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Smart Cities

Smart Cities is an international, scientific, peer-reviewed, open access journal on the science and technology of smart cities, published bimonthly online by MDPI. The International Council for Research and Innovation in Building and Construction (CIB) is affiliated with Smart Cities and its members receive discounts on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), Inspec, AGRIS, and other databases.
Journal Rank: JCR - Q1 (Urban Studies) / CiteScore - Q1 (Urban Studies)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 26.8 days after submission; acceptance to publication is undertaken in 4.5 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 5.5 (2024); 5-Year Impact Factor: 6.4 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2624-6511

Latest Articles

20 pages, 3386 KiB

Open AccessArticle

Evaluating Acoustic vs. AI-Based Satellite Leak Detection in Aging US Water Infrastructure: A Cost and Energy Savings Analysis

by Prashant Nagapurkar, Naushita Sharma, Susana Garcia and Sachin Nimbalkar

Smart Cities 2025, 8(4), 122; https://doi.org/10.3390/smartcities8040122 - 22 Jul 2025

The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system [...] Read more.

The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system by using leak detection technologies can create net energy and cost savings. In this work, a new framework has been presented to calculate the economic level of leakage within water supply and distribution systems for two primary leak detection technologies (acoustic vs. satellite). In this work, a new framework is presented to calculate the economic level of leakage (ELL) within water supply and distribution systems to support smart infrastructure in smart cities. A case study focused using water audit data from Atlanta, Georgia, compared the costs of two leak mitigation technologies: conventional acoustic leak detection and artificial intelligence–assisted satellite leak detection technology, which employs machine learning algorithms to identify potential leak signatures from satellite imagery. The ELL results revealed that conducting one survey would be optimum for an acoustic survey, whereas the method suggested that it would be expensive to utilize satellite-based leak detection technology. However, results for cumulative financial analysis over a 3-year period for both technologies revealed both to be economically favorable with conventional acoustic leak detection technology generating higher net economic benefits of USD 2.4 million, surpassing satellite detection by 50%. A broader national analysis was conducted to explore the potential benefits of US water infrastructure mirroring the exemplary conditions of Germany and The Netherlands. Achieving similar infrastructure leakage index (ILI) values could result in annual cost savings of $4–$4.8 billion and primary energy savings of 1.6–1.9 TWh. These results demonstrate the value of combining economic modeling with advanced leak detection technologies to support sustainable, cost-efficient water infrastructure strategies in urban environments, contributing to more sustainable smart living outcomes. Full article

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17 pages, 2256 KiB

Open AccessArticle

Performance Analysis of Different Borehole Heat Exchanger Configurations: A Case Study in NW Italy

by Jessica Maria Chicco, Nicolò Giordano, Cesare Comina and Giuseppe Mandrone

Smart Cities 2025, 8(4), 121; https://doi.org/10.3390/smartcities8040121 - 21 Jul 2025

The central role of heating and cooling in energy transition has been recognised in recent years, especially with geopolitical developments since February 2022 which demand an acceleration in deploying local energy sources to increase the resilience of the energy sector. Geothermal energy is [...] Read more.

The central role of heating and cooling in energy transition has been recognised in recent years, especially with geopolitical developments since February 2022 which demand an acceleration in deploying local energy sources to increase the resilience of the energy sector. Geothermal energy is a promising and vital option to optimize heating and cooling systems, promoting sustainability of urban environments. To this end, a proper design is of paramount importance to guarantee the energy performance of the whole system. This work deals with the optimization of the technical and geometrical characteristics of borehole heat exchangers (BHEs) as part of a shallow geothermal plant that is assumed to be integrated in an already operating gas-fired DH grid. Thermal performances of three different configurations were analysed according to the geological information that revealed an aquifer at −36 m overlying a poorly permeable marly succession. Numerical simulations validated the geological, hydrogeological, and thermo-physical models by back-analysing the experimental results of a thermal response test (TRT) on a pilot 150 m deep BHE. Five-year simulations were then performed to compare 150 m and 36 m polyethylene 2U, and 36 m steel coaxial BHEs. The coaxial configuration shows the best performance both in terms of specific power (74.51 W/m) and borehole thermal resistance (0.02 mK/W). Outcomes of the study confirm that coupling the best geological and technical parameters ensure the best energy performance and economic sustainability. Full article

(This article belongs to the Special Issue Energy Strategies of Smart Cities)

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30 pages, 2282 KiB

Open AccessArticle

User Experience of Navigating Work Zones with Automated Vehicles: Insights from YouTube on Challenges and Strengths

by Melika Ansarinejad, Kian Ansarinejad, Pan Lu and Ying Huang

Smart Cities 2025, 8(4), 120; https://doi.org/10.3390/smartcities8040120 - 19 Jul 2025

Understanding automated vehicle (AV) behavior in complex road environments and user attitudes in such contexts is critical for their safe and effective integration into smart cities. Despite growing deployment, limited public data exist on AV performance in construction zones; highly dynamic settings marked [...] Read more.

Understanding automated vehicle (AV) behavior in complex road environments and user attitudes in such contexts is critical for their safe and effective integration into smart cities. Despite growing deployment, limited public data exist on AV performance in construction zones; highly dynamic settings marked by irregular lane markings, shifting detours, and unpredictable human presence. This study investigates AV behavior in these conditions through qualitative, video-based analysis of user-documented experiences on YouTube, focusing on Tesla’s supervised Full Self-Driving (FSD) and Waymo systems. Spoken narration, captions, and subtitles were examined to evaluate AV perception, decision-making, control, and interaction with humans. Findings reveal that while AVs excel in structured tasks such as obstacle detection, lane tracking, and cautious speed control, they face challenges in interpreting temporary infrastructure, responding to unpredictable human actions, and navigating low-visibility environments. These limitations not only impact performance but also influence user trust and acceptance. The study underscores the need for continued technological refinement, improved infrastructure design, and user-informed deployment strategies. By addressing current shortcomings, this research offers critical insights into AV readiness for real-world conditions and contributes to safer, more adaptive urban mobility systems. Full article

(This article belongs to the Special Issue Paving the Future: Sustainable Road Design and Urban Mobility in Smart Cities)

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16 pages, 26966 KiB

Open AccessArticle

Nonlinear Heat Effects of Building Material Stock in Chinese Megacities

by Leizhen Liu, Yi Zhou, Liqing Tan and Rukun Jiang

Smart Cities 2025, 8(4), 119; https://doi.org/10.3390/smartcities8040119 - 17 Jul 2025

Urbanization is accompanied by an increased use of building materials. However, the lack of high-resolution building material stock (BMS) maps limits our understanding of the relationship between BMS and urban heat. To address this, we estimated BMS across eight typical Chinese megacities using [...] Read more.

Urbanization is accompanied by an increased use of building materials. However, the lack of high-resolution building material stock (BMS) maps limits our understanding of the relationship between BMS and urban heat. To address this, we estimated BMS across eight typical Chinese megacities using multi-source geographic data and investigated the relationship between BMS and land surface temperature (LST). The results showed that (1) the total BMS for the eight megacities was 9175.07 Mt, with Beijing and Shanghai having the largest shares. While BMS correlated significantly with population, growth patterns varied across cities. (2) Spatial autocorrelation between BMS and LST was evident. Around 16% of urban areas exhibited High–High clustering between BMS and LST, decreasing to 10% during the daytime. The relationship between BMS and LST is nonlinear, and also prominent at night, especially in Beijing. (3) Diverse building forms, especially building height, contribute to a nonlinear relationship between BMS and LST. Full article

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43 pages, 2816 KiB

Open AccessArticle

Generative AI-Driven Smart Contract Optimization for Secure and Scalable Smart City Services

by Sameer Misbah, Muhammad Farrukh Shahid, Shahbaz Siddiqui, Tariq Jamil S. Khanzada, Rehab Bahaaddin Ashari, Zahid Ullah and Mona Jamjoom

Smart Cities 2025, 8(4), 118; https://doi.org/10.3390/smartcities8040118 - 16 Jul 2025

Smart cities use advanced infrastructure and technology to improve the quality of life for their citizens. Collaborative services in smart cities are making the smart city ecosystem more reliable. These services are required to enhance the operation of interoperable systems, such as smart [...] Read more.

Smart cities use advanced infrastructure and technology to improve the quality of life for their citizens. Collaborative services in smart cities are making the smart city ecosystem more reliable. These services are required to enhance the operation of interoperable systems, such as smart transportation services that share their data with smart safety services to execute emergency response, surveillance, and criminal prevention measures. However, an important issue in this ecosystem is data security, which involves the protection of sensitive data exchange during the interoperability of heterogeneous smart services. Researchers have addressed these issues through blockchain integration and the implementation of smart contracts, where collaborative applications can enhance both the efficiency and security of the smart city ecosystem. Despite these facts, complexity is an issue in smart contracts since complex coding associated with their deployment might influence the performance and scalability of collaborative applications in interconnected systems. These challenges underscore the need to optimize smart contract code to ensure efficient and scalable solutions in the smart city ecosystem. In this article, we propose a new framework that integrates generative AI with blockchain in order to eliminate the limitations of smart contracts. We make use of models such as GPT-2, GPT-3, and GPT4, which natively can write and optimize code in an efficient manner and support multiple programming languages, including Python 3.12.x and Solidity. To validate our proposed framework, we integrate these models with already existing frameworks for collaborative smart services to optimize smart contract code, reducing resource-intensive processes while maintaining security and efficiency. Our findings demonstrate that GPT-4-based optimized smart contracts outperform other optimized and non-optimized approaches. This integration reduces smart contract execution overhead, enhances security, and improves scalability, paving the way for a more robust and efficient smart contract ecosystem in smart city applications. Full article

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40 pages, 1957 KiB

Open AccessArticle

Bridging Digital Gaps in Smart City Governance: The Mediating Role of Managerial Digital Readiness and the Moderating Role of Digital Leadership

by Ian Firstian Aldhi, Fendy Suhariadi, Elvia Rahmawati, Elisabeth Supriharyanti, Dwi Hardaningtyas, Rini Sugiarti and Ansar Abbas

Smart Cities 2025, 8(4), 117; https://doi.org/10.3390/smartcities8040117 - 13 Jul 2025

Indonesia’s commitment to digital transformation is exemplified by the Gerakan 100 Smart City program, aiming to enhance public sector performance through technology integration. This study examines how information technology capability and 21st century digital skills influence public sector performance, mediated by managerial digital [...] Read more.

Indonesia’s commitment to digital transformation is exemplified by the Gerakan 100 Smart City program, aiming to enhance public sector performance through technology integration. This study examines how information technology capability and 21st century digital skills influence public sector performance, mediated by managerial digital readiness and moderated by digital leadership. Grounded in Dynamic Capability Theory and Upper Echelon Theory, data from 1380 civil servants were analyzed using PLS-SEM via SmartPLS 4.1.0.9. Results show that both IT capability and digital skills significantly improve managerial digital readiness, which in turn positively impacts public sector performance. Managerial readiness mediates the effect of both predictors on performance, while digital leadership strengthens these relationships. Theoretically, this study frames managerial digital readiness as a dynamic capability shaped by leadership cognition. Practically, it highlights the importance of aligning infrastructure, skills, and leadership development to advance digital governance. Future research should consider longitudinal, multilevel, and qualitative designs to deepen insights. Full article

(This article belongs to the Section Applied Science and Humanities for Smart Cities)

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17 pages, 4758 KiB

Open AccessArticle

QESIF: A Lightweight Quantum-Enhanced IoT Security Framework for Smart Cities

by Abdul Rehman and Omar Alharbi

Smart Cities 2025, 8(4), 116; https://doi.org/10.3390/smartcities8040116 - 10 Jul 2025

Smart cities necessitate ultra-secure and scalable communication frameworks to manage billions of interconnected IoT devices, particularly in the face of the emerging quantum computing threats. This paper proposes the QESIF, a novel Quantum-Enhanced Secure IoT Framework that integrates Quantum Key Distribution (QKD) with [...] Read more.

Smart cities necessitate ultra-secure and scalable communication frameworks to manage billions of interconnected IoT devices, particularly in the face of the emerging quantum computing threats. This paper proposes the QESIF, a novel Quantum-Enhanced Secure IoT Framework that integrates Quantum Key Distribution (QKD) with classical IoT infrastructures via a hybrid protocol stack and a quantum-aware intrusion detection system (Q-IDS). The QESIF achieves high resilience against eavesdropping by monitoring quantum bit error rate (QBER) and leveraging entropy-weighted key generation. The simulation results, conducted using datasets TON IoT, Edge-IIoTset, and Bot-IoT, demonstrate the effectiveness of the QESIF. The framework records an average QBER of 0.0103 under clean channels and discards over 95% of the compromised keys in adversarial settings. It achieves Attack Detection Rates (ADRs) of 98.1%, 98.7%, and 98.3% across the three datasets, outperforming the baselines by 4–9%. Moreover, the QESIF delivers the lowest average latency of 20.3 ms and the highest throughput of 868 kbit/s in clean scenarios while maintaining energy efficiency with 13.4 mJ per session. Full article

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30 pages, 2849 KiB

Open AccessArticle

A Semantic Link Network Model for Supporting Traceability of Logistics on Blockchain

by Xiaoping Sun, Sirui Zhuge and Hai Zhuge

Smart Cities 2025, 8(4), 115; https://doi.org/10.3390/smartcities8040115 - 9 Jul 2025

Logistics transports of various resources such as production materials, foods, and products support the operation of smart cities. The ability to trace the states of logistics transports requires an efficient storage and retrieval of the states of logistics transports and locations of logistics [...] Read more.

Logistics transports of various resources such as production materials, foods, and products support the operation of smart cities. The ability to trace the states of logistics transports requires an efficient storage and retrieval of the states of logistics transports and locations of logistics objects. However, the restriction of sharing states and locations of logistics objects across organizations makes it hard to deploy a centralized database for supporting traceability in a cross-organization logistics system. This paper proposes a semantic data model on Blockchain to represent a logistics process based on the Semantic Link Network model, where each semantic link represents a logistics transport of a logistics object between two organizations. A state representation model is designed to represent the states of a logistics transport with semantic links. It enables the locations of logistics objects to be derived from the link states. A mapping from the semantic links into the blockchain transactions is designed to enable the schema of semantic links and the states of semantic links to be published in blockchain transactions. To improve the efficiency of tracing a path of semantic links on a blockchain platform, an algorithm is designed to build shortcuts along the path of semantic links to enable a query on the path of a logistics object to reach the target in logarithmic steps on the blockchain platform. A reward–penalty policy is designed to allow participants to confirm the states of links on the blockchain. Analysis and simulation demonstrate the flexibility, effectiveness, and efficiency of the Semantic Link Network on immutable blockchain for implementing logistics traceability. Full article

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14 pages, 3592 KiB

Open AccessArticle

Novel Machine Learning-Based Smart City Pedestrian Road Crossing Alerts

by Song-Kyoo Kim and I Cheng Chan

Smart Cities 2025, 8(4), 114; https://doi.org/10.3390/smartcities8040114 - 8 Jul 2025

This paper presents a novel system designed to enhance pedestrian safety in urban environments by utilizing real-time video analysis and machine learning techniques. With a focus on the bustling streets of Macao, known for its high pedestrian traffic and complex road conditions, the [...] Read more.

This paper presents a novel system designed to enhance pedestrian safety in urban environments by utilizing real-time video analysis and machine learning techniques. With a focus on the bustling streets of Macao, known for its high pedestrian traffic and complex road conditions, the proposed model alerts drivers to the presence of pedestrians, significantly reducing the risk of accidents. Leveraging the You Only Look Once algorithm, this research demonstrates how timely alerts can be generated based on risk assessments derived from video footage. The model is rigorously tested against diverse driving scenarios, providing robust accuracy in detecting potential hazards. A comparative analysis of various machine learning algorithms, including Gradient Boosting and Logistic Regression, underscores the effectiveness and reliability of the system. The key finding of this research indicates that dataset refinement and enhanced feature differentiation could lead to improved model performance. Ultimately, this work seeks to contribute to the development of smart city initiatives that prioritize safety through advanced technological solutions. This approach exemplifies a vision for more responsive and responsible urban transport systems. Full article

(This article belongs to the Special Issue Intelligent Control and Planning for Urban Network Efficiency and Safety Optimization)

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50 pages, 1773 KiB

Open AccessReview

Understanding Smart Governance of Sustainable Cities: A Review and Multidimensional Framework

by Abdulaziz I. Almulhim and Tan Yigitcanlar

Smart Cities 2025, 8(4), 113; https://doi.org/10.3390/smartcities8040113 - 8 Jul 2025

Smart governance—the integration of digital technologies into urban governance—is increasingly recognized as a transformative approach to addressing complex urban challenges such as rapid urbanization, climate change, social inequality, and resource constraints. As a foundational pillar of the smart city paradigm, it enhances decision-making, [...] Read more.

Smart governance—the integration of digital technologies into urban governance—is increasingly recognized as a transformative approach to addressing complex urban challenges such as rapid urbanization, climate change, social inequality, and resource constraints. As a foundational pillar of the smart city paradigm, it enhances decision-making, service delivery, transparency, and civic participation through data-driven tools, digital platforms, and emerging technologies such as AI, IoT, and blockchain. While often positioned as a pathway toward sustainability and inclusivity, existing research on smart governance remains fragmented, particularly regarding its relationship to urban sustainability. This study addresses that gap through a systematic literature review using the PRISMA methodology, synthesizing theoretical models, empirical findings, and diverse case studies. It identifies key enablers—such as digital infrastructure, data governance, citizen engagement, and institutional capacity—and highlights enduring challenges including digital inequity, data security concerns, and institutional inertia. In response to this, the study proposes a multidimensional framework that integrates governance, technology, and sustainability, offering a holistic lens through which to understand and guide urban transformation. This framework underscores the importance of balancing technological innovation with equity, resilience, and inclusivity, providing actionable insights for policymakers and planners navigating the complexities of smart cities and urban development. By aligning smart governance practices with the United Nations’ sustainable development goals (SDG)—particularly SDG 11 on sustainable cities and communities—the study offers a strategic roadmap for fostering resilient, equitable, and digitally empowered urban futures. Full article

(This article belongs to the Collection Smart Governance and Policy)

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26 pages, 8474 KiB

Open AccessArticle

Centralised Smart EV Charging in PV-Powered Parking Lots: A Techno-Economic Analysis

by Mattia Secchi, Jan Martin Zepter and Mattia Marinelli

Smart Cities 2025, 8(4), 112; https://doi.org/10.3390/smartcities8040112 - 4 Jul 2025

The increased uptake of Electric Vehicles (EVs) requires the installation of charging stations in parking lots, both to facilitate charging while running daily errands and to support EV owners with no access to home charging. Photovoltaic (PV) generation is ideal for powering up [...] Read more.

The increased uptake of Electric Vehicles (EVs) requires the installation of charging stations in parking lots, both to facilitate charging while running daily errands and to support EV owners with no access to home charging. Photovoltaic (PV) generation is ideal for powering up EVs, both for environmental reasons and for the benefit it creates for Charging Point Operators (CPOs). In this paper, we propose a centralised V1G Smart Charging (SC) algorithm for EV parking lots, considering real EV charging dynamics, which minimises both the EV charging costs for their owners and the CPO electricity provision costs or the related CO₂ emissions. We also introduce an innovative SC benefit-splitting algorithm that makes sure SC savings are fairly split between EV owners. Eight scenarios are described, considering costs or emissions minimisation, with and without a PV system. The centralised algorithm is benchmarked against a decentralised one, and tested in an exemplary workplace parking lot in Denmark, that includes includes 12 charging stations and one PV system, owned by the same entity. Reductions of up to 11% in EV charging costs, 67% in electricity provision costs for the CPO, and 8% in CO₂ emissions are achieved by making smart use of a 35 kWp rooftop PV system. Additionally, the SC benefit-splitting algorithm successfully ensures that EV owners save money when adopting SC. Full article

(This article belongs to the Section Energy and ICT)

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33 pages, 2091 KiB

Open AccessReview

Blockchain and Smart Cities: Co-Word Analysis and BERTopic Modeling

by Abderahman Rejeb, Karim Rejeb, Heba F. Zaher and Steve Simske

Smart Cities 2025, 8(4), 111; https://doi.org/10.3390/smartcities8040111 - 1 Jul 2025

This paper explores the intersection of blockchain technology and smart cities to support the transition toward decentralized, secure, and sustainable urban systems. Drawing on co-word analysis and BERTopic modeling applied to the literature published between 2016 and 2025, this study maps the thematic [...] Read more.

This paper explores the intersection of blockchain technology and smart cities to support the transition toward decentralized, secure, and sustainable urban systems. Drawing on co-word analysis and BERTopic modeling applied to the literature published between 2016 and 2025, this study maps the thematic and technological evolution of blockchain in urban environments. The co-word analysis reveals blockchain’s foundational role in enabling secure and interoperable infrastructures, particularly through its integration with IoT, edge computing, and smart contracts. These systems underpin critical urban services such as transportation, healthcare, energy trading, and waste management by enhancing data privacy, authentication, and system resilience. The application of BERTopic modeling further uncovers a shift from general technological exploration to more specialized and sector-specific applications. These include real-time mobility systems, decentralized healthcare platforms, peer-to-peer energy exchanges, and blockchain-enabled drone coordination. The results demonstrate that blockchain increasingly supports cross-sectoral innovation, enabling transparency, trust, and circular flows in urban systems. Overall, the current study identifies blockchain as both a technological backbone and an ethical infrastructure for smart cities that supports secure, adaptive, and sustainable urban development. Full article

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25 pages, 2409 KiB

Open AccessArticle

A Study on Imbalances in Urban Internal Spatial Capacity Allocation Based on High-Precision Population and Land Value Distribution Data

by Peiru Wu, Maojun Zhai and Lingzhu Zhang

Smart Cities 2025, 8(4), 110; https://doi.org/10.3390/smartcities8040110 - 1 Jul 2025

In the context of intelligent and fine-grained urban governance, the coordinated configuration of spatial capacity and locational value has become a key proposition for optimizing urban resource utilization. Using Shanghai as a case study, this paper represents spatial capacity with population density and [...] Read more.

In the context of intelligent and fine-grained urban governance, the coordinated configuration of spatial capacity and locational value has become a key proposition for optimizing urban resource utilization. Using Shanghai as a case study, this paper represents spatial capacity with population density and locational value with land values. By quantifying the degree of spatial mismatch between population density and land values, the study reveals the imbalance between spatial capacity and locational value. First, the research calibrates the population grid data to obtain the population distribution within the study area; subsequently, a composite land value prediction model is constructed to compute the land value distribution across the study region; finally, the spatial mismatch index is calculated using the regression residual method to quantify the degree to which population density deviates from land values. The results indicate that there is a significant spatial mismatch between population density and land values in Shanghai, which unveils an imbalance in the allocation of spatial capacity within the city. This framework can be integrated into smart city digital twins and real-time monitoring platforms, providing 100 m resolution decision support for spatial resource optimization. Full article

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20 pages, 1050 KiB

Open AccessArticle

AI-Driven Sentiment Analysis for Discovering Climate Change Impacts

by Zeinab Shahbazi, Rezvan Jalali and Zahra Shahbazi

Smart Cities 2025, 8(4), 109; https://doi.org/10.3390/smartcities8040109 - 1 Jul 2025

Climate change presents serious challenges for infrastructure, regional planning, and public awareness. However, effectively understanding and analyzing large-scale climate discussions remains difficult. Traditional methods often struggle to extract meaningful insights from unstructured data sources, such as social media discourse, making it harder to [...] Read more.

Climate change presents serious challenges for infrastructure, regional planning, and public awareness. However, effectively understanding and analyzing large-scale climate discussions remains difficult. Traditional methods often struggle to extract meaningful insights from unstructured data sources, such as social media discourse, making it harder to track climate-related concerns and emerging trends. To address this gap, this study applies Natural Language Processing (NLP) techniques to analyze large volumes of climate-related data. By employing supervised and weak supervision methods, climate data are efficiently labeled to enable targeted analysis of regional- and infrastructure-specific climate impacts. Furthermore, BERT-based Named Entity Recognition (NER) is utilized to identify key climate-related terms, while sentiment analysis of platforms like Twitter provides valuable insights into trends in public opinion. AI-driven visualization tools, including predictive modeling and interactive mapping, are also integrated to enhance the accessibility and usability of the analyzed data. The research findings reveal significant patterns in climate-related discussions, supporting policymakers and planners in making more informed decisions. By combining AI-powered analytics with advanced visualization, the study enhances climate impact assessment and promotes the development of sustainable, resilient infrastructure. Overall, the results demonstrate the strong potential of AI-driven climate analysis to inform policy strategies and raise public awareness. Full article

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31 pages, 3093 KiB

Open AccessReview

A Comprehensive Review of IoT Standards: The Role of IEEE 1451 in Smart Cities and Smart Buildings

by José Rita, José Salvado, Helbert da Rocha and António Espírito-Santo

Smart Cities 2025, 8(4), 108; https://doi.org/10.3390/smartcities8040108 - 30 Jun 2025

The increasing demand for IoT solutions in smart cities, coupled with the increasing use of sensors and actuators and automation in these environments, has highlighted the need for efficient communication between Internet of Things (IoT) devices. The success of such systems relies on [...] Read more.

The increasing demand for IoT solutions in smart cities, coupled with the increasing use of sensors and actuators and automation in these environments, has highlighted the need for efficient communication between Internet of Things (IoT) devices. The success of such systems relies on interactions between devices that are governed by communication protocols which define how information is exchanged. However, the heterogeneity of sensor networks (wired and wireless) often leads to incompatibility issues, hindering the seamless integration of diverse devices. To address these challenges, standardisation is essential to promote scalability and interoperability across IoT systems. The IEEE 1451 standard provides a solution by defining a common interface that enables plug-and-play integration and enhances flexibility across diverse IoT devices. This standard enables seamless communication between devices from different manufacturers, irrespective of their characteristics, and ensures compatibility via the Transducer Electronic Data Sheet (TEDS) and the Network Capable Application Processor (NCAP). By reducing system costs and promoting adaptability, the standard mitigates the complexities posed by heterogeneity in IoT systems, fostering scalable, interoperable, and cost-effective solutions for IoT systems. The IEEE 1451 standard addresses key barriers to system integration, enabling the full potential of IoT technologies. This paper aims to provide a comprehensive review of the challenges transducer networks face around IoT applications, focused on the context of smart cities. This review underscores the significance and potential of the IEEE 1451 standard in establishing a framework that enables the harmonisation of IoT applications. The primary contribution of this work lies in emphasising the importance of adopting the standards for the development of harmonised and flexible systems. Full article

(This article belongs to the Section Internet of Things)

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26 pages, 4037 KiB

Open AccessArticle

Sustainability Assessment Framework for Urban Transportation Combining System Dynamics Modeling and GIS; A TOD and Parking Policy Approach

by Ahad Farnood, Ursula Eicker, Carmela Cucuzzella, Govind Gopakumar and Sepideh Khorramisarvestani

Smart Cities 2025, 8(4), 107; https://doi.org/10.3390/smartcities8040107 - 30 Jun 2025

Urban transportation systems face increasing pressure to reduce car dependency and greenhouse gas emissions while supporting sustainable growth. This study addresses the lack of integrated modeling approaches that capture both spatial and temporal dynamics in transport planning. It develops a novel framework combining [...] Read more.

Urban transportation systems face increasing pressure to reduce car dependency and greenhouse gas emissions while supporting sustainable growth. This study addresses the lack of integrated modeling approaches that capture both spatial and temporal dynamics in transport planning. It develops a novel framework combining System Dynamics (SD) and Geographic Information Systems (GIS) to assess the sustainability of Transit-Oriented Development (TOD) strategies and parking policies in two brownfield redevelopment sites in Montreal. The framework embeds spatial metrics, such as proximity to transit, parking availability, and active transportation infrastructure into dynamic feedback loops. Using scenario analysis, the study compares a baseline reflecting current norms with an intervention scenario emphasizing higher density near transit, reduced parking ratios, and improved walkability and bike infrastructure. The results suggest that aligning TOD principles with targeted parking limits and investments in active mobility can substantially reduce car ownership and emissions. While primarily conceptual, the model provides a foundation for location-sensitive, feedback-driven planning tools that support sustainable urban mobility. Full article

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24 pages, 875 KiB

Open AccessReview

Implementing Digital Sovereignty to Accelerate Smarter Mobility Solutions in Local Communities

by Anthony Jnr. Bokolo

Smart Cities 2025, 8(4), 106; https://doi.org/10.3390/smartcities8040106 - 29 Jun 2025

Achieving a climate neutral economy by 2050 in Europe in line with the European Green Deal places specific responsibility on the transportation sector, which contributes to greenhouse gas (GHG) emissions. For the transportation domain to reduce its GHG emissions, there is need to [...] Read more.

Achieving a climate neutral economy by 2050 in Europe in line with the European Green Deal places specific responsibility on the transportation sector, which contributes to greenhouse gas (GHG) emissions. For the transportation domain to reduce its GHG emissions, there is need to advance urban mobility solutions in local communities via the use of data in all modes of transportation. Accordingly, to intelligently improve mobility solutions, huge amounts of data are needed from citizens in local communities to improve mobility services. However, the access, usage, and ownership of data in the transportation sector continue to be hindered due to issues including privacy, security, and trust concerns, among others. However, to improve smarter mobility solutions, there is a need for clarification of digital sovereignty, which today hinders data flow among different actors in the transportation sector. Therefore, research is needed to provide an approach that enables digital sovereignty while providing innovative mobility services and applications to citizens. Accordingly, this article carried out a systematic review to explore how to maintain digital sovereignty to improve urban mobility services in local communities. Based on grounded theory and a literature review, this study explores the factors that influence digital sovereignty from local communities’ point of view. More importantly, a policy framework is proposed to improve sovereign data usage control for citizens. Additionally, recommendations for achieving digital sovereignty are presented to foster data ecosystem business opportunities for mobility service providers and to increase data autonomy, trust, and transparency for citizens. Full article

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25 pages, 7855 KiB

Open AccessArticle

Latency-Sensitive Wireless Communication in Dynamically Moving Robots for Urban Mobility Applications

by Jakub Krejčí, Marek Babiuch, Jiří Suder, Václav Krys and Zdenko Bobovský

Smart Cities 2025, 8(4), 105; https://doi.org/10.3390/smartcities8040105 - 25 Jun 2025

Reliable wireless communication is essential for mobile robotic systems operating in dynamic environments, particularly in the context of smart mobility and cloud-integrated urban infrastructures. This article presents an experimental study analyzing the impact of robot motion dynamics on wireless network performance, contributing to [...] Read more.

Reliable wireless communication is essential for mobile robotic systems operating in dynamic environments, particularly in the context of smart mobility and cloud-integrated urban infrastructures. This article presents an experimental study analyzing the impact of robot motion dynamics on wireless network performance, contributing to the broader discussion on data reliability and communication efficiency in intelligent transportation systems. Measurements were conducted using a quadruped robot equipped with an onboard edge computing device, navigating predefined trajectories in a laboratory setting designed to emulate real-world variability. Key wireless parameters, including signal strength (RSSI), latency, and packet loss, were continuously monitored alongside robot kinematic data such as speed, orientation (roll, pitch, yaw), and movement patterns. The results show a significant correlation between dynamic motion—especially high forward velocities and rotational maneuvers—and degradations in network performance. Increased robot speeds and frequent orientation changes were associated with elevated latency and greater packet loss, while static or low-motion periods exhibited more stable communication. These findings highlight critical challenges for real-time data transmission in mobile IoRT (Internet of Robotic Things) systems, and emphasize the role of network-aware robotic behavior, interoperable communication protocols, and edge-to-cloud data integration in ensuring robust wireless performance within smart city environments. Full article

(This article belongs to the Special Issue Smart Mobility: Linking Research, Regulation, Innovation and Practice)

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25 pages, 11137 KiB

Open AccessArticle

Driving Equity: Can Electric Vehicle Carsharing Improve Grocery Access in Underserved Communities? A Case Study of BlueLA

by Ziad Yassine, Elizabeth Deakin, Elliot W. Martin and Susan A. Shaheen

Smart Cities 2025, 8(4), 104; https://doi.org/10.3390/smartcities8040104 - 25 Jun 2025

Carsharing has long supported trip purposes typically made by private vehicles, with grocery shopping especially benefiting from the carrying capacity of a personal vehicle. BlueLA is a one-way, station-based electric vehicle (EV) carsharing service in Los Angeles aimed at improving access in low-income [...] Read more.

Carsharing has long supported trip purposes typically made by private vehicles, with grocery shopping especially benefiting from the carrying capacity of a personal vehicle. BlueLA is a one-way, station-based electric vehicle (EV) carsharing service in Los Angeles aimed at improving access in low-income neighborhoods. We hypothesize that BlueLA improves grocery access for underserved households by increasing their spatial-temporal reach to diverse grocery store types. We test two hypotheses: (1) accessibility from BlueLA stations to grocery stores varies by store type, traffic conditions, and departure times; and (2) Standard (general population) and Community (low-income) members differ in perceived grocery access and station usage. Using a mixed-methods approach, we integrate walking and driving isochrones, store data (n = 5888), trip activity data (n = 59,112), and survey responses (n = 215). Grocery shopping was a key trip purpose, with 69% of Community and 61% of Standard members reporting this use. Late-night grocery access is mostly limited to convenience stores, while roundtrips to full-service stores range from 55 to 100 min and cost USD 12 to USD 20. Survey data show that 84% of Community and 71% of Standard members reported improved grocery access. The findings highlight the importance of trip timing and the potential for carsharing and retail strategies to improve food access. Full article

(This article belongs to the Special Issue Cost-Effective Transportation Planning for Smart Cities)

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18 pages, 1606 KiB

Open AccessArticle

Comparative Analysis of Traffic Detection Using Deep Learning: A Case Study in Debrecen

by João Porto, Pedro Sampaio, Peter Szemes, Hemerson Pistori and Jozsef Menyhart

Smart Cities 2025, 8(4), 103; https://doi.org/10.3390/smartcities8040103 - 24 Jun 2025

This study evaluates deep learning models for vehicle detection in urban environments, focusing on the integration of regional data and standardized evaluation protocols. A central contribution is the creation of DebStreet, a novel dataset that captures images from a specific urban setting under [...] Read more.

This study evaluates deep learning models for vehicle detection in urban environments, focusing on the integration of regional data and standardized evaluation protocols. A central contribution is the creation of DebStreet, a novel dataset that captures images from a specific urban setting under varying weather conditions, providing regionally representative information for model development and evaluation. Using DebStreet, four state-of-the-art architectures were assessed: Faster R-CNN, YOLOv8, DETR, and Side-Aware Boundary Localization (SABL). Notably, SABL and YOLOv8 demonstrated superior precision and robustness across diverse scenarios, while DETR showed significant improvements with extended training and increased data volume. Faster R-CNN also proved competitive when carefully optimized. These findings underscore how the combination of regionally representative datasets with consistent evaluation methodologies enables the development of more effective, adaptable, and context-aware vehicle detection systems, contributing valuable insights for advancing intelligent urban mobility solutions. Full article

(This article belongs to the Section Smart Transportation)

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