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Smart Cities, Volume 8, Issue 1 (February 2025) – 33 articles

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33 pages, 861 KiB  
Article
Secure Electric Vehicle Charging Infrastructure in Smart Cities: A Blockchain-Based Smart Contract Approach
by Abdullahi Chowdhury, Sakib Shahriar Shafin, Saleh Masum, Joarder Kamruzzaman and Shi Dong
Smart Cities 2025, 8(1), 33; https://doi.org/10.3390/smartcities8010033 (registering DOI) - 15 Feb 2025
Abstract
Increasing adoption of electric vehicles (EVs) and the expansion of EV charging infrastructure present opportunities for enhancing sustainable transportation within smart cities. However, the interconnected nature of EV charging stations (EVCSs) exposes this infrastructure to various cyber threats, including false data injection, man-in-the-middle [...] Read more.
Increasing adoption of electric vehicles (EVs) and the expansion of EV charging infrastructure present opportunities for enhancing sustainable transportation within smart cities. However, the interconnected nature of EV charging stations (EVCSs) exposes this infrastructure to various cyber threats, including false data injection, man-in-the-middle attacks, malware intrusions, and denial of service attacks. Financial attacks, such as false billing and theft of credit card information, also pose significant risks to EV users. In this work, we propose a Hyperledger Fabric-based blockchain network for EVCSs to mitigate these risks. The proposed blockchain network utilizes smart contracts to manage key processes such as authentication, charging session management, and payment verification in a secure and decentralized manner. By detecting and mitigating malicious data tampering or unauthorized access, the blockchain system enhances the resilience of EVCS networks. A comparative analysis of pre- and post-implementation of the proposed blockchain network demonstrates how it thwarts current cyberattacks in the EVCS infrastructure. Our analyses include performance metrics using the benchmark Hyperledger Caliper test, which shows the proposed solution’s low latency for real-time operations and scalability to accommodate the growth of EV infrastructure. Deployment of this blockchain-enhanced security mechanism will increase user trust and reliability in EVCS systems. Full article
24 pages, 17666 KiB  
Review
What Have Urban Digital Twins Contributed to Urban Planning and Decision Making? From a Systematic Literature Review Toward a Socio-Technical Research and Development Agenda
by Shervin Azadi, Dena Kasraian, Pirouz Nourian and Pieter van Wesemael
Smart Cities 2025, 8(1), 32; https://doi.org/10.3390/smartcities8010032 - 13 Feb 2025
Abstract
Urban digital twins (UDTs) were first discussed in 2018. Seven years later, we ask: What has been their contribution to urban planning and decision making so far? Here, we systematically review 88 peer-reviewed articles to map and compare UDTs’ ambitions with their realized [...] Read more.
Urban digital twins (UDTs) were first discussed in 2018. Seven years later, we ask: What has been their contribution to urban planning and decision making so far? Here, we systematically review 88 peer-reviewed articles to map and compare UDTs’ ambitions with their realized contributions. Our results indicate that despite the vast technical developments, socio-technical challenges have remained largely unaddressed, causing many of UDTs’ ambitions to remain unrealized. We identify three categories in these socio-technical challenges: interdisciplinary integration (II), consensual contextualization (CC), and procedural operationalization (PO). Accordingly, we consolidate a socio-technical research and development agenda to realize the ambitions of UDTs for urban planning and decision making: Augmented Urban Planning (AUP). Full article
(This article belongs to the Collection Digital Twins for Smart Cities)
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20 pages, 7549 KiB  
Article
Development of a Delivery Time-Period Selection Model for Urban Freight Using GPS Data
by Ryota Kodera, Takanori Sakai and Tetsuro Hyodo
Smart Cities 2025, 8(1), 31; https://doi.org/10.3390/smartcities8010031 - 13 Feb 2025
Abstract
Developing policy instruments related to urban freight, such as congestion pricing, urban consolidation schemes, and off-hours delivery, requires an understanding of the distribution of shipment delivery times. Furthermore, agent-based urban freight simulators use relevant information (shipment delivery time distribution or vehicle tour start [...] Read more.
Developing policy instruments related to urban freight, such as congestion pricing, urban consolidation schemes, and off-hours delivery, requires an understanding of the distribution of shipment delivery times. Furthermore, agent-based urban freight simulators use relevant information (shipment delivery time distribution or vehicle tour start time distribution) as input to simulate tour generation. However, studies focusing on shipment delivery time-period selection modeling are very limited. In this study, we propose a method using GPS trajectory data from the Tokyo Metropolitan Area to estimate a shipment delivery time-period selection model based on pseudo-shipment records inferred from GPS data. The results indicate that shipment distance, size, and destination attributes can explain the delivery times of goods. Moreover, we demonstrate the practicality of the model by comparing the simulation result with the observed data for three areas with distinct characteristics, concluding that the model could be applied to urban freight simulation models for accurately reproducing spatial heterogeneity in shipment delivery time periods. This study contributes to promoting smart city development and management by proposing a method to use big data to better understand deliveries and support the development of relevant advanced city logistics solutions. Full article
(This article belongs to the Special Issue City Logistics and Smart Cities: Models, Approaches and Planning)
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15 pages, 3263 KiB  
Article
Smart Green Energy Management for Campus: An Integrated Machine Learning and Reinforcement Learning Model
by Charan Teja Madabathula, Kunal Agrawal, Vijen Mehta, Swathi Kasarabada, Sai Srimai Kommamuri, Guannan Liu and Jerry Gao
Smart Cities 2025, 8(1), 30; https://doi.org/10.3390/smartcities8010030 - 13 Feb 2025
Abstract
The increasing demand for energy efficiency and the integration of renewable energy sources have become crucial for sustainability in modern campuses. This work presents a smart green energy management system (SGEMS) that integrates a machine learning model and reinforcement learning (RL) to optimize [...] Read more.
The increasing demand for energy efficiency and the integration of renewable energy sources have become crucial for sustainability in modern campuses. This work presents a smart green energy management system (SGEMS) that integrates a machine learning model and reinforcement learning (RL) to optimize energy consumption and solar generation across a green campus. Using historical data from three campus buildings, we developed a predictive model to forecast short-term energy consumption and solar generation. The XGBoost algorithm, combined with RL, demonstrated superior performance in predicting energy consumption and generation, outperforming other models with a root mean square error (RMSE) of 14.72, a mean absolute error (MAE) of 12.00, and a mean absolute percentage error (MAPE) of 2.18%. This work proposes a web-based interface for real-time energy monitoring and decision-making, helping users forecast power shortages and manage energy usage effectively. The proposed approach provides a scalable solution for campuses aiming to reduce reliance on external grids and increase energy efficiency, setting a benchmark for future green campus initiatives. Full article
(This article belongs to the Special Issue Big Data and AI Services for Sustainable Smart Cities)
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31 pages, 14820 KiB  
Article
Digital Transformation in African Heritage Preservation: A Digital Twin Framework for a Sustainable Bab Al-Mansour in Meknes City, Morocco
by Imane Serbouti, Jérôme Chenal, Saâd Abdesslam Tazi, Ahmad Baik and Mustapha Hakdaoui
Smart Cities 2025, 8(1), 29; https://doi.org/10.3390/smartcities8010029 - 12 Feb 2025
Abstract
The advent of digital transformation has redefined the preservation of cultural heritage and historic sites through the integration of Digital Twin technology. Initially developed for industrial applications, Digital Twins are now increasingly employed in heritage conservation as dynamic, digital replicas of physical assets [...] Read more.
The advent of digital transformation has redefined the preservation of cultural heritage and historic sites through the integration of Digital Twin technology. Initially developed for industrial applications, Digital Twins are now increasingly employed in heritage conservation as dynamic, digital replicas of physical assets and environments. These systems enable detailed, interactive approaches to documentation, management, and preservation. This paper presents a detailed framework for implementing Digital Twin technology in the management of heritage buildings. By utilizing advanced methods for data collection, processing, and analysis, the framework creates a robust data hub for Digital Twin Heritage Buildings (DTHB). This architecture enhances real-time monitoring, improves accuracy, reduces operational costs, and enables predictive maintenance while minimizing invasive inspections. Focusing on Bab Al-Mansour Gate in Meknes, Morocco, a significant cultural landmark, this research outlines the workflow for developing a Bab Al-Mansour DTHB platform. The platform monitors structural health and detects damage over time, offering a dynamic tool for conservation planning. By integrating innovative technologies with data-driven solutions, this study provides a replicable model for preserving heritage sites, addressing critical gaps in real-time monitoring, resource optimization, and environmental risk mitigation. Full article
(This article belongs to the Collection Digital Twins for Smart Cities)
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21 pages, 8043 KiB  
Article
AI Agent-Based Intelligent Urban Digital Twin (I-UDT): Concept, Methodology, and Case Studies
by Sebin Choi and Sungmin Yoon
Smart Cities 2025, 8(1), 28; https://doi.org/10.3390/smartcities8010028 - 11 Feb 2025
Abstract
The concept of digital twins (DTs) has expanded to encompass buildings and cities, with urban building energy modeling (UBEM) playing a crucial role in predicting urban-scale energy consumption via modeling individual energy use and interactions. As a virtual model within urban digital twins [...] Read more.
The concept of digital twins (DTs) has expanded to encompass buildings and cities, with urban building energy modeling (UBEM) playing a crucial role in predicting urban-scale energy consumption via modeling individual energy use and interactions. As a virtual model within urban digital twins (UDTs), UBEM offers the potential for managing energy in sustainable cities. However, UDTs face challenges with regard to integrating large-scale data and relying on bottom-up UBEM approaches. In this study, we propose an AI agent-based intelligent urban digital twin (I-UDT) to enhance DTs’ technical realization and UBEM’s service functionality. Integrating GPT within the UDT enabled the efficient integration of fragmented city-scale data and the extraction of building features, addressing the limitations of the service realization of traditional UBEM. This framework ensures continuous updates of the virtual urban model and the streamlined provision of updated information to users in future studies. This research establishes the concept of an I-UDT and lays a foundation for future implementations. The case studies include (1) data analysis, (2) prediction, (3) feature engineering, and (4) information services for 3500 buildings in Seoul. Through these case studies, the I-UDT was integrated and analyzed scattered data, predicted energy consumption, derived conditioned areas, and evaluated buildings on benchmark. Full article
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29 pages, 13238 KiB  
Article
Spatial Insights for Building Resilience: The Territorial Risk Management & Analysis Across Scale Framework for Bridging Scales in Multi-Hazard Assessment
by Francesca Maria Ugliotti, Muhammad Daud and Emmanuele Iacono
Smart Cities 2025, 8(1), 27; https://doi.org/10.3390/smartcities8010027 - 11 Feb 2025
Abstract
In an era of increasingly abundant and granular spatial and temporal data, the traditional divide between environmental GIS and building-centric BIM scales is diminishing, offering an opportunity to enhance natural hazard assessment by bridging the gap between territorial impacts and the effects on [...] Read more.
In an era of increasingly abundant and granular spatial and temporal data, the traditional divide between environmental GIS and building-centric BIM scales is diminishing, offering an opportunity to enhance natural hazard assessment by bridging the gap between territorial impacts and the effects on individual structures. This study addresses the challenge of integrating disparate data formats by establishing a centralised database as the foundation for a comprehensive risk assessment approach. A use case focusing on flood risk assessment for a public building in northwest Italy demonstrates the practical implications of this integrated methodology. The proposed TErritorial RIsk Management & Analysis Across Scale (TERIMAAS) framework utilises this centralised repository to store, process, and dynamically update diverse BIM and GIS datasets, incorporating real-time IoT-derived information. The GIS spatial analysis assesses risk scores for each hazard type, providing insights into vulnerability and potential impacts. BIM data further refine this assessment by incorporating building and functional characteristics, enabling a comprehensive evaluation of resilience and risk mitigation strategies tailored to dynamic environmental conditions across scales. The results of the proposed scalable approach could provide a valuable understanding of the territory for policymakers, urban planners, and any stakeholder involved in disaster risk management and infrastructure resilience planning. Full article
(This article belongs to the Section Smart Buildings)
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15 pages, 1457 KiB  
Perspective
Towards Nature-Positive Smart Cities: Bridging the Gap Between Technology and Ecology
by Alessio Russo
Smart Cities 2025, 8(1), 26; https://doi.org/10.3390/smartcities8010026 - 10 Feb 2025
Abstract
In the biodiversity and climate emergency, a holistic approach is needed for the development of smart cities. This perspective paper proposed a novel conceptual framework for nature-positive smart cities in a socio-technical-ecological system (STES), which bridged the gap between technological advancement and ecological [...] Read more.
In the biodiversity and climate emergency, a holistic approach is needed for the development of smart cities. This perspective paper proposed a novel conceptual framework for nature-positive smart cities in a socio-technical-ecological system (STES), which bridged the gap between technological advancement and ecological principles within the existing smart city approach, enabling cities to transition towards a biodiversity-led paradigm. Based on recent literature on smart cities and nature-positive cities, this framework combines the principles of nature-positive cities and smart cities with the technological capabilities of Nature 4.0, using tools such as AI, sensors, IoT, big data analytics, and machine learning. The literature shows that smart green spaces have already been developed worldwide; therefore, education is needed for personnel working in local government to effectively use this new technology. The paper presents examples of how smart technologies can be utilised within urban green spaces to maximise ecosystem services and biodiversity. Finally, it provides recommendations and areas for future research, concluding with a call for specific policy interventions to facilitate the transition towards nature-positive smart cities. Full article
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37 pages, 931 KiB  
Review
Advances in Traffic Congestion Prediction: An Overview of Emerging Techniques and Methods
by Aristeidis Mystakidis, Paraskevas Koukaras and Christos Tjortjis
Smart Cities 2025, 8(1), 25; https://doi.org/10.3390/smartcities8010025 - 7 Feb 2025
Abstract
The ongoing increase in urban populations has resulted in the enduring issue of traffic congestion, adversely affecting the quality of life, including commute duration, road safety, and local air quality. Consequently, recognizing and forecasting underlying traffic congestion patterns have become essential, with Traffic [...] Read more.
The ongoing increase in urban populations has resulted in the enduring issue of traffic congestion, adversely affecting the quality of life, including commute duration, road safety, and local air quality. Consequently, recognizing and forecasting underlying traffic congestion patterns have become essential, with Traffic Congestion Prediction (TCP) emerging as an increasingly significant area of study. Advancements in Machine Learning (ML) and Artificial Intelligence (AI), as well as improvements in Internet of Things (IoT) sensor technologies have made TCP research crucial to the development of Intelligent Transportation Systems (ITSs). This review examines advanced TCP, emphasizing innovative forecasting methods and technologies and their importance for the ITS sector. This paper provides an overview of statistical, ML, Deep Learning (DL) approaches, and their ensembles that compose TCP. We examine several forecasting methods and discuss relative and absolute evaluation metrics from regression and classification perspectives. Finally, we present an overall step-by-step standard methodology that is often utilized in TCP problems. By combining these elements, this review highlights critical advancements and ongoing challenges in TCP, providing robust and detailed information for state-of-the-art ITS solutions. Full article
(This article belongs to the Section Smart Transportation)
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34 pages, 1811 KiB  
Review
Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
by Halah Alabdouli, Mohamed S. Hassan and Akmal Abdelfatah
Smart Cities 2025, 8(1), 24; https://doi.org/10.3390/smartcities8010024 - 6 Feb 2025
Abstract
Due to its anticipated impacts on the performance of transportation systems, intelligent transport systems (ITS) have emerged as one of the most extensively investigated topics. The U.S. Department of Transportation has defined route guidance systems (RGSs) as one of the main categories within [...] Read more.
Due to its anticipated impacts on the performance of transportation systems, intelligent transport systems (ITS) have emerged as one of the most extensively investigated topics. The U.S. Department of Transportation has defined route guidance systems (RGSs) as one of the main categories within ITS. Systems like these are essential components when managing travel and transportation. While RGSs play a pivotal role in both present and future transportation, there has been limited research on evaluating the effectiveness and dependability of integrating them with vehicular communication frameworks. Therefore, this paper aims to evaluate the RGS architectures proposed to date in the literature, providing comparisons and classifications based on their structures and requirements for communication systems. Moreover, it explores existing, next generation, as well as prospective choices for V2X communication technologies, evaluating how well they contribute to the development of RGS applications by integrating them with potential communication systems. Specifically, this study assesses the suitability of communication technologies in meeting the requirements of RGS applications. In conclusion, it suggests a framework for integrating RGS and V2X systems and offers directions for future research in this area. Full article
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36 pages, 4557 KiB  
Review
Integrating Social Dimensions into Urban Digital Twins: A Review and Proposed Framework for Social Digital Twins
by Saleh Qanazi, Eric Leclerc and Pauline Bosredon
Smart Cities 2025, 8(1), 23; https://doi.org/10.3390/smartcities8010023 - 5 Feb 2025
Abstract
The rapid evolution of smart city technologies has expanded digital twin (DT) applications from industrial to urban contexts. However, current urban digital twins (UDTs) remain predominantly focused on the physical aspects of urban environments (“spaces”), often overlooking the interwoven social dimensions that shape [...] Read more.
The rapid evolution of smart city technologies has expanded digital twin (DT) applications from industrial to urban contexts. However, current urban digital twins (UDTs) remain predominantly focused on the physical aspects of urban environments (“spaces”), often overlooking the interwoven social dimensions that shape the concept of “place”. This limitation restricts their ability to fully represent the complex interplay between physical and social systems in urban settings. To address this gap, this paper introduces the concept of the social digital twin (SDT), which integrates social dimensions into UDTs to bridge the divide between technological systems and the lived urban experience. Drawing on an extensive literature review, the study defines key components for transitioning from UDTs to SDTs, including conceptualization and modeling of human interactions (geo-individuals and geo-socials), social applications, participatory governance, and community engagement. Additionally, it identifies essential technologies and analytical tools for implementing SDTs, outlines research gaps and practical challenges, and proposes a framework for integrating social dynamics within UDTs. This framework emphasizes the importance of active community participation through a governance model and offers a comprehensive methodology to support researchers, technology developers, and policymakers in advancing SDT research and practical applications. Full article
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33 pages, 1317 KiB  
Systematic Review
Building Urban Resilience Through Smart City Planning: A Systematic Literature Review
by Abdulaziz I. Almulhim
Smart Cities 2025, 8(1), 22; https://doi.org/10.3390/smartcities8010022 - 31 Jan 2025
Abstract
Smart city planning is crucial for enhancing urban resilience, especially with the contemporary challenges of rising urban population and climate change. This study conducts a systematic literature review (SLR) to examine the integration of urban resilience in smart city planning, synthesizing the current [...] Read more.
Smart city planning is crucial for enhancing urban resilience, especially with the contemporary challenges of rising urban population and climate change. This study conducts a systematic literature review (SLR) to examine the integration of urban resilience in smart city planning, synthesizing the current literature to identify key components, barriers, and enablers. The study found that technological integration, sustainability measures, and citizens’ participation are critical factors to the effective development of smart cities. The review emphasizes the need for an integrated approach to urban resilience, calling for continued research and collaboration among stakeholders. It highlights how urban sustainability and resilience should be addressed within an urban system and that interdisciplinary work, stakeholder consultation, and public engagement are required. It finally suggests the integration of creativity and diversity in urban planning practices and policies for improving vulnerability to modern-day challenges in urban contexts. It concludes by outlining implications for urban planning practices and policy development, advocating for innovative, inclusive strategies to enhance urban resilience. Full article
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28 pages, 1697 KiB  
Review
Toward Sustainable Urban Mobility: A Systematic Review of Transit-Oriented Development for the Appraisal of Dubai Metro Stations
by Oussama Yahia, Afaq Hyder Chohan, Mohammad Arar and Jihad Awad
Smart Cities 2025, 8(1), 21; https://doi.org/10.3390/smartcities8010021 - 30 Jan 2025
Abstract
In Dubai’s rapidly expanding urban landscape, addressing the adverse impacts of increasing automobile reliance is critical. Growing vehicle usage contributes to urban sprawl, prolonged commutes, infrastructure strain, and diminished green spaces. As a sustainable alternative, Transit-Oriented Development (TOD) promotes compact density, mixed-use environments, [...] Read more.
In Dubai’s rapidly expanding urban landscape, addressing the adverse impacts of increasing automobile reliance is critical. Growing vehicle usage contributes to urban sprawl, prolonged commutes, infrastructure strain, and diminished green spaces. As a sustainable alternative, Transit-Oriented Development (TOD) promotes compact density, mixed-use environments, and transit-focused design, particularly suited for Dubai’s evolving context. This study evaluates the applicability of Transit-Adjusted Development (TAD) and TOD appraisal models, specifically the 3D and 6D frameworks, to stations on both the Red and Green Lines of the Dubai Metro. By examining Dubai’s complex urban form, the research identifies strategic interventions to enhance urban mobility and mitigate sprawl. Through an extensive literature review, key factors shaping sustainable urban transport such as accessibility, land-use diversity, density, design, distance to transit, and demand management are analyzed. This investigation highlights the suitability of implementing TOD principles at prominent metro stations, including Al Rashidiya, Al Qusais, and Mall of the Emirates. These stations hold significant potential for strengthening transit efficiency, fostering pedestrian-friendly neighborhoods, and reducing dependency on private vehicles. The findings underscore the importance of integrating TOD strategies into Dubai’s metropolitan planning. By doing so, Dubai can move toward a more connected, efficient, and environmentally responsible urban future. Full article
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24 pages, 6060 KiB  
Article
Mitigating Voltage Violations in Smart City Microgrids Under Coordinated False Data Injection Cyberattacks: Simulation and Experimental Insights
by Ehsan Naderi and Arash Asrari
Smart Cities 2025, 8(1), 20; https://doi.org/10.3390/smartcities8010020 - 29 Jan 2025
Abstract
This article investigates the impacts of coordinated false data injection attacks (FDIAs) on voltage profiles in smart microgrids integrated with renewable-based distributed energy resources (DERs), a critical component of urban energy infrastructure in smart cities. By leveraging simulation and experimental methods, a coordinated [...] Read more.
This article investigates the impacts of coordinated false data injection attacks (FDIAs) on voltage profiles in smart microgrids integrated with renewable-based distributed energy resources (DERs), a critical component of urban energy infrastructure in smart cities. By leveraging simulation and experimental methods, a coordinated framework is developed for understanding and mitigating these threats, ensuring the stability of renewable-based DERs integral to modern urban systems. In the examined framework, a team of attackers independently identify the optimal times of two different cyberattacks leading to undervoltage and overvoltage in a smart microgrid. The objective function of each model is to increase the voltage violation in the form of either overvoltage or undervoltage caused by the corresponding FDIA. In such a framework, the attackers design a multi-objective optimization problem (MOOP) simultaneously resulting in voltage violations in the most vulnerable regions of the targeted microgrid. Considering the conflict between objective functions in the developed MOOP, a Pareto-based solution methodology is utilized to obtain a set of optimal solutions, called non-dominated solutions, as well as the best compromise solution (BCS). The effectiveness of the unified FDIA is verified based on simulation and experimental validations. In this regard, the IEEE 13-node test feeder has been modified as a microgrid for the simulation analysis, whereas the experimental validation has been performed on a lab-scale hybrid PV/wind microgrid containing renewable energy resources. Full article
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35 pages, 6240 KiB  
Article
LLM Agents for Smart City Management: Enhancing Decision Support Through Multi-Agent AI Systems
by Anna Kalyuzhnaya, Sergey Mityagin, Elizaveta Lutsenko, Andrey Getmanov, Yaroslav Aksenkin, Kamil Fatkhiev, Kirill Fedorin, Nikolay O. Nikitin, Natalia Chichkova, Vladimir Vorona and Alexander Boukhanovsky
Smart Cities 2025, 8(1), 19; https://doi.org/10.3390/smartcities8010019 - 24 Jan 2025
Viewed by 407
Abstract
This study investigates the implementation of LLM agents in smart city management, leveraging both the inherent language processing abilities of LLMs and the distributed problem solving capabilities of multi-agent systems for the improvement of urban decision making processes. A multi-agent system architecture combines [...] Read more.
This study investigates the implementation of LLM agents in smart city management, leveraging both the inherent language processing abilities of LLMs and the distributed problem solving capabilities of multi-agent systems for the improvement of urban decision making processes. A multi-agent system architecture combines LLMs with existing urban information systems to process complex queries and generate contextually relevant responses for urban planning and management. The research is focused on three main hypotheses testing: (1) LLM agents’ capability for effective routing and processing diverse urban queries, (2) the effectiveness of Retrieval-Augmented Generation (RAG) technology in improving response accuracy when working with local knowledge and regulations, and (3) the impact of integrating LLM agents with existing urban information systems. Our experimental results, based on a comprehensive validation dataset of 150 question–answer pairs, demonstrate significant improvements in decision support capabilities. The multi-agent system achieved pipeline selection accuracy of 94–99% across different models, while the integration of RAG technology improved response accuracy by 17% for strategic development queries and 55% for service accessibility questions. The combined use of document databases and service APIs resulted in the highest performance metrics (G-Eval scores of 0.68–0.74) compared to standalone LLM responses (0.30–0.38). Using St. Petersburg’s Digital Urban Platform as a testbed, we demonstrate the practical applicability of this approach to create integrated city management systems with support complex urban decision making processes. This research contributes to the growing field of AI-enhanced urban management by providing empirical evidence of LLM agents’ effectiveness in processing heterogeneous urban data and supporting strategic planning decisions. Our findings suggest that LLM-based multi-agent systems can significantly enhance the efficiency and accuracy of urban decision making while maintaining high relevance in responses. Full article
(This article belongs to the Special Issue Big Data and AI Services for Sustainable Smart Cities)
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22 pages, 16526 KiB  
Article
Public Vitality-Driven Optimization of Urban Public Space Networks—A Case Study from Nanjing, China
by Ning Xu, Xiao Zhang and Pu Wang
Smart Cities 2025, 8(1), 18; https://doi.org/10.3390/smartcities8010018 - 24 Jan 2025
Viewed by 329
Abstract
Spontaneous recreational activities in public spaces are a vital source of public vitality. Given the similarity between the walking patterns of recreational crowds in public spaces and the movement of electrons on a two-dimensional circuit surface, this study combines big data from various [...] Read more.
Spontaneous recreational activities in public spaces are a vital source of public vitality. Given the similarity between the walking patterns of recreational crowds in public spaces and the movement of electrons on a two-dimensional circuit surface, this study combines big data from various sources to create an “electrical conductivity surface” that attracts and aggregates recreational crowds. Using current flow simulation, we generate the path selection preferences of people as they move across public spaces. The results reveal an uneven distribution of public spaces in Nanjing’s main urban area, with high-vitality areas mostly concentrated in the urban center. The core demand for enhancing public vitality lies is improving connectivity between multiple spaces. Based on this, the public space plan for Nanjing’s main urban area emphasizes overall connectivity by aligning with the natural landscape, thus linking the city’s green and gray infrastructure. In this study, we have assessed current public space services and their development potential from a number of different angles, developing a digital approach for optimizing the urban layout. We aim to provide a human-centric, bottom-up perspective to complement the top-down city planning and management approach. This will enable urban planners to make informed decisions for creating and managing more vibrant cities. Full article
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36 pages, 3856 KiB  
Article
The Urban Building Energy Retrofitting Tool: An Open-Source Framework to Help Foster Building Retrofitting Using a Life Cycle Costing Perspective—First Results for Montréal
by Oriol Gavaldà-Torrellas, Pilar Monsalvete, Saeed Ranjbar and Ursula Eicker
Smart Cities 2025, 8(1), 17; https://doi.org/10.3390/smartcities8010017 - 24 Jan 2025
Viewed by 468
Abstract
Building decarbonization is a major challenge for cities. Deciding which buildings to retrofit buildings, and when and how, is difficult, given the complex interaction between energy costs and investment requirements. Several tools have been developed in recent years to help public and private [...] Read more.
Building decarbonization is a major challenge for cities. Deciding which buildings to retrofit buildings, and when and how, is difficult, given the complex interaction between energy costs and investment requirements. Several tools have been developed in recent years to help public and private stakeholders with these decisions, but none cover aspects the authors think are fundamental. For this reason, an urban buildings retrofit tool was developed and is presented in this article. This new tool is based on a bottom-up approach, with all buildings simulated individually, considering aspects such as shading and adjacencies. As a second step, three scenarios with different levels of ambition were implemented in the tool, and the energy demand and emissions resulting from these scenarios were calculated. As a third step, the retrofitting scenarios’ initial investment and operational costs were implemented using a detailed Life Cycle Costing (LCC) approach. A robust and scalable structure was developed and applied to calculate the LCC of various retrofitting scenarios in Montréal, which will be described in detail. Full article
(This article belongs to the Topic Sustainable and Smart Building)
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41 pages, 2511 KiB  
Article
The Problems of Scooter-Sharing in Smart Cities Based on the Example of the Silesian Region in Poland
by Radosław Wolniak and Katarzyna Turoń
Smart Cities 2025, 8(1), 16; https://doi.org/10.3390/smartcities8010016 - 21 Jan 2025
Viewed by 877
Abstract
The rapid urbanization and pursuit of sustainability have elevated shared mobility as a cornerstone of smart cities. Among its modalities, scooter-sharing has gained popularity for its convenience and eco-friendliness, yet it faces significant adoption barriers. This study investigates the challenges to scooter-sharing systems [...] Read more.
The rapid urbanization and pursuit of sustainability have elevated shared mobility as a cornerstone of smart cities. Among its modalities, scooter-sharing has gained popularity for its convenience and eco-friendliness, yet it faces significant adoption barriers. This study investigates the challenges to scooter-sharing systems within smart cities, focusing on the Silesian region of Poland as a case study. It aims to identify region-specific barriers and opportunities for scooter-sharing adoption in Central and Eastern Europe and to provide insights into its long-term development trends and potential challenges. Using comprehensive statistical methods, including factor analysis and regression models, this study identifies key barriers such as insufficient bike paths, poor path conditions, inadequate signage, fleet maintenance issues, and complex rental processes. External factors like adverse weather and heavy traffic, coupled with health and safety concerns, further hinder adoption, particularly among vulnerable populations. Additionally, the study explores future trends in scooter-sharing, emphasizing the role of advanced technologies, adaptive urban planning, and sustainable fleet management in ensuring long-term feasibility. Drawing on global case studies, it underscores the need for tailored infrastructural investments, advanced fleet management, and user-centric policies to align scooter-sharing systems with smart city goals of sustainability, accessibility, and improved mobility. These findings offer actionable insights for policymakers and service providers striving to integrate scooter-sharing into the evolving landscape of urban mobility. Full article
(This article belongs to the Special Issue Smart Mobility: Linking Research, Regulation, Innovation and Practice)
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18 pages, 5557 KiB  
Article
Improvement in Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision Metrics
by Jamal Raiyn and Galia Weidl
Smart Cities 2025, 8(1), 15; https://doi.org/10.3390/smartcities8010015 - 21 Jan 2025
Viewed by 397
Abstract
This paper proposes a new strategy for a collision avoidance system leveraging time-to-collision (TTC) metrics for handling cut-in scenarios, which are particularly challenging for autonomous vehicles (AVs). By integrating deep learning with TTC calculations, the system predicts potential collisions and determines appropriate evasive [...] Read more.
This paper proposes a new strategy for a collision avoidance system leveraging time-to-collision (TTC) metrics for handling cut-in scenarios, which are particularly challenging for autonomous vehicles (AVs). By integrating deep learning with TTC calculations, the system predicts potential collisions and determines appropriate evasive actions compared to traditional TTC-based approaches. The methodology is validated through extensive simulations, demonstrating a significant improvement in collision avoidance performance compared to traditional TTC-based approaches. By integrating deep learning models with TTC calculations, the system predicts potential collisions and determines appropriate evasive actions. The use of the Gaussian model to contributes to time-to-collision (TTC) analysis by providing a probabilistic framework to quantify collision risk under uncertainty. It calculates the likelihood that TTC will fall below a critical threshold (TTC_crit), indicating a potential collision. By modeling input variations—such as sensor inaccuracies, fluctuating vehicle velocity, and unpredictable driving behavior—as a Gaussian distribution, the system can handle real-world uncertainties more effectively. This enables continuous, real-time risk prediction, allowing for dynamic and adaptive collision avoidance decisions. The Gaussian approach enhances the robustness of TTC-based systems by improving their ability to predict and prevent collisions in uncertain driving conditions. Full article
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17 pages, 4082 KiB  
Article
Geospatial Clustering in Smart City Resource Management: An Initial Step in the Optimisation of Complex Technical Supply Systems
by Aliaksey A. Kapanski, Roman V. Klyuev, Aleksandr E. Boltrushevich, Svetlana N. Sorokova, Egor A. Efremenkov, Anton Y. Demin and Nikita V. Martyushev
Smart Cities 2025, 8(1), 14; https://doi.org/10.3390/smartcities8010014 - 21 Jan 2025
Viewed by 553
Abstract
For large cities with developing infrastructures, optimising water supply systems plays a crucial role. However, without a clear understanding of the network structure and water consumption patterns, addressing these challenges becomes significantly more complex. This paper proposes a methodology for geospatial data analysis [...] Read more.
For large cities with developing infrastructures, optimising water supply systems plays a crucial role. However, without a clear understanding of the network structure and water consumption patterns, addressing these challenges becomes significantly more complex. This paper proposes a methodology for geospatial data analysis aimed at solving two key tasks. The first is the delineation of service zones for infrastructure objects to enhance system manageability. The second involves the development of an approach for the optimal placement of devices to collect and transmit hydraulic network parameters, ensuring their alignment with both water supply sources and serviced areas. The study focuses on data from the water supply network of a city with a population exceeding half a million people, where hierarchical clustering using Ward’s method was applied to analyse territorial distribution. Four territorial clusters were identified, each characterised by unique attributes reflecting consumer concentration and water consumption volumes. The cluster boundaries were compared with the existing service scheme of the system, confirming their alignment with real infrastructure. The quality of clustering was further evaluated using the silhouette coefficient, which validated the high accuracy and reliability of the chosen approach. The paper demonstrates the effectiveness of cluster boundary visualisation for assessing the uniform distribution of pressure sensors within the urban water supply network. The results of the study show that integrating geographic data with water consumption information not only facilitates effective infrastructure planning and resource allocation but also lays the foundation for the digitalization of the hydraulic network, a critical component of sustainable development in modern smart cities. Full article
(This article belongs to the Special Issue Digital Innovation and Transformation for Smart Cities)
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25 pages, 1485 KiB  
Article
Can Machine Learning Enhance Intrusion Detection to Safeguard Smart City Networks from Multi-Step Cyberattacks?
by Jowaria Khan, Rana Elfakharany, Hiba Saleem, Mahira Pathan, Emaan Shahzad, Salam Dhou and Fadi Aloul
Smart Cities 2025, 8(1), 13; https://doi.org/10.3390/smartcities8010013 - 20 Jan 2025
Viewed by 509
Abstract
Intrusion detection systems are essential for detecting network cyberattacks. As the sophistication of cyberattacks increases, it is critical that defense technologies adapt to counter them. Multi-step attacks, which need several correlated intrusion operations to reach the desired target, are a rising trend in [...] Read more.
Intrusion detection systems are essential for detecting network cyberattacks. As the sophistication of cyberattacks increases, it is critical that defense technologies adapt to counter them. Multi-step attacks, which need several correlated intrusion operations to reach the desired target, are a rising trend in the cybersecurity field. System administrators are responsible for recreating whole attack scenarios and developing improved intrusion detection systems since the systems at present are still designed to generate alerts for only single attacks with little to no correlation. This paper proposes a machine learning approach to identify and classify multi-step network intrusion attacks, with particular relevance to smart cities, where interconnected systems are highly vulnerable to cyber threats. Smart cities rely on these systems seamlessly functioning with one another, and any successful cyberattack could have devastating effects, including large-scale data theft. In such a context, the proposed machine learning model offers a robust solution for detecting and mitigating multi-step cyberattacks in these critical environments. Several machine learning algorithms are considered, namely Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Light Gradient-Boosting Machine (LGBM), Extreme Gradient Boosting (XGB) and Random Forest (RF). These models are trained on the Multi-Step Cyber-Attack Dataset (MSCAD), a recent dataset that is highly representative of real-world multi-step cyberattack scenarios, which increases the accuracy and efficiency of such systems. The experimental results show that the best performing model was XGB, which achieved a testing accuracy of 100% and an F1 Score of 88%. The proposed model is computationally efficient and easy to deploy, which ensures a fast, sustainable and low power-consuming intrusion detection system at the cutting edge. Full article
(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)
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23 pages, 22602 KiB  
Article
Enhancing Human Detection in Occlusion-Heavy Disaster Scenarios: A Visibility-Enhanced DINO (VE-DINO) Model with Reassembled Occlusion Dataset
by Zi-An Zhao, Shidan Wang, Min-Xin Chen, Ye-Jiao Mao, Andy Chi-Ho Chan, Derek Ka-Hei Lai, Duo Wai-Chi Wong and James Chung-Wai Cheung
Smart Cities 2025, 8(1), 12; https://doi.org/10.3390/smartcities8010012 - 16 Jan 2025
Viewed by 650
Abstract
Natural disasters create complex environments where effective human detection is both critical and challenging, especially when individuals are partially occluded. While recent advancements in computer vision have improved detection capabilities, there remains a significant need for efficient solutions that can enhance search-and-rescue (SAR) [...] Read more.
Natural disasters create complex environments where effective human detection is both critical and challenging, especially when individuals are partially occluded. While recent advancements in computer vision have improved detection capabilities, there remains a significant need for efficient solutions that can enhance search-and-rescue (SAR) operations in resource-constrained disaster scenarios. This study modified the original DINO (Detection Transformer with Improved Denoising Anchor Boxes) model and introduced the visibility-enhanced DINO (VE-DINO) model, designed for robust human detection in occlusion-heavy environments, with potential integration into SAR system. VE-DINO enhances detection accuracy by incorporating body part key point information and employing a specialized loss function. The model was trained and validated using the COCO2017 dataset, with additional external testing conducted on the Disaster Occlusion Detection Dataset (DODD), which we developed by meticulously compiling relevant images from existing public datasets to represent occlusion scenarios in disaster contexts. The VE-DINO achieved an average precision of 0.615 at IoU 0.50:0.90 on all bounding boxes, outperforming the original DINO model (0.491) in the testing set. The external testing of VE-DINO achieved an average precision of 0.500. An ablation study was conducted and demonstrated the robustness of the model subject when confronted with varying degrees of body occlusion. Furthermore, to illustrate the practicality, we conducted a case study demonstrating the usability of the model when integrated into an unmanned aerial vehicle (UAV)-based SAR system, showcasing its potential in real-world scenarios. Full article
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34 pages, 2610 KiB  
Review
Nanogrids in Modern Power Systems: A Comprehensive Review
by Nasrin Einabadi and Mehrdad Kazerani
Smart Cities 2025, 8(1), 11; https://doi.org/10.3390/smartcities8010011 - 16 Jan 2025
Viewed by 385
Abstract
Nanogrids are becoming an essential part of modern home power systems, offering sustainable solutions for residential areas. These medium-to-low voltage, small-scale grids, operating at medium-to-low voltage, enable the integration of distributed energy resources such as wind turbines, solar photovoltaics, and battery energy storage [...] Read more.
Nanogrids are becoming an essential part of modern home power systems, offering sustainable solutions for residential areas. These medium-to-low voltage, small-scale grids, operating at medium-to-low voltage, enable the integration of distributed energy resources such as wind turbines, solar photovoltaics, and battery energy storage systems. However, ensuring power quality, stability, and effective energy management remains a challenge due to the variability of renewable energy sources and evolving customer demands, including the increasing charging load of electric vehicles. This paper reviews the current research on nanogrid architecture, functionality in low-voltage distribution systems, energy management, and control systems. It also explores power-sharing strategies among nanogrids within a microgrid framework, focusing on their potential for supplying off-grid areas. Additionally, the application of blockchain technology in providing secure and decentralized energy trading transactions is explored. Potential challenges in future developments of nanogrids are also discussed. Full article
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25 pages, 4451 KiB  
Article
Integrating Blockchain Technology into Mobility-as-a-Service Platforms for Smart Cities
by Radu Miron, Mihai Hulea, Vlad Muresan, Iulia Clitan and Andrei Rusu
Smart Cities 2025, 8(1), 9; https://doi.org/10.3390/smartcities8010009 - 7 Jan 2025
Viewed by 755
Abstract
As cities evolve into smarter and more connected environments, there is a growing need for innovative solutions to improve urban mobility. This study examines the potential of integrating blockchain technology into passenger transportation systems within smart cities, with a particular emphasis on a [...] Read more.
As cities evolve into smarter and more connected environments, there is a growing need for innovative solutions to improve urban mobility. This study examines the potential of integrating blockchain technology into passenger transportation systems within smart cities, with a particular emphasis on a blockchain-enabled Mobility-as-a-Service (MaaS) solution. In contrast to traditional technologies, blockchain’s decentralized structure improves data security and guarantees transaction transparency, thus reducing the risk of fraud and errors. The proposed MaaS framework enables seamless collaboration between key transportation stakeholders, promoting more efficient utilization of services like buses, trains, bike-sharing, and ride-hailing. By improving integrated payment and ticketing systems, the solution aims to create a smoother user experience while advancing the urban goals of efficiency, environmental sustainability, and secure data handling. This research evaluates the feasibility of a Hyperledger Fabric-based solution, demonstrating its performance under various load conditions and proposing scalability adjustments based on pilot results. The conclusions indicate that blockchain-enabled MaaS systems have the potential to transform urban mobility. Further exploration into pilot projects and the expansion to freight transportation are needed for an integrated approach to city-wide transport solutions. Full article
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65 pages, 9824 KiB  
Review
Leveraging Smart City Technologies for Enhanced Real Estate Development: An Integrative Review
by Tarek Al-Rimawi and Michael Nadler
Smart Cities 2025, 8(1), 10; https://doi.org/10.3390/smartcities8010010 - 7 Jan 2025
Viewed by 1037
Abstract
This study aims to identify the added value of smart city technologies in real estate development, one of the most significant factors that would transform traditional real estate into smart ones. In total, 16 technologies utilized at both levels have been investigated. The [...] Read more.
This study aims to identify the added value of smart city technologies in real estate development, one of the most significant factors that would transform traditional real estate into smart ones. In total, 16 technologies utilized at both levels have been investigated. The research followed an integrative review methodology; the review is based on 168 publications. The compiled results based on metadata analysis displayed the state of each technology’s added values and usage in both scales. A total of 131 added values were identified. These added values were categorized based on the real estate life cycle sub-phases and processes. Moreover, the value of the integration between these technologies was revealed. The review and results proved that these technologies are mature enough for practical use; therefore, real estate developers, city management, planners, and experts should focus on implementing them. City management should invest in Big Data and geodata and adopt several technologies based on the aspects required for development. This study can influence stakeholders, enhance their decision-making on which technology would suit their needs, and provide recommendations on who to utilize them. Also, it provides a starting point for stakeholders who aim to establish a road map for incorporating smart technologies in future smart real estate. Full article
(This article belongs to the Section Smart Buildings)
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24 pages, 2299 KiB  
Review
Review of Energy Communities: Definitions, Regulations, Topologies, and Technologies
by Alexandra Catalina Lazaroiu, Mariacristina Roscia, George Cristian Lazaroiu and Pierluigi Siano
Smart Cities 2025, 8(1), 8; https://doi.org/10.3390/smartcities8010008 - 6 Jan 2025
Viewed by 1036
Abstract
The Clean Energy package recognizes and offers a favorable regulatory framework for citizens and energy communities with renewable energy sources. However, various countries’ national regulations will be highly important for the successful development of energy communities in existing cities and surrounding areas. Energy [...] Read more.
The Clean Energy package recognizes and offers a favorable regulatory framework for citizens and energy communities with renewable energy sources. However, various countries’ national regulations will be highly important for the successful development of energy communities in existing cities and surrounding areas. Energy communities represent a way in which citizens and local authorities can invest in clean energy sources and energy efficiency, with several benefits in addition to the financial ones, like strengthening the concept of community and individual contributions to reductions in the overall carbon footprint. In this paper, an overview of recent developments in financial incentives in energy communities, their organization, and typologies, as well as benefits shared among the participants, is performed. The overview reveals the potential of energy communities in contributing to the economic, energetic, and social development of cities towards sustainable and smart cities. Full article
(This article belongs to the Special Issue Feature Papers in Smart Cities)
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18 pages, 4315 KiB  
Article
Real-Time Monitoring of Environmental Parameters in Schools to Improve Indoor Resilience Under Extreme Events
by Salit Azoulay Kochavi, Oz Kira and Erez Gal
Smart Cities 2025, 8(1), 7; https://doi.org/10.3390/smartcities8010007 - 3 Jan 2025
Viewed by 965
Abstract
Climatic changes lead to many extreme weather events throughout the globe. These extreme weather events influence our behavior, exposing us to different environmental conditions, such as poor indoor quality. Poor indoor air quality (IAQ) poses a significant concern in the modern era, as [...] Read more.
Climatic changes lead to many extreme weather events throughout the globe. These extreme weather events influence our behavior, exposing us to different environmental conditions, such as poor indoor quality. Poor indoor air quality (IAQ) poses a significant concern in the modern era, as people spend up to 90% of their time indoors. Ventilation influences key IAQ elements such as temperature, relative humidity, and particulate matter (PM). Children, considered a vulnerable group, spend approximately 30% of their time in educational settings, often housed in old structures with poorly maintained ventilation systems. Extreme weather events lead young students to stay indoors, usually behind closed doors and windows, which may lead to exposure to elevated levels of air pollutants. In our research, we aim to demonstrate how real-time monitoring of air pollutants and other environmental parameters under extreme weather is important for regulating the indoor environment. A study was conducted in a school building with limited ventilation located in an arid region near the Red Sea, which frequently suffers from high PM concentrations. In this study, we tracked the indoor environmental conditions and air quality during the entire month of May 2022, including an extreme outdoor weather event of sandstorms. During this month, we continuously monitored four classrooms in an elementary school built in 1967 in Eilat. Our findings indicate that PM2.5 was higher indoors (statistically significant) by more than 16% during the extreme event. Temperature was also elevated indoors (statistically significant) by more than 5%. The parameters’ deviation highlights the need for better indoor weather control and ventilation systems, as well as ongoing monitoring in schools to maintain healthy indoor air quality. This also warrants us as we are approaching an era of climatic instability, including higher occurrence of similar extreme events, which urge us to develop real-time responses in urban areas. Full article
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21 pages, 6950 KiB  
Article
Mechanism-Driven Intelligent Settlement Prediction for Shield Tunneling Through Areas Without Ground Monitoring
by Min Hu, Pengpeng Zhao, Jing Lu and Bingjian Wu
Smart Cities 2025, 8(1), 6; https://doi.org/10.3390/smartcities8010006 - 27 Dec 2024
Viewed by 630
Abstract
Ground settlement is a crucial indicator for assessing the safety of shield tunneling and its impact on the surrounding environment. However, most existing settlement prediction methods are based on historical data, which can only be applied with effective monitoring conditions. To overcome this [...] Read more.
Ground settlement is a crucial indicator for assessing the safety of shield tunneling and its impact on the surrounding environment. However, most existing settlement prediction methods are based on historical data, which can only be applied with effective monitoring conditions. To overcome this limitation, this paper proposes the mechanism-driven intelligent settlement prediction method (MISPM), which considers the mechanisms of settlement and attitude movements during construction to design new features that can indirectly reflect settlement. Simulation experiments were used to compare the impact of different candidate features and algorithms on prediction performance, verifying the validity and accuracy of the model. The efficacy of MISPM in predicting settlement changes in advance was substantiated by practical engineering applications. Results showed that MISPM could accurately predict settlement changes even without ground monitoring, thereby corroborating its reliability and applicability in supporting safe tunneling in complex geological environments. In the construction of urban infrastructure, this method has the potential to enhance the efficiency of tunnel construction and ensure environmental safety, which is of great significance for the development of smart cities. Full article
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29 pages, 1715 KiB  
Article
Multi-Armed Bandit Approaches for Location Planning with Dynamic Relief Supplies Allocation Under Disaster Uncertainty
by Jun Liang, Zongjia Zhang and Yanpeng Zhi
Smart Cities 2025, 8(1), 5; https://doi.org/10.3390/smartcities8010005 - 25 Dec 2024
Viewed by 606
Abstract
Natural disasters (e.g., floods, earthquakes) significantly impact citizens, economies, and the environment worldwide. Due to their sudden onset, devastating effects, and high uncertainty, it is crucial for emergency departments to take swift action to minimize losses. Among these actions, planning the locations of [...] Read more.
Natural disasters (e.g., floods, earthquakes) significantly impact citizens, economies, and the environment worldwide. Due to their sudden onset, devastating effects, and high uncertainty, it is crucial for emergency departments to take swift action to minimize losses. Among these actions, planning the locations of relief supply distribution centers and dynamically allocating supplies is paramount, as governments must prioritize citizens’ safety and basic living needs following disasters. To address this challenge, this paper develops a three-layer emergency logistics network to manage the flow of emergency materials, from warehouses to transfer stations to disaster sites. A bi-objective, multi-period stochastic integer programming model is proposed to solve the emergency location, distribution, and allocation problem under uncertainty, focusing on three key decisions: transfer station selection, upstream emergency material distribution, and downstream emergency material allocation. We introduce a multi-armed bandit algorithm, named the Geometric Greedy algorithm, to optimize transfer station planning while accounting for subsequent dynamic relief supply distribution and allocation in a stochastic environment. The new algorithm is compared with two widely used multi-armed bandit algorithms: the ϵ-Greedy algorithm and the Upper Confidence Bound (UCB) algorithm. A case study in the Futian District of Shenzhen, China, demonstrates the practicality of our model and algorithms. The results show that the Geometric Greedy algorithm excels in both computational efficiency and convergence stability. This research offers valuable guidelines for emergency departments in optimizing the layout and flow of emergency logistics networks. Full article
(This article belongs to the Section Applied Science and Humanities for Smart Cities)
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36 pages, 2930 KiB  
Article
Probabilistic Causal Modeling of Barriers to Accessibility for Persons with Disabilities in Canada
by Mouri Zakir, Gregor Wolbring and Svetlana Yanushkevich
Smart Cities 2025, 8(1), 4; https://doi.org/10.3390/smartcities8010004 - 24 Dec 2024
Viewed by 627
Abstract
This paper utilizes a methodological two-step process incorporating statistical and causal probabilistic modeling techniques to investigate factors affecting the accessibility experiences of persons with disabilities in Canada. We deploy a network-based approach using empirical data to perform a holistic assessment of the relations [...] Read more.
This paper utilizes a methodological two-step process incorporating statistical and causal probabilistic modeling techniques to investigate factors affecting the accessibility experiences of persons with disabilities in Canada. We deploy a network-based approach using empirical data to perform a holistic assessment of the relations between various demographic features (e.g., age, gender and type of disability) and accessibility barriers. A statistical measurement method is applied that utilizes structural equation modeling supported by exploratory factor analysis. For causal probabilistic modeling, Bayesian networks are employed as a straightforward and compact way to interpret knowledge representation. This causal reasoning approach analyzes the nature and frequency of encountering barriers based on data to understand the risk factors contributing to pressing accessibility issues. Furthermore, to evaluate network performance and overcome any data limitations, synthetic data generation techniques are applied to create and validate artificial data built on real-world knowledge. The proposed framework strives to provide reasoning to understand the prevalence of physical, social, communication or technological barriers encountered by persons with disabilities in their daily lives. This study contributes to the identification of areas for prioritization in facilitating accessibility regulation and practices to realize an inclusive society. Full article
(This article belongs to the Special Issue Inclusive Smart Cities)
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