data in a Sustainable Smart City)
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