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Applied Sciences
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Smart Cities in Applied Sciences
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Smart Cities in Applied Sciences

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 17113

Special Issue Editors

Prof. Dr. Hyung-Sup Jung

E-Mail Website
Guest Editor
1. Department of Smart Cities, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Republic of Korea
2. Department of Geoinformatics, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Republic of Korea
Interests: SAR; InSAR; GeoAI; sensor modeling; satellite image processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dong-Min Lee

E-Mail Website
Guest Editor
1. Department of Smart Cities, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Korea
2. Department of Transportation Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Korea
Interests: smart mobility; human factor; intelligent transportation system; personal mobility
Prof. Dr. Myungje Woo

E-Mail Website
Guest Editor
1. Department of Smart Cities, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Korea
2. Department of Urban Planning and Design, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Korea
Interests: economic development; megaregion and regional planning; urban growth management; spatial analysis; smart city; planning for North Korea
Prof. Dr. Jin Nam

E-Mail Website
Guest Editor
1. Department of Smart Cities, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Korea
2. Department of Urban Planning and Design, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Korea
Interests: urban planning in smart cities; urban development; housing and real estate market analysis; smart regeneration

Special Issue Information

Dear Colleagues,

Smart Cities are urban areas that use very diverse sensor networks to collect big data. Big data is collected from citizens, cars, devices, and buildings and then it is processed and analyzed by artificial intelligence (AI) technologies. The valuable information processed and analyzed from the big data by AI technologies is used to efficiently monitor and manage traffic and transportation systems, power plants, utilities, water supply networks, waste, crime detection, geospatial information systems, schools, libraries, hospitals, and other community services. Smart cities have become more essential to achieving the optimization of the efficiency of city operations and services. The objectives of this Special Issue are 1) to create a multidisciplinary forum of discussion in the research fields of smart cities and 2) to find new techniques and applications for smart urban regeneration, smart mobility, smart geoinformatics, smart energy and environment, smart disaster management, smart public safety, and smart construction and maintenance, etc.

Prof. Dr. Hyung-Sup Jung
Prof. Dr. Dong-Min Lee
Prof. Dr. Myungje Woo
Prof. Dr. Jin Nam
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 submissions that pass pre-check are 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. Applied Sciences 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 2400 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 Cities
  • Smart Geoinformatics
  • Smart Urban Regeneration
  • Smart Mobility
  • Smart Geoinformatics
  • Smart Energy and Environment
  • Smart Disaster Management
  • Smart Public Safety
  • Smart Construction and Maintenance

Published Papers (5 papers)

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Research

21 pages, 11713 KiB  
Article
Minimizing Intersection Waiting Time: Proposal of a Queue Network Model Using Kendall’s Notation in Panama City
by Carlos Rovetto, Edmanuel Cruz, Ivonne Nuñez, Keyla Santana, Andrzej Smolarz, José Rangel and Elia Esther Cano
Appl. Sci. 2023, 13(18), 10030; https://0-doi-org.brum.beds.ac.uk/10.3390/app131810030 - 06 Sep 2023
Viewed by 1408
Abstract
The paper presents a proposed queuing model based on Kendall’s notation for the intersection of two streets in Panama City (53 East and 56 East). The proposed model is based on a set of traffic lights that controls the flow of vehicles at [...] Read more.
The paper presents a proposed queuing model based on Kendall’s notation for the intersection of two streets in Panama City (53 East and 56 East). The proposed model is based on a set of traffic lights that controls the flow of vehicles at the intersection according to a predetermined schedule. The model analyzes the stability of the system and simulations are performed to evaluate its performance. The main objective of the paper is to optimize the vehicle flow by minimizing the waiting time for passage. In the study, it was observed that the current traffic light system on Calle 50 (50th Street) is unstable and oversaturated during weekdays, which generates large vehicle queues with no estimated exit times. It was proposed to increase the system capacity to 1300 vehicles per hour to achieve reasonable stability and provide a solution to improve traffic signal timing on 50th Street. The need to increase the system capacity has been demonstrated and an optimal value has been suggested. The evaluation of other models and the use of AI can further strengthen the system and improve the prediction accuracy in different traffic scenarios. Full article
(This article belongs to the Special Issue Smart Cities in Applied Sciences)
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28 pages, 49884 KiB  
Article
The Importance of Robust Datasets to Assess Urban Accessibility: A Comparable Study in the Distrito Tec, Monterrey, Mexico, and the Stanford District, San Francisco Bay Area, USA
by Karen Lizette Rodríguez-Hernández, Jorge Narezo-Balzaretti, Ana Luisa Gaxiola-Beltrán, Mauricio Adolfo Ramírez-Moreno, Blas Luis Pérez-Henríquez, Ricardo Ambrocio Ramírez-Mendoza, Daniel Krajzewicz and Jorge de-Jesús Lozoya-Santos
Appl. Sci. 2022, 12(23), 12267; https://0-doi-org.brum.beds.ac.uk/10.3390/app122312267 - 30 Nov 2022
Cited by 4 | Viewed by 2848
Abstract
Urban planning has a crucial role in helping cities meet the United Nations’ Sustainable Development Goals and robust datasets to assess mobility accessibility are central to smart urban planning. These datasets provide the information necessary to perform detailed analyses that help develop targeted [...] Read more.
Urban planning has a crucial role in helping cities meet the United Nations’ Sustainable Development Goals and robust datasets to assess mobility accessibility are central to smart urban planning. These datasets provide the information necessary to perform detailed analyses that help develop targeted urban interventions that increase accessibility in cities as related to the emerging vision of the 15 Minute City. This study discusses the need for such data by performing a comparative urban accessibility analysis of two university campuses and their surrounding urban areas, here defined as the Stanford District, located in the San Francisco Bay Area in the United States, and Distrito Tec in Monterrey, Mexico. The open-source tool Urban Mobility Accessibility Computer (UrMoAC) is used to assess accessibility measures in each district using available data. UrMoAC calculates distances and average travel times from block groups to major destinations using different transport modes considering the morphology of the city, which makes this study transferable and scalable. The results show that both areas have medium levels of accessibility if cycling is used as the primary mode of transportation. Hence, improving the safety and quality of cycling in both cities emerges as one of the main recommendations from the research. Finally, the results obtained can be used to generate public policies that address the specific needs of each community’s urban region based on their accessibility performance. Full article
(This article belongs to the Special Issue Smart Cities in Applied Sciences)
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18 pages, 1587 KiB  
Article
An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction
by Jianjun Ni, Yan Chen, Yu Gu, Xiaolong Fang and Pengfei Shi
Appl. Sci. 2022, 12(7), 3597; https://0-doi-org.brum.beds.ac.uk/10.3390/app12073597 - 01 Apr 2022
Cited by 5 | Viewed by 1875
Abstract
With the improvement of the living standards of the residents, it is a very important and challenging task to continuously improve the accuracy of PM2.5 (particulate matter less than 2.5 μm in diameter) prediction. Deep learning-based networks, such as LSTM and CNN, [...] Read more.
With the improvement of the living standards of the residents, it is a very important and challenging task to continuously improve the accuracy of PM2.5 (particulate matter less than 2.5 μm in diameter) prediction. Deep learning-based networks, such as LSTM and CNN, have achieved good performance in recent years. However, these methods require sufficient data to train the model. The performance of these methods is limited for the sites where the data is lacking, such as the newly constructed monitoring sites. To deal with this problem, an improved deep learning model based on the hybrid transfer learning strategy is proposed for predicting PM2.5 concentration in this paper. In the proposed model, the maximum mean discrepancy (MMD) is used to select which station in the source domain is most suitable for migration to the target domain. An improved dual-stage two-phase (DSTP) model is used to extract the spatial–temporal features of the source domain and the target domain. Then the domain adversarial neural network (DANN) is used to find the domain invariant features between the source and target domains by domain adaptation. Thus, the model trained by source domain site data can be used to assist the prediction of the target site without degradation of the prediction performance due to domain drift. At last, some experiments are conducted. The experimental results show that the proposed model can effectively improve the accuracy of the PM2.5 prediction at the sites lacking data, and the proposed model outperforms most of the latest models. Full article
(This article belongs to the Special Issue Smart Cities in Applied Sciences)
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19 pages, 10365 KiB  
Article
Dual Image-Based CNN Ensemble Model for Waste Classification in Reverse Vending Machine
by Taeyoung Yoo, Seongjae Lee and Taehyoun Kim
Appl. Sci. 2021, 11(22), 11051; https://0-doi-org.brum.beds.ac.uk/10.3390/app112211051 - 22 Nov 2021
Cited by 5 | Viewed by 5258
Abstract
A reverse vending machine motivates citizens to bring recyclable waste by rewarding them, which is a viable solution to increase the recycling rate. Reverse vending machines generally use near-infrared sensors, barcode sensors, or cameras to classify recycling resources. However, sensor-based reverse vending machines [...] Read more.
A reverse vending machine motivates citizens to bring recyclable waste by rewarding them, which is a viable solution to increase the recycling rate. Reverse vending machines generally use near-infrared sensors, barcode sensors, or cameras to classify recycling resources. However, sensor-based reverse vending machines suffer from a high configuration cost and the limited scope of target objects, and conventional single image-based reverse vending machines usually make erroneous predictions about intentional fraud objects. This paper proposes a dual image-based convolutional neural network ensemble model to address these problems. For this purpose, we first created a prototype reverse vending machine and constructed an image dataset containing two cross-sections of objects, top and front view. Then, we chose convolutional neural network models widely used in image classification as the candidates for building an accurate and lightweight ensemble model. Considering the size and classification performance of candidates, we constructed the best-fit ensemble combination and evaluated its classification performance. The final ensemble model showed a classification accuracy higher than 95% for all target classes, including fraud objects. This result proves that our approach achieves better robustness against intentional fraud objects than single image-based models and thus can broaden the scope for target resources. The measurement results on lightweight embedded platforms also demonstrated that our model provides a short inference time that is enough to facilitate the real-time execution of reverse vending machines based on low-cost edge artificial intelligence devices. Full article
(This article belongs to the Special Issue Smart Cities in Applied Sciences)
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20 pages, 2347 KiB  
Article
Facilitating Successful Smart Campus Transitions: A Systems Thinking-SWOT Analysis Approach
by Bankole Awuzie, Alfred Beati Ngowi, Temitope Omotayo, Lovelin Obi and Julius Akotia
Appl. Sci. 2021, 11(5), 2044; https://0-doi-org.brum.beds.ac.uk/10.3390/app11052044 - 25 Feb 2021
Cited by 15 | Viewed by 4527
Abstract
An identification of strengths, weakness, opportunities, and threats (SWOT) factors remains imperative for enabling a successful Smart Campus transition. The absence of a structured approach for analyzing the relationships between these SWOT factors and the influence thereof on Smart Campus transitions negate effective [...] Read more.
An identification of strengths, weakness, opportunities, and threats (SWOT) factors remains imperative for enabling a successful Smart Campus transition. The absence of a structured approach for analyzing the relationships between these SWOT factors and the influence thereof on Smart Campus transitions negate effective implementation. This study leverages a systems thinking approach to bridge this gap. Data were collected through a stakeholder workshop within a University of Technology case study and analyzed using qualitative content analysis (QCA). This resulted in the establishment of SWOT factors affecting Smart Campus transitions. Systems thinking was utilized to analyze the relationships between these SWOT factors resulting in a causal loop diagram (CLD) highlighting extant interrelationships. A panel of experts drawn from the United Kingdom, New Zealand, and South Africa validated the relationships between the SWOT factors as elucidated in the CLD. Subsequently, a Smart Campus transition framework predicated on the CLD archetypes was developed. The framework provided a holistic approach to understanding the interrelationships between various SWOT factors influencing Smart Campus transitions. This framework remains a valuable tool for facilitating optimal strategic planning and management approaches by policy makers, academics, and implementers within the global Higher Education Institution (HEI) landscape for managing successful Smart Campus transition at the South African University of Technology (SAUoT) and beyond. Full article
(This article belongs to the Special Issue Smart Cities in Applied Sciences)
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