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Artificial Intelligence and the Future of Smart Cities
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Artificial Intelligence and the Future of Smart Cities

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 November 2024) | Viewed by 6948

Special Issue Editors

Prof. Dr. Amjad Umar

E-Mail Website
Guest Editor
Information Systems Engineering and Management, Harrisburg University of Science and Technology, Harrisburg, PA, USA
Interests: virtual reality; smart city; social media
Dr. Ali Jalooli

E-Mail Website
Guest Editor
Department of Computer Science, California State University, Dominguez Hills, Carson, CA, USA
Interests: AI policy and smart cities; urban planning; internet of things and heterogeneous wireless networks; cloud/edge computing; machine learning; cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue titled 'Artificial Intelligence and the Future of Smart Cities' explores the transformative role of AI in enhancing urban environments. As cities worldwide strive for smarter, more sustainable development, AI emerges as a pivotal force in orchestrating complex urban systems and services. This Special Issue aims to gather cutting-edge research that showcases AI-driven innovations in urban planning, infrastructure management, environmental monitoring, and public safety. Contributions will demonstrate how AI technologies such as machine learning, data analytics, and IoT integration not only optimize city operations but also improve quality of life and reduce ecological footprints. By highlighting diverse implementations from global contexts, this Issue will offer insights into challenges, technological advancements, and policy frameworks shaping the future of smart cities. It seeks to spur academic discourse and guide practical implementations that propel cities towards more adaptive, resilient, and intelligent futures.

Prof. Dr. Amjad Umar
Dr. Ali Jalooli
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

  • artificial intelligence
  • smart cities
  • urban planning
  • sustainable development
  • IoT in urban contexts
  • AI-driven infrastructure management
  • environmental monitoring
  • public safety technology
  • data analytics in urban management
  • AI policy and smart cities

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Published Papers (4 papers)

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Research

17 pages, 3740 KiB  
Article
Self-Adaptive Clustering Model Based on Variable Time-Series Similarity Measure Analysis for V2G Electricity Price Prediction
by Tie Hua Zhou, Xirao Xun, Ling Wang, Gongliang Hu, Wei Ding and Lei Kou
Appl. Sci. 2025, 15(4), 2069; https://doi.org/10.3390/app15042069 - 16 Feb 2025
Viewed by 506
Abstract
Data with time attributions such as price, load, and stock, which directly reflect the variation tendency, are the most common type of data character available. However, it is difficult to predict complex and volatile time-series character data. Further, most density cluster methods employ [...] Read more.
Data with time attributions such as price, load, and stock, which directly reflect the variation tendency, are the most common type of data character available. However, it is difficult to predict complex and volatile time-series character data. Further, most density cluster methods employ existing data to train the initial radius; however, a certain density radius is hard to be made suitable for continuously generated on-going datasets. Therefore, how to select a suitable timespan according to the time-series character in a way that makes it possible to support an adaptive updated density radius for real-time calculation is a core process. In this paper, a self-adaptive multi-density (SAMD) prediction model is proposed for solving the dynamic density radius selection problem in time-series data so as to improve the accuracy of real-time prediction. This multi-density clustering method can effectively shorten the iteration times and achieve dynamic clustering by the proposed jump sequence, which can optimize the jump points in the electricity price sequence. Moreover, we especially focus on the time interval features and other multi-source influencing factors together to construct the multi-core function with double-layer optimization to calculate the weighted coefficients, which have good adaptability and improve the classification and recognition performance. The experimental results show that the model had higher prediction accuracy and reduced processing time consumption in order to achieve real-time prediction. Full article
(This article belongs to the Special Issue Artificial Intelligence and the Future of Smart Cities)
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20 pages, 26727 KiB  
Article
A Supervised Approach for Land Use Identification in Trento Using Mobile Phone Data as an Alternative to Unsupervised Clustering Techniques
by Manuel Mendoza-Hurtado, Gonzalo Cerruela-García and Domingo Ortiz-Boyer
Appl. Sci. 2025, 15(4), 1753; https://doi.org/10.3390/app15041753 - 9 Feb 2025
Viewed by 728
Abstract
This study explores land use classification in Trento using supervised learning techniques combined with call detail records (CDRs) as a proxy for human activity. Located in an alpine environment, Trento presents unique geographic challenges, including varied terrain and sparse network coverage, making it [...] Read more.
This study explores land use classification in Trento using supervised learning techniques combined with call detail records (CDRs) as a proxy for human activity. Located in an alpine environment, Trento presents unique geographic challenges, including varied terrain and sparse network coverage, making it an ideal case for testing the robustness of supervised learning approaches. By analyzing spatiotemporal patterns in CDRs, we trained and evaluated several classification algorithms, including k-nearest neighbors (kNN), support vector machines (SVM), and random forests (RF), to map land use categories, such as home, work, and forest. A comparative analysis highlights the performance of each method, emphasizing the strengths of RF in capturing complex patterns, its good generalization ability, and the usage of kNN with different distance measures. Our supervised machine-learning approach outperforms unsupervised clustering techniques by capturing complex patterns and achieving higher accuracy. Results demonstrate the potential of CDRs for urban planning, offering a cost-effective approach for fine-grained land use monitoring with the particularities of Trento, as its landscape combines urban areas, agricultural fields, and forested regions, reflecting its alpine setting, in contrast with other metropolitan regions. Full article
(This article belongs to the Special Issue Artificial Intelligence and the Future of Smart Cities)
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17 pages, 1086 KiB  
Article
On the Use of Machine Learning and Key Performance Indicators for Urban Planning and Design
by Majsa Ammouriova, Veronika Tsertsvadze, Angel A. Juan, Trinidad Fernandez and Leon Kapetas
Appl. Sci. 2024, 14(20), 9501; https://doi.org/10.3390/app14209501 - 17 Oct 2024
Viewed by 2002
Abstract
Global efforts to achieve climate neutrality increasingly rely on innovative urban planning and design strategies. This study focuses on the identification and application of key performance indicators (KPIs) to support policymakers and local authorities in driving sustainable urban transitions. Using a real-life case [...] Read more.
Global efforts to achieve climate neutrality increasingly rely on innovative urban planning and design strategies. This study focuses on the identification and application of key performance indicators (KPIs) to support policymakers and local authorities in driving sustainable urban transitions. Using a real-life case study of European cities and countries, this research leverages data analytics and machine learning to inform decision-making processes. Specifically, the k-means clustering algorithm was employed to group countries based on socioeconomic and environmental KPIs, while principal component analysis was used to rank the most influential indicators in shaping these clusters. The analysis highlighted GDP per capita, corruption perception, and climate-related expenditure as key drivers of clustering. Additionally, time series analysis of KPI trends demonstrated the impact of policy decisions over time. This study showcases how machine learning and data-driven approaches can provide valuable insights for urban planners, offering a robust framework for evaluating and improving climate-neutrality strategies at both city and country levels. Full article
(This article belongs to the Special Issue Artificial Intelligence and the Future of Smart Cities)
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14 pages, 5936 KiB  
Article
GeoLocator: A Location-Integrated Large Multimodal Model (LMM) for Inferring Geo-Privacy
by Yifan Yang, Siqin Wang, Daoyang Li, Shuju Sun and Qingyang Wu
Appl. Sci. 2024, 14(16), 7091; https://doi.org/10.3390/app14167091 - 13 Aug 2024
Cited by 3 | Viewed by 2524
Abstract
To ensure the sustainable development of artificial intelligence (AI) application in urban and geospatial science, it is important to protect the geographic privacy, or geo-privacy, which refers to an individual’s geographic location details. As a crucial aspect of personal security, geo-privacy plays a [...] Read more.
To ensure the sustainable development of artificial intelligence (AI) application in urban and geospatial science, it is important to protect the geographic privacy, or geo-privacy, which refers to an individual’s geographic location details. As a crucial aspect of personal security, geo-privacy plays a key role not only in individual protection but also in maintaining ethical standards in geoscientific practices. Despite its importance, geo-privacy is often not sufficiently addressed in daily activities. With the increasing use of large multimodal models (LMMs) such as GPT-4 for open-source intelligence (OSINT), the risks related to geo-privacy breaches have significantly escalated. This study introduces a novel GPT-4-based model, GeoLocator, integrated with location capabilities, and conducts four experiments to evaluate its ability to accurately infer location information from images and social media content. The results demonstrate that GeoLocator can generate specific geographic details with high precision, thereby increasing the potential for inadvertent exposure of sensitive geospatial information. This highlights the dual challenges posed by online data-sharing and information-gathering technologies in the context of geo-privacy. We conclude with a discussion on the broader impacts of GeoLocator and our findings on individuals and communities, emphasizing the urgent need for increased awareness and protective measures against geo-privacy breaches in the era of advancing AI and widespread social media usage. This contribution thus advocates for sustainable and responsible geoscientific practices. Full article
(This article belongs to the Special Issue Artificial Intelligence and the Future of Smart Cities)
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