Smart Cities

Observing and measuring public life is essential for designing inclusive, vibrant, and climate-resilient public spaces. While urban planners have traditionally relied on manual observation, recent advances in open-source Computer Vision (CV) now enable automated analysis. However, most CV sensors in urban studies focus on transportation analysis, offering limited insight into nuanced human behaviors such as sitting or socializing. This limitation stems in part from the challenges CV algorithms face in detecting subtle activities within public spaces. This study introduces the Public Life Sensor Kit (PLSK), an open-source, do-it-yourself system that integrates a GoPro camera with an NVIDIA Jetson edge device, and evaluates whether pose estimation-enhanced CV models can improve the detection of fine-grained public life behaviors, such as sitting and social interaction. The PLSK was deployed during a public space intervention project in Sydney, Australia. The resulting data were measured against data collected from the Vivacity sensor, a commercial transportation-focused CV system, and traditional human observation. The results show that the PLSK outperforms the commercial sensor in detecting and classifying key public life activities, including pedestrian traffic, sitting, and socializing. These findings highlight the potential of the PLSK to support ethically collected and behavior-rich public space analysis and advocate for its adoption in next-generation urban sensing technologies. Full article

Smart mobility is widely promoted as a solution to urban congestion, pollution, and inefficiency. Yet, its adoption remains inconsistent, particularly in developing and small cities. While prior research has examined technological enablers, the structural and systemic barriers that constrain adoption are less understood. This study identifies and analyzes the institutional, political, technological, and socio-cultural barriers that collectively inhibit smart mobility transitions. Using Total Interpretive Structural Modelling (TISM) and MICMAC analysis, the study hierarchically maps 14 interrelated barriers derived from literature and validated through expert consultation. Findings reveal that legacy paradigms in conventional transport planning, fragmented institutional mandates, and regulatory misalignment are the foundational constraints that reinforce downstream challenges such as affordability, limited service coverage, and user resistance. Anchored in Critical Urban Theory, the study reframes smart mobility adoption as a contested and political process shaped by institutional inertia and unequal access to technology. The paper contributes to the literature by offering a theory-informed diagnostic framework for understanding mobility transitions. It also provides practical entry points for policymakers, planners, and mobility innovators seeking to target root cause interventions rather than symptoms, to enable more equitable, scalable, and resilient smart mobility transitions. Full article

Autonomous vehicles (AVs) hold strong potential to redefine traffic operations, yet their impacts at varying penetration levels within mixed traffic remain insufficiently quantified. This study evaluates the influence of SAE Level 5 AVs on traffic performance at two typical urban signalized intersections using a hybrid microsimulation approach that integrates behavioral AV modeling and performance evaluation. The analysis covers two typical intersection layouts, one with two through lanes and another with three, tested under varying traffic volumes and left-turn shares. A total of 324 simulation scenarios were conducted with AV penetration ranging from 0% to 100% (in 20% increments) and left-turn proportions of 15%, 30%, and 45%. The results show that 100% AV penetration lowers the average delay by up to 40% in the two-lane intersection scenario and 32% in the three-lane scenario, relative to the 0% AV baseline. Even 20% AV penetration yields about half of the maximum improvement. The greatest benefits occur with aggressive AV driving profiles, balanced approach volumes, and small left-turn shares. These findings provide preliminary evidence of AVs’ potential to enhance intersection efficiency and support Sustainable Development Goals (SDGs) 11 and 13, offering insights to guide intersection design and AV deployment strategies for data-driven, sustainable urban mobility. Full article

This study investigates how digital infrastructure contributes to smart city performance in emerging economic contexts and whether its impact is shaped by governance models. We estimate the effect of a Digital Technology Index on a composite Smart City Index, employing a generalized least squares (GLS) random-effects model to address heteroskedasticity and serial correlation. The analysis reveals a robust and statistically significant relationship: a one-standard-deviation increase in digital infrastructure corresponds to a 0.7-standard-deviation rise in smart city performance. The relationship is piecewise-linear, stagnating in the early stage before rising sharply after a threshold. To interpret these results, we draw on a qualitative case study of Wang Chan Valley (WCV), a science and innovation hub in Thailand’s Eastern Economic Corridor. WCV exemplifies how early-stage digital investment can amplify smart development outcomes and generate spillover effects across the broader urban region. The case reinforces the hypothesis that digital infrastructure embedded within participatory innovation ecosystems yields greater and more sustainable smart-city gains than technology investment alone. Taken together, the findings contribute to the understanding of how governance mediates the effectiveness of digital infrastructure in driving smart urban transformation within emerging economies. Full article

Crisis management in smart cities demands coherent, interoperable, and reusable semantic models to represent complex systems, their risks, crisis situations, interdependencies, and decision-making processes across all lifecycle phases, i.e., prevention, preparedness, response, and recovery. This paper presents the TERMINUS (TERritorial Management and INfrastructures ontology for institutional and industrial USage) ontology, a BFO (Basic Formal Ontology)-aligned conceptual model based on semantic patterns for the crisis management lifecycle operationalized as both ontology design patterns (ODPs) to structure the ontology and ontology query patterns (OQPs) to use it in specific contexts. ODPs capture reusable conceptual structures for modeling domains, while OQPs provide SPARQL (SPARQL Protocol and RDF Query Language)-based templates to retrieve and reason over knowledge graph instances derived from these model chunks. The approach ensures semantic continuity from conceptual modeling to operational applications, enabling automated scenario generation, cascading risk analysis, and participatory decision-making. We position the patterns within the crisis management lifecycle and demonstrate their use through real-world case studies, covering semantic spatio-temporal risk assessment, interdependent infrastructure risk cascades, creative emergency scenario design, and recovery planning. Evaluation results highlight the ontology’s ability to support domain experts in generating plausible context-specific models, fostering collaborative validation, and enhancing preparedness and resilience. TERMINUS thus provides a versatile and interoperable semantic infrastructure for integrating ontologies and knowledge graphs into urban crisis management workflows. Full article

To address the problem of selecting an “appropriate” charging station for emergency charging during the journey of electric vehicles, this paper proposes a basic architecture of an intelligent charging navigation system composed of the power system, traffic system, charging stations, and on-board navigation terminals. The concept of a charging time window is introduced into a “reservation-based charging + consumption” service model for electric vehicle charging prediction. On this basis, a dynamic dispatching model based on a rolling time axis is designed, enabling the charging process of users to be freed from the constraints of queuing time and time-dependent charging service fees. Case simulations show that intelligent charging navigation for electric vehicles based on reservation charging service can effectively improve the users’ charging experience while taking into account both the operating state of the power grid and the benefits of charging station operators. Full article

The rise of the smart city reflects a transformative shift in urban development, defined by the integration of advanced technologies and data-driven solutions seeking to address rapid urbanization, environmental externalities, and the ever-increasing pressing need for optimal resource use. Nevertheless, a better understanding of the factors that shape citizens’ behavioral intentions towards smart-city living is becoming a sheer necessity. This study is among the first to empirically examine determinants describing the propensity to use smart-city services in an urban setting of south-eastern Europe. In this regard, we employ the smart-city stakeholders’ adoption (SSA) model in order to shed light on smart-city technology acceptance, further focusing on the underlying impact of demographics in shaping citizen attitudes and perceptions. Findings suggest that key predictors of acceptance (latent variables describing self-efficacy, price value, and trust in technology), all positively affect behavioral intention while the non-significance of effort expectancy contradicts the relevant results of previous studies and warrants further investigation. Furthermore, the analysis supports the theorized indirect effects of the model, whereas perceived privacy and perceived security both influence behavioral intention via trust in technology, while price value mediates the effect of citizen’s trust in government. The role of demographics was examined for potential moderating effects and was found to be significant, particularly in the case of age and education. Even though the demographic moderators we opted for do not substantially affect the explanatory power of the model, they seem to improve its specificity, particularly regarding perceptions on effort expectancy across the different demographic groups. Such results offer actionable insights on the relevance of smart-city acceptance models to the different demographic groups and in tailoring policies according to demographic segmentation groups with common characteristics. Full article

Global warming and the energy crisis are two significant challenges in the world. The prime sources of greenhouse gas emissions are the transportation and power generation sectors because they rely on fossil fuels. To overcome these problems, the world needs to adopt electric vehicles (EVs) and renewable energy sources (RESs) as sustainable solutions. The rapid evolution of electric mobility is largely driven by the development of EV charging infrastructures (EVCIs), which provide the essential support for large-scale EV adoption. As the number of CIs grows, the utility grid faces more challenges, such as power quality issues, power demand, voltage instability, etc. These issues affect the grid performance, along with the battery lifecycle of the EVs and the charging system. A charging infrastructure integrated with the RES-based microgrid (MG) is an effective way to moderate the problem. Also, these methods are about reframing how smart sustainable cities generate, distribute, and consume energy. MG-based CI operates on-grid and off-grid based on the charging demand and trades electricity with the utility grid when required. This paper presents state-of-the-art transportation electrification, MG classification, and various energy sources in the DC MG. The grid-integrated DC MG, international standards for EV integration with the grid, impacts of CI on the electrical network, and potential methods to curtail the negative impact of EVs on the utility grid are explored comprehensively. The negative impact of EV load on the voltage profile and power loss of the IEEE 33 bus system is analysed in three diverse cases. This paper also provides directions for further research on grid-integrated DC MG-based charging infrastructure. Full article

The accelerating complexity of urban environments has prompted cities to adopt digital technologies that improve efficiency, sustainability, and resilience. Among these, Urban Digital Twins (UDTw) have emerged as transformative tools for real-time representation, simulation, and management of urban systems. This Systematic Literature Review (SLR) examines the integration of Digital Twins (DTw), the Internet of Things (IoT), and Artificial Intelligence (AI) into the Smart City Development (SCD). Following the PSALSAR framework and PRISMA 2020 guidelines, 64 peer-reviewed articles from IEEE Xplore, Association for Computing Machinery (ACM), Scopus, and Web of Science (WoS) digital libraries were analyzed by using bibliometric and thematic methods via the Bibliometrix package in R. The review allowed identifying key technological trends, such as edge–cloud, architectures, 3D immersive visualization, Generative AI (GenAI), and blockchain, and classifies UDTw applications into five domains: traffic management, urban planning, environmental monitoring, energy systems, and public services. Persistent challenges have been also outlined, including semantic interoperability, predictive modeling, data privacy, and impact evaluation. This study synthesizes the current state of the field, by clearly identifying a thematic mapping, and proposes a research agenda to align technical innovation with measurable urban outcomes, offering strategic insights for researchers, policymakers, and planners. Full article

Cities face mounting pressures to deliver reliable, low-carbon services amid rapid urbanization and budget constraints. Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT) are widely promoted to automate operations and strengthen decision-support across the built environment; however, it remains unclear whether these interventions are both effective and systemically integrated across domains. We conducted a Preferred Reporting Items for Systematic Reviews (PRISMA) aligned systematic review and meta-analysis (January 2015–July 2025) of empirical AI/ML/DL/IoT interventions in urban infrastructure. Searches across five open-access indices Multidisciplinary Digital Publishing Institute (MDPI), Directory of Open Access Journals (DOAJ), Connecting Repositories (CORE), Bielefeld Academic Search Engine (BASE), and Open Access Infrastructure for Research in Europe (OpenAIRE)returned 7432 records; after screening, 71 studies met the inclusion criteria for quantitative synthesis. A random-effects model shows a large, pooled effect (Hedges’ g = 0.92; 95% CI: 0.78–1.06; p < 0.001) for within-domain performance/sustainability outcomes. Yet 91.5% of implementations operate at integration Levels 0–1 (isolated or minimal data sharing), and only 1.4% achieve real-time multi-domain integration (Level 3). Publication bias is likely (Egger’s test p = 0.03); a conservative bias-adjusted estimate suggests a still-positive effect of g ≈ 0.68–0.70. Findings indicate a dual reality: high efficacy in silos but pervasive fragmentation that prevents cross-domain synergies. We outline actions, mandating open standards and APIs, establishing city-level data governance, funding Level-2/3 integration pilots, and adopting cross-domain evaluation metrics to translate local gains into system-wide value. Overall certainty of evidence is rated Moderate based on Grading of Recommendations Assessment, Development, and Evaluation (GRADE) due to heterogeneity and small-study effects, offset by the magnitude and consistency of benefits. Full article

In the 21st century, rapid urbanisation has brought both challenges and opportunities. Smart cities have emerged as innovative solutions to meet the complex demands of urban life. Information and Communication Technology (ICT) serves as the backbone of this transformation, integrating infrastructure, public services, and environmental sustainability. Within ICT, the computing continuum has become a key paradigm for efficient resource management, while Artificial Intelligence (AI) and the Internet of Things (IoT) enhance urban planning, optimise resource use, and strengthen governance. This paper systematically reviews smart city developments from January 2020 to June 2025, focusing on technological advances and sustainability goals in databases such as Scopus, IEEE Xplore, and Web of Science. By synthesising the literature, it identifies common challenges, implementation strategies, and future directions. The review highlights the central role of the computing continuum and AI, covering enabling technologies, applications, case studies, and deployment challenges. Our findings indicate that the IoT, AI, and data analytics are currently dominant approaches, yet significant gaps remain in citizen participation, equitable access, and long-term governance. Overall, smart cities aim to integrate data, digital technologies, and intelligent infrastructure to improve the quality of life while promoting sustainable, resilient, and inclusive services. Full article

Smart and sustainable cities are often assessed using indicator-based models. However, most existing systems evaluate cities as a whole, offering limited support for project-level decision-making, particularly in small and medium-sized cities with scarce resources. This study aims to fill this gap by developing a comprehensive indicator framework tailored to the evaluation of smart city projects, designed to guide investment choices and support evidence-based planning. To build this framework, a systematic review of international indicator systems was conducted, compiling and refining over 1200 indicators into a unified taxonomy. The analysis revealed structural imbalances, with environmental and social dimensions prevailing over economic and governance aspects, and confirmed substantial redundancies, with nearly one-third of indicators overlapping. Using project actions as an analytical lens, gaps were detected and 73 evaluation areas defined. From these, anticipated impact indicators were developed and linked to corresponding performance metrics. Beyond consolidating fragmented systems, the framework provides a practical and balanced tool for multidimensional project assessment. An initial empirical pre-validation demonstrated its coverage and usability, reinforcing its potential to support planners and policymakers in comparing investment alternatives. Unlike traditional ranking or maturity models, it directly bridges the gap between abstract smart city strategies and tangible, project-level outcomes. Full article

As vehicle-centric perception struggles with occlusion and dense traffic, vehicle-infrastructure cooperative perception (VICP) offers a viable route to extend sensing coverage and robustness. This study proposes a feature-level VICP framework that fuses vehicle- and roadside-derived visual features via V2X communication. The model integrates four components: regional feature reconstruction (RFR) for transferring region-specific roadside cues, context-driven channel attention (CDCA) for channel recalibration, uncertainty-weighted fusion (UWF) for confidence-guided weighting, and point sampling voxel fusion (PSVF) for efficient alignment. Evaluated on the DAIR-V2X-C benchmark, our method consistently outperforms state-of-the-art feature-level fusion baselines, achieving improved ${AP}_{3D}$ and ${AP}_{BEV}$ (reported settings: 16.31% and 21.49%, respectively). Ablations show RFR provides the largest single-module gain +3.27% ${AP}_{3D}$ and +3.85% ${AP}_{BEV}$, UWF yields substantial robustness gains, and CDCA offers modest calibration benefits. The framework enhances occlusion handling and cross-view detection while reducing dependence on explicit camera calibration, supporting more generalizable cooperative perception. Full article

Rural communities face persistent transport disadvantages due to low population density, limited-service availability, and high operational costs, restricting access to essential services and exacerbating social inequality. Autonomous public transport systems offer a transformative solution by enabling flexible, cost-effective, and inclusive mobility options. This study investigates the operational determinants for autonomous road-based transit systems in rural and peri-urban South-East Queensland (SEQ), employing a structured survey of 273 residents and analytical approaches, including General Additive Model (GAM) and Extreme Gradient Boosting (XGBoost). The findings indicate that small shuttles suit flexible, non-routine trips, with leisure travelers showing the highest importance (Gain = 0.473) and university precincts demonstrating substantial influence (Gain = 0.253), both confirmed as significant predictors by GAM (EDF = 0.964 and EDF = 0.909, respectively). Minibus shuttles enhance first-mile and last-mile connectivity, driven primarily by leisure travelers (Gain = 0.275) and tourists (Gain = 0.199), with shopping trips identified as a significant non-linear predictor by GAM (EDF = 1.819). Standard-sized buses are optimal for high-capacity transport, particularly for school children (Gain = 0.427) and school trips (Gain = 0.148), with GAM confirming their significance (EDF = 1.963 and EDF = 0.834, respectively), demonstrating strong predictive accuracy. Hybrid models integrating autonomous and conventional buses are preferred over complete replacement, with autonomous taxis raising equity concerns for low-income individuals (Gain = 0.047, indicating limited positive influence). Integration with Mobility-as-a-Service platforms demonstrates strong, particularly for special events (Gain = 0.290) and leisure travelers (Gain = 0.252). These insights guide policymakers in designing autonomous road-based transit systems to improve rural connectivity and quality of life. Full article

Object detection is a critical technology for smart city development. As request volumes surge, inference is increasingly offloaded from centralized clouds to user-proximal edge sites to reduce latency and backhaul traffic. However, heterogeneous workloads, fluctuating bandwidth, and dynamic device capabilities make offloading and scheduling difficult to optimize in edge environments. Deep reinforcement learning (DRL) has proved effective for this problem, but in practice, it relies on manually engineered reward functions that must be redesigned whenever service objectives change. To address this limitation, we introduce an LLM-driven framework that retargets DRL policies for edge object detection directly through natural language instructions. By leveraging understanding of the text and encoding capabilities of large language models (LLMs), our system (i) interprets the current optimization objective; (ii) generates an executable, environment-compatible reward function code; and (iii) iteratively refines the reward via closed-loop simulation feedback. In simulations for a real-world dataset, policies trained with LLM-generated rewards adapt from prompts alone and outperform counterparts trained with expert-designed rewards, while eliminating manual reward engineering. Full article

The rapid evolution of 5G New Radio networks has introduced a wide range of services with diverse requirements, complicating their coexistence within the shared radio spectrum and posing challenges in traffic scheduling and resource allocation. This study aims to analyze and categorize the methods, approaches, and techniques proposed to ensure efficient joint and dynamic packet scheduling and resource allocation among heterogeneous services—namely eMBB, URLLC, and mMTC—in 5G and beyond, with a focus on Quality of Service and user satisfaction. This scoping review draws from publications indexed in IEEE Xplore and Scopus and synthesizes the most relevant evidence related to packet scheduling across heterogeneous services, highlighting key approaches, core performance metrics, and emerging trends. Following the PRISMA-ScR methodology, 48 out of an initial 140 articles were included for explicitly addressing coexistence, scheduling, and resource allocation. The findings reveal a research emphasis on eMBB and URLLC coexistence, while integration with mMTC remains underexplored. Moreover, the evidence suggests that hybrid and deep learning-based approaches are particularly promising for tackling coexistence and resource management challenges in future mobile networks. Full article

The growing need for efficient and sustainable urban water management has accelerated the adoption of smart monitoring infrastructures based on wireless sensor networks (WSNs). This study proposes a connectivity-aware methodology for the optimal deployment of wireless sensor networks (WSNs) in smart water metering systems. The approach models the wireless sensors as nodes embedded in household water meters and determines the minimal yet sufficient set of Data Aggregation Points required to ensure complete network coverage and transmission reliability. A scalable and hierarchical topology is generated by integrating an enhanced minimum spanning tree algorithm with set covering techniques and geographic constraints, leading to a robust intermediate layer of aggregation nodes. These nodes are wirelessly linked to a single cellular base station, minimizing infrastructure costs while preserving communication quality. Simulation results on realistic urban layouts demonstrate that the proposed strategy reduces network fragmentation, improves energy efficiency, and simplifies routing paths compared to traditional ad hoc designs. The results offer a practical framework for deploying resilient and cost-effective smart water metering solutions in densely populated urban environments. Full article

One of the main challenges faced by DC microgrid (DCMG) is their low inertia, which leads to rapid and significant voltage fluctuations during load or generation changes. These fluctuations can negatively impact sensitive loads and protection devices. Previous studies have addressed this by enabling battery converters to mimic the behavior of synchronous generators (SGs), but this approach becomes ineffective when the converters or batteries reach their current or energy limits, leading to a loss of inertia and potential system instability. In interconnected multi-microgrid (MMG) systems, the presence of multiple batteries offers the potential to enhance system inertia, provided there is a coordinated control strategy. This research introduces a hierarchical control method that combines decentralized and centralized approaches. Decentralized control allows individual converters to emulate SG behavior, while the centralized control uses Internet of Things (IoT) technology to enable real-time coordination among all Energy Storage Units (ESUs). This coordination improves inertia across the DCMMG system, enhances energy management, and strengthens overall system stability. IoT integration ensures real-time data exchange, monitoring, and collaborative decision-making. The proposed scheme is validated through MATLAB simulations, with results confirming its effectiveness in improving inertial response and supporting the integration of renewable energy sources within DCMMGs. Full article

Rapid urbanization and the proliferation of heterogeneous urban data have intensified the challenges of semantic interoperability and integrated urban governance. To address this, we propose the Smart City Ontology Framework (SMOF), a standards-driven ontology that unifies Building Information Modeling (BIM), Geographic Information Systems (GIS), Internet of Things (IoT), and relational data. SMOF organizes five core modules and eleven major entity categories, with universal and extensible attributes and relations to support cross-domain data integration. SMOF was developed through competency questions, authoritative knowledge sources, and explicit design principles, ensuring methodological rigor and alignment with real governance needs. Its evaluation combined three complementary approaches against baseline models: quantitative metrics demonstrated higher attribute richness and balanced hierarchy; LLM as judge assessments confirmed conceptual completeness, consistency, and scalability; and expert scoring highlighted superior scenario fitness and clarity. Together, these results indicate that SMOF achieves both structural soundness and practical adaptability. Beyond structural evaluation, SMOF was validated in two representative urban service scenarios, demonstrating its capacity to integrate heterogeneous data, support graph-based querying and enable ontology-driven reasoning. In sum, SMOF offers a robust and scalable solution for semantic data integration, advancing smart city governance and decision-making efficiency. Full article