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Algorithms
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Algorithms for Smart Cities (2nd Edition)
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Algorithms for Smart Cities (2nd Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 11701

Special Issue Editors

Dr. Gloria Cerasela Crişan

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Guest Editor
Department of Mathematics and Informatics, Faculty of Sciences, Vasile Alecsandri University of Bacău, 600115 Bacău, Romania
Interests: artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Elena Nechita

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Guest Editor
Department of Mathematics and Informatics, Faculty of Sciences, Vasile Alecsandri University of Bacău, 600115 Bacău, Romania
Interests: artificial intelligence; probability theory; education
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vasile-Daniel Pavaloaia

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Guest Editor
Department of Accounting, Business Information Systems and Statistics, Alexandru Ioan Cuza University of Iasi, 700506 Iași, Romania
Interests: neural networks; machine learning; deep learning; sentiment analysis; IoT systems; information systems for management; enterprise resource planning
Special Issues, Collections and Topics in MDPI journals
Dr. Yajie Zou

E-Mail Website
Guest Editor
Department of Transportation Engineering, Tongji University, Shanghai 200070, China
Interests: traffic safety; intelligent transportation systems; transportation data mining; risk analysis; applications of statistical analysis in transportation

Special Issue Information

Dear Colleagues,

ICT supports our society in responding to increased human pressure on Earth. Sustainable development challenges urban areas to consume resources more efficiently, to optimize operations, to boost people’s involvement in governance, and to raise the quality of life and environment. Late technological, environmental, and social changes determine the need for articulated strategies that address these challenges, comprehensive models of real problems, and effective ICT solutions.

Further, algorithms have the potential to revolutionize urban planning, infrastructure management, and resource allocation. They can help optimize energy consumption, reduce waste generation, and improve transportation systems. Moreover, algorithms can be used to analyze data from various sources, including sensors, social media, and citizen feedback, and to provide insights into urban challenges and opportunities.

The aim of this Special Issue is to address the broad range of societal issues raised by modern urban communities as well as to explore the power of algorithms’ application within the sustainable development of smart cities. The efficient use of physical infrastructure, enhancement of public health and public education, lower environmental impact, and better resilience of the inhabitants as well as of the city structures are the expected topics of interest. Researchers and practitioners working in artificial intelligence, city logistics, internet of things, data analytics, etc., are invited to submit their original and unpublished works to this Special Issue. Of particular interest are papers describing integrated approaches, for example, those including computer vision, optimization methods, GIS, etc.

Dr. Gloria Cerasela Crisan
Prof. Dr. Elena Nechita
Prof. Dr. Vasile-Daniel Pavaloaia
Dr. Yajie Zou
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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
  • city logistics
  • data analytics
  • e-governance
  • e-health
  • image recognition
  • internet of things
  • optimization methods
  • recommender systems
  • artificial neural networks algorithms
  • machine learning algorithms for intelligent software development
  • remote sensing
  • transportation networks
  • smart government
  • smart education
  • smart electronics
  • smart offices

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Related Special Issue

Published Papers (8 papers)

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Research

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14 pages, 1656 KiB  
Article
Utilizing Cell Transmission Models to Alleviate Accident-Induced Traffic Congestion in Two-Way Grid Networks
by Yi-Sheng Huang, Yi-Shun Weng and Chun-Yu Shih
Algorithms 2025, 18(4), 193; https://doi.org/10.3390/a18040193 - 29 Mar 2025
Viewed by 202
Abstract
The Cell Transmission Model (CTM) is a commonly used framework and cost-effective approach for evaluating transportation-related solutions, particularly for analyzing urban traffic congestion, due to its strong mathematical framework. Its effectiveness relies heavily on accuracy, making proper calibration essential for deriving reliable design [...] Read more.
The Cell Transmission Model (CTM) is a commonly used framework and cost-effective approach for evaluating transportation-related solutions, particularly for analyzing urban traffic congestion, due to its strong mathematical framework. Its effectiveness relies heavily on accuracy, making proper calibration essential for deriving reliable design decisions. This study utilizes CTM calibration techniques to design control strategies for mitigating accident-induced traffic congestion in two-way grid networks. By modifying the number of downstream cells and their vehicle capacity, we assess the impact of these adjustments on traffic flow efficiency within the grid structure. Additionally, we utilize MATLAB R2022a to design an intelligent transportation network simulation environment, providing a robust platform for testing and optimizing traffic management strategies specific to two-way grid networks. The findings of this research contribute to the introduction of a novel refinement to the traditional CTM by dividing only cell 9 into three smaller cells to accurately capture different movement directions, enhancing intersection modeling without increasing overall computational complexity. Full article
(This article belongs to the Special Issue Algorithms for Smart Cities (2nd Edition))
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23 pages, 25753 KiB  
Article
A Lightweight Deep Learning Approach for Detecting External Intrusion Signals from Optical Fiber Sensing System Based on Temporal Efficient Residual Network
by Yizhao Wang, Ziye Guo, Haitao Luo, Jing Liu and Ruohua Zhou
Algorithms 2025, 18(2), 101; https://doi.org/10.3390/a18020101 - 11 Feb 2025
Viewed by 615
Abstract
Deep neural networks have been widely applied to fiber optic sensor systems, where the detection of external intrusion in metro tunnels is a major challenge; thus, how to achieve the optimal balance between resource consumption and accuracy is a critical issue. To address [...] Read more.
Deep neural networks have been widely applied to fiber optic sensor systems, where the detection of external intrusion in metro tunnels is a major challenge; thus, how to achieve the optimal balance between resource consumption and accuracy is a critical issue. To address this issue, we propose a lightweight deep learning model, the Temporal Efficient Residual Network (TEResNet), for the detection of anomalous intrusion. In contrast to the majority of two-dimensional convolutional approaches, which require a deep architecture to encompass both low- and high-frequency domains, our methodology employs temporal convolutions and a compact residual network architecture. This allows the model to incorporate lower-level features into the higher-level feature formation in subsequent layers, leveraging informative features from the lower layers, and thus reducing the number of stacked layers for generating high-level features. As a result, the model achieves a superior performance with a relatively small number of layers. Moreover, the two-dimensional feature map is reduced in size to reduce the computational burden without adding parameters. This is crucial for enabling rapid intrusion detection. Experiments were conducted in the construction environment of the Guangzhou Metro, resulting in the creation of a dataset containing 6948 signal segments, which is publicly accessible. The results demonstrate that TEResNet outperforms the existing intrusion detection methods and advanced deep learning networks, achieving an accuracy of 97.12% and an F1 score of 96.15%. With only 48,009 learnable parameters, it provides an efficient and reliable solution for intrusion detection in metro tunnels, aligning with the growing demand for lightweight and robust information processing systems. Full article
(This article belongs to the Special Issue Algorithms for Smart Cities (2nd Edition))
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26 pages, 6386 KiB  
Article
Spatial Intelligence in E-Commerce: Integrating Mobile Agents with GISs for a Dynamic Recommendation System
by Mohamed Shili, Salah Hammedi and Mahmoud Elkhodr
Algorithms 2025, 18(1), 28; https://doi.org/10.3390/a18010028 - 7 Jan 2025
Viewed by 853
Abstract
The evolving capabilities of Geographic Information Systems (GISs) are transforming various industries, including e-commerce, by providing enhanced spatial analysis and precision in customer targeting, and improving the ability of recommender systems. This paper proposes a novel framework that integrates mobile agents with GISs [...] Read more.
The evolving capabilities of Geographic Information Systems (GISs) are transforming various industries, including e-commerce, by providing enhanced spatial analysis and precision in customer targeting, and improving the ability of recommender systems. This paper proposes a novel framework that integrates mobile agents with GISs to deliver real-time, personalized recommendations in e-commerce. By utilizing the OpenStreetMap API for geographic mapping and the Java Agent Development Environment (JADE) platform for mobile agents, the system leverages both geospatial data and customer preferences to offer highly relevant product suggestions based on location and behaviour. Mobile agents enable real-time data collection, processing, and interaction with customers, facilitating dynamic adaptations to their needs. The combination of GISs and mobile agents enhances the system’s ability to analyze spatial data, providing tailored recommendations that align with user preferences and geographic context. This integrated approach not only improves the online shopping experience but also introduces new opportunities for location-specific marketing strategies, boosting the effectiveness of targeted advertising. The validation of this system highlights its potential to significantly enhance customer engagement and satisfaction through context-aware recommendations. The integration of GISs and mobile agents lays a strong foundation for future advancements in personalized e-commerce solutions, offering a scalable model for businesses looking to optimize marketing efforts and customer experiences. Full article
(This article belongs to the Special Issue Algorithms for Smart Cities (2nd Edition))
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26 pages, 1185 KiB  
Article
Energy Consumption Outlier Detection with AI Models in Modern Cities: A Case Study from North-Eastern Mexico
by José-Alberto Solís-Villarreal, Valeria Soto-Mendoza, Jesús Alejandro Navarro-Acosta and Efraín Ruiz-y-Ruiz
Algorithms 2024, 17(8), 322; https://doi.org/10.3390/a17080322 - 24 Jul 2024
Cited by 2 | Viewed by 2250
Abstract
The development of smart cities will require the construction of smart buildings. Smart buildings will demand the incorporation of elements for efficient monitoring and control of electrical consumption. The development of efficient AI algorithms is needed to generate more accurate electricity consumption predictions; [...] Read more.
The development of smart cities will require the construction of smart buildings. Smart buildings will demand the incorporation of elements for efficient monitoring and control of electrical consumption. The development of efficient AI algorithms is needed to generate more accurate electricity consumption predictions; therefore; anomaly detection in electricity consumption predictions has become an important research topic. This work focuses on the study of the detection of anomalies in domestic electrical consumption in Mexico. A predictive machine learning model of future electricity consumption was generated to evaluate various anomaly-detection techniques. Their effectiveness in identifying outliers was determined, and their performance was documented. A 30-day forecast of electrical consumption and an anomaly-detection model have been developed using isolation forest. Isolation forest successfully captured up to 75% of the anomalies. Finally, the Shapley values have been used to generate an explanation of the results of a model capable of detecting anomalous data for the Mexican context. Full article
(This article belongs to the Special Issue Algorithms for Smart Cities (2nd Edition))
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Review

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25 pages, 653 KiB  
Review
Algorithms Facilitating the Observation of Urban Residential Vacancy Rates: Technologies, Challenges and Breakthroughs
by Binglin Liu, Weijia Zeng, Weijiang Liu, Yi Peng and Nini Yao
Algorithms 2025, 18(3), 174; https://doi.org/10.3390/a18030174 - 20 Mar 2025
Viewed by 349
Abstract
In view of the challenges brought by a complex environment, diverse data sources and urban development needs, our study comprehensively reviews the application of algorithms in urban residential vacancy rate observation. First, we explore the definition and measurement of urban residential vacancy rate, [...] Read more.
In view of the challenges brought by a complex environment, diverse data sources and urban development needs, our study comprehensively reviews the application of algorithms in urban residential vacancy rate observation. First, we explore the definition and measurement of urban residential vacancy rate, pointing out the difficulties in accurately defining vacant houses and obtaining reliable data. Then, we introduce various algorithms such as traditional statistical learning, machine learning, deep learning and ensemble learning, and analyze their applications in vacancy rate observation. The traditional statistical learning algorithm builds a prediction model based on historical data mining and analysis, which has certain advantages in dealing with linear problems and regular data. However, facing the high nonlinear relationships and complexity of the data in the urban residential vacancy rate observation, its prediction accuracy is difficult to meet the actual needs. With their powerful nonlinear modeling ability, machine learning algorithms have significant advantages in capturing the nonlinear relationships of data. However, they require high data quality and are prone to overfitting phenomenon. Deep learning algorithms can automatically learn feature representation, perform well in processing large amounts of high-dimensional and complex data, and can effectively deal with the challenges brought by various data sources, but the training process is complex and the computational cost is high. The ensemble learning algorithm combines multiple prediction models to improve the prediction accuracy and stability. By comparing these algorithms, we can clarify the advantages and adaptability of different algorithms in different scenarios. Facing the complex environment, the data in the observation of urban residential vacancy rate are affected by many factors. The unbalanced urban development leads to significant differences in residential vacancy rates in different areas. Spatiotemporal heterogeneity means that vacancy rates vary in different geographical locations and over time. The complexity of data affected by various factors means that the vacancy rate is jointly affected by macroeconomic factors, policy regulatory factors, market supply and demand factors and individual resident factors. These factors are intertwined, increasing the complexity of data and the difficulty of analysis. In view of the diversity of data sources, we discuss multi-source data fusion technology, which aims to integrate different data sources to improve the accuracy of vacancy rate observation. The diversity of data sources, including geographic information system (GIS) (Geographic Information System) data, remote sensing images, statistics data, social media data and urban grid management data, requires integration in format, scale, precision and spatiotemporal resolution through data preprocessing, standardization and normalization. The multi-source data fusion algorithm should not only have the ability of intelligent feature extraction and related analysis, but also deal with the uncertainty and redundancy of data to adapt to the dynamic needs of urban development. We also elaborate on the optimization methods of algorithms for different data sources. Through this study, we find that algorithms play a vital role in improving the accuracy of vacancy rate observation and enhancing the understanding of urban housing conditions. Algorithms can handle complex spatial data, integrate diverse data sources, and explore the social and economic factors behind vacancy rates. In the future, we will continue to deepen the application of algorithms in data processing, model building and decision support, and strive to provide smarter and more accurate solutions for urban housing management and sustainable development. Full article
(This article belongs to the Special Issue Algorithms for Smart Cities (2nd Edition))
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21 pages, 257 KiB  
Review
A Review of Multi-Source Data Fusion and Analysis Algorithms in Smart City Construction: Facilitating Real Estate Management and Urban Optimization
by Binglin Liu, Qian Li, Zhihua Zheng, Yanjia Huang, Shuguang Deng, Qiongxiu Huang and Weijiang Liu
Algorithms 2025, 18(1), 30; https://doi.org/10.3390/a18010030 - 8 Jan 2025
Cited by 3 | Viewed by 1950
Abstract
In the context of the booming construction of smart cities, multi-source data fusion and analysis algorithms play a key role in optimizing real estate management and improving urban efficiency. In this review, we comprehensively and systematically review the relevant algorithms, covering the types, [...] Read more.
In the context of the booming construction of smart cities, multi-source data fusion and analysis algorithms play a key role in optimizing real estate management and improving urban efficiency. In this review, we comprehensively and systematically review the relevant algorithms, covering the types, characteristics, fusion techniques, analysis algorithms, and their synergies of multi-source data. We found that multi-source data, including sensors, social media, citizen feedback, and GIS data, face challenges such as data quality and privacy security when being fused. Data fusion algorithms are diverse and have their own advantages and disadvantages. Data analysis algorithms help urban management in areas such as spatial analysis and deep learning. Algorithm collaboration can improve decision-making accuracy and efficiency and promote the rational allocation of urban resources. In the future, algorithm development will focus on data quality, real-time, deep mining, interdisciplinary research, privacy protection, and collaborative application expansion, providing strong support for the sustainable development of smart cities. Full article
(This article belongs to the Special Issue Algorithms for Smart Cities (2nd Edition))
22 pages, 2446 KiB  
Review
A Comprehensive Review of Autonomous Driving Algorithms: Tackling Adverse Weather Conditions, Unpredictable Traffic Violations, Blind Spot Monitoring, and Emergency Maneuvers
by Cong Xu and Ravi Sankar
Algorithms 2024, 17(11), 526; https://doi.org/10.3390/a17110526 - 15 Nov 2024
Cited by 3 | Viewed by 3478
Abstract
With the rapid development of autonomous driving technology, ensuring the safety and reliability of vehicles under various complex and adverse conditions has become increasingly important. Although autonomous driving algorithms perform well in regular driving scenarios, they still face significant challenges when dealing with [...] Read more.
With the rapid development of autonomous driving technology, ensuring the safety and reliability of vehicles under various complex and adverse conditions has become increasingly important. Although autonomous driving algorithms perform well in regular driving scenarios, they still face significant challenges when dealing with adverse weather conditions, unpredictable traffic rule violations (such as jaywalking and aggressive lane changes), inadequate blind spot monitoring, and emergency handling. This review aims to comprehensively analyze these critical issues, systematically review current research progress and solutions, and propose further optimization suggestions. By deeply analyzing the logic of autonomous driving algorithms in these complex situations, we hope to provide strong support for enhancing the safety and reliability of autonomous driving technology. Additionally, we will comprehensively analyze the limitations of existing driving technologies and compare Advanced Driver Assistance Systems (ADASs) with Full Self-Driving (FSD) to gain a thorough understanding of the current state and future development directions of autonomous driving technology. Full article
(This article belongs to the Special Issue Algorithms for Smart Cities (2nd Edition))
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11 pages, 205 KiB  
Review
The Current State and Future of the Urban Cold Chain: A Review of Algorithms for Environmental Optimization
by Isla Usvakangas, Ronja Tuovinen and Pekka Neittaanmäki
Algorithms 2024, 17(10), 465; https://doi.org/10.3390/a17100465 - 18 Oct 2024
Cited by 1 | Viewed by 1692
Abstract
Cold chains are essential in providing people with food and medicine across the globe. As the global environmental crisis poses an existential threat to humanity and societies strive for more sustainable ways of life, these critically important systems need to adapt to the [...] Read more.
Cold chains are essential in providing people with food and medicine across the globe. As the global environmental crisis poses an existential threat to humanity and societies strive for more sustainable ways of life, these critically important systems need to adapt to the needs of a new era. As it is, the transportation sector as a whole accounts for a fifth of global emissions, with the cold chain being embedded in this old fossil-fuel-dependent infrastructure. With the EU is passing regulations and legislation to cut down on emissions and phase out polluting technologies like combustion engine vehicles, the next couple of decades in Europe will be defined by rapid infrastructural change. For logistics and cold transportation, this shift presents many opportunities but also highlights the need for innovation and new research. In this literature review, we identify pressing issues with the current urban cold chain, review the recent research around environmental optimization in urban logistics, and give a cross-section of the field: what the trending research topics in urban logistics optimization across the globe are, and what kind of blind spots are identifiable in the body of research, as well as changes arising with future green logistics infrastructure. We approach the issues discussed specifically from the point of view of refrigerated urban transportation, though many issues extend beyond it to transportation infrastructure at large. Full article
(This article belongs to the Special Issue Algorithms for Smart Cities (2nd Edition))
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