Search Results (1,496)

This review examines how smart city technologies have influenced health equity in urban settings between 2019 and 2026, guided by a dual-lens framework that assesses both equity-related outcomes and the systemic factors enabling or constraining their realization. As cities increasingly turn to digital tools to strengthen healthcare delivery, it remains unclear whether these interventions narrow or widen existing health disparities. Drawing on interdisciplinary literature spanning public health, urban informatics, and digital governance, this synthesis finds that smart health initiatives have improved healthcare access for some underserved populations, but their overall effect on health equity remains modest, inconsistent, and highly dependent on local context. Persistent structural obstacles, including digital divides, socio-economic inequality, and fragmented governance, continue to limit equitable implementation, while institutional barriers emerge as the most widespread challenge. Conversely, people-centered design, participatory governance, inclusive digital infrastructure, and equity-sensitive policy frameworks stand out as critical enablers of more just outcomes. The review further identifies an underexplored link between environmental sustainability and smart city technology, showing that AI-driven tools addressing emissions and urban environmental quality can indirectly support more equitable health systems. Overall, the findings underscore that technological innovation alone is insufficient; realizing the equity potential of smart cities requires deliberate integration of social justice, environmental sustainability, and collaborative governance into digital health strategy and practice. Full article

Biochar is no longer considered merely a substitute for conventional fillers in asphalt materials; rather, it represents a multifunctional modifier that aligns with the goals of sustainable road design and urban mobility in smart cities. Its application now extends to the rheological modification of asphalt binders, mitigation of asphalt fume emissions, improvement in aging resistance and interfacial adhesion, and assessment of carbon sequestration potential. Biochar can improve the high-temperature stability, rutting and aging resistance, and asphalt–aggregate adhesion of asphalt materials in a suitable dosage, and at the same time reduce emissions of volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), hydrogen sulfide (H₂S), and other fumes. However, the above effects are highly dependent on the biochar feedstock, production process, physicochemical properties, particle size, dosage and degree of dispersion. An excess amount or uneven distribution will reduce the crack resistance and fatigue life at low temperatures; phase separation may also occur and VOC emissions will increase. Therefore, the main problem in this area has shifted from whether biochar is effective to when it can be applied for particular pavement performance goals, what pollutant control targets are aimed for, and over what life-cycle periods. This review integrates evidence obtained at the binder, mastic, and mixture scales and critically evaluates the influence of biochar on pavement performance, fume emissions, aging, interfacial adhesion, and environmental safety. It also argues that empirical dosage selection should be replaced by coordinated optimization of biochar structure, material performance, emission mitigation, and life-cycle impacts. Verification of the low-carbon benefits and environmental safety of biochar-modified asphalt will ultimately require standardized assessment frameworks and consistently defined system boundaries. Ultimately, this work provides a foundation for integrating biochar-modified asphalt into eco-friendly and resilient road infrastructures, aligning with the goals of smart urban mobility and sustainable transportation. Full article

This paper presents a comprehensive review of the integration of Intelligent Transportation Systems (ITS) and Digital Twin (DT) technologies for intelligent traffic management. It examines the role of key enabling technologies, including the Internet of Things (IoT), machine learning (ML), deep learning (DL), reinforcement learning (RL), Graph Neural Networks (GNNs), vehicle-to-everything (V2X) communication, and edge computing. These technologies support real-time traffic monitoring, traffic prediction, and adaptive control in ITS. The review synthesizes recent research on conventional traffic control methods, optimization-based approaches, learning-based techniques, and DT-enabled traffic management solutions. Particular attention is given to the integration of DTs with intelligent traffic signal control, real-time synchronization, multi-intersection coordination, communication latency, sensing uncertainty, and scalability. The reviewed literature demonstrates the potential of DT-enabled ITS to improve traffic efficiency, reduce congestion, enhance transportation safety, and support sustainable mobility through data-driven decision-making. However, significant challenges remain regarding communication delays, sensor and data uncertainty, computational complexity, scalability, and validation under realistic urban conditions. Based on the reviewed literature, this paper identifies key research gaps and outlines future research directions toward scalable, reliable, adaptive, and real-time DT-enabled ITS architectures for next-generation smart cities. Full article

This study investigates how integrated smart urban systems enhance resource efficiency in Saudi Arabia’s rapidly urbanising cities. Despite growing global interest in smart cities, there remains a critical gap in empirical research that simultaneously examines smart energy, water, and waste systems within a unified analytical framework, particularly in emerging urban contexts, while the mediating role of governance efficiency remains underexplored. Adopting a quantitative survey design, this study employs Structural Equation Modelling (SEM) to analyse 384 valid data from 384 stakeholders across selected urban areas. A stratified sampling approach ensures representation of policymakers, urban planners, and infrastructure managers, and measurement constructs are adapted from validated scales to ensure reliability and validity. The study examines relationships between smart energy systems, smart water management, smart waste monitoring, governance efficiency, and resource efficiency outcomes, with governance efficiency conceptualised as a mediating variable enhancing the effectiveness of smart urban systems. The study hypothesises that integrated smart technologies significantly improve resource efficiency, with smart infrastructure as a key predictor; structural model robustness is assessed using standard SEM fit indices, with CFI (0.93), TLI (0.91), and RMSEA (0.052). This research contributes to theory by integrating smart urban systems and governance into a unified framework, extending smart city and circular economy literature, while offering practical insights aligned with Saudi Vision 2030 to support sustainable urban development. It is recommended that policymakers prioritise integrated smart infrastructure, strengthen institutional frameworks, and promote public awareness to maximise resource optimisation. Future research should adopt longitudinal designs, expand across multiple cities, and incorporate behavioural and policy variables to enhance generalizability and deepen insights into smart urban resource efficiency. Full article

Unmanned aerial vehicles (UAVs) are increasingly considered as enabling technologies for last-mile delivery, emergency medical response, and smart-city applications. However, drone-based logistics, emergency communication, sensing activities, and future aerial mobility are often addressed as separate research domains. This article proposes a four-dimensional planning framework for drone-enabled mobility, integrating goods, information, sensing, and human mobility within a unified conceptual structure. The framework is developed through a literature-informed conceptual analysis and previous applied research experiences related to drone-assisted logistics and emergency communication. Goods mobility includes parcel delivery, medical logistics, emergency supply transport, and hybrid operational models involving trucks, public transport, depots, and micro-hubs. Information mobility refers to the use of drones as mobile communication tools for emergency warnings, citizen interaction, drone-to-infrastructure communication, and infomobility services. Sensing mobility concerns traffic monitoring, environmental observation, disaster mapping, crowd monitoring, and infrastructure inspection. Human mobility is considered as an emerging extension related to urban air mobility (UAM), electric vertical take-off and landing (eVTOL) systems, and low-altitude aerial corridors. Cross-cutting issues such as energy autonomy, solar-assisted drones, multimodal integration, safety, communication, regulation, sustainability, and public acceptance are discussed. The proposed framework provides a structured basis for assessing drones as components of sustainable, resilient, and multimodal mobility systems. Full article

China’s smart-city and green-transition policies have made digital-green integration an important pathway for improving urban ecological resilience. This study examines whether digital-green integration enhances urban ecological resilience, through which channels this effect occurs, and under what regional conditions it becomes more pronounced. The analysis uses a balanced panel of 510 observations for 30 Chinese provincial-level regions from 2005 to 2021, compiled from national and provincial statistical yearbooks. An indicator system encompassing resistance, recovery, and renewal is used to assess urban ecological resilience, and the composite index is calculated using the entropy-weighting method. Digital–green integration is captured by a coupling coordination index linking digital infrastructure with green finance. Its effect is estimated within a two-way fixed-effects framework, with additional tests used to assess the robustness of the findings and explore the underlying mechanisms, moderating conditions, and regional differences. The results show that: (1) digital–green integration significantly enhances urban ecological resilience, with its effect concentrated primarily in the recovery dimension; (2) industrial-structure upgrading and improvements in energy efficiency serve as important transmission channels, while better traffic infrastructure further strengthens this positive relationship; and (3) the effect is more pronounced in provinces with lower levels of urbanization and stronger environmental regulation. Governments should promote digital-green integration to support industrial-structure upgrading and energy-efficiency improvement, thereby strengthening cities’ capacity to recover from ecological shocks. Greater support should be directed to less urbanized regions, while areas with weaker environmental regulation should improve disclosure, verification, and enforcement. Future research could employ city-level data and spatial econometric models to examine the magnitude, geographic reach, and transmission mechanisms of cross-regional spillovers. Full article

Urban regeneration is an important policy tool for restructuring urban space, but its implications for operational transport-related carbon emissions remain unclear. Using the City Smart Planning System (CitySPS), this study simulates emissions in Shenzhen from 2020 to 2035 under a baseline and five policy scenarios representing temporal modification, land-use type modification and spatial replacement. The analysis uses legally designated regeneration parcels and multi-source spatial data. The accounting boundary covers CitySPS-represented operational transport-related carbon emissions from urban travel and excludes demolition, construction, building operation, embodied emissions, and other life-cycle sources. The baseline emissions rose from 1.840 × 10⁷ t CO₂ in 2020 to 2.269 × 10⁷ t CO₂ in 2035 (approximately 23%). Relative to the same-year baseline, all the policy scenarios produce higher simulated emissions in 2030 (+0.11% to +1.26%) but lower simulated emissions in 2035 (−0.34% to −1.97%). Under the evaluated configurations and the shared CitySPS assumptions, spatial replacement produces the largest simulated reduction in 2035 (−1.97%) despite an increase in 2030 (+0.41%). The results indicate time-dependent and heterogeneous outcomes across the scenario configurations. Full article

Accurate carbon footprint accounting is fundamental for urban environmental governance. However, multi-stakeholder transit networks struggle with data manipulation, privacy risks, and labor-intensive manual auditing. To resolve these trust and scalability bottlenecks, this paper introduces a tri-layer hybrid blockchain framework based on an “off-chain storage, on-chain evidence” paradigm. The architecture synergizes a relational database (MySQL) for high-throughput structured data, the InterPlanetary File System (IPFS) for decentralized raw evidence, and Hyperledger Fabric to immutably anchor dual-layer cryptographic hashes. We engineer a smart contract auditing pipeline that autonomously executes deterministic verification of hash consistency, emission thresholds, and physical logic integrity. Empirical evaluations utilizing a large-scale urban transit dataset injected with adversarial mutations demonstrate high robustness, achieving F1-scores of 1.000 across multidimensional anomalies. This replaces manual testing with statistically significant verification. Ultimately, this framework provides environmental regulators and transit authorities with a highly scalable, privacy-preserving, and trust-minimized infrastructure for continuous carbon footprint traceability. Full article

The rapid advancement of autonomous driving technologies makes the effective coordination of vehicle groups a critical requirement for ensuring both safety and efficiency in smart urban environments. Although individual autonomous vehicles may operate correctly in isolation, their collective behavior can still lead to emergent issues such as deadlocks or collisions arising from complex inter-vehicle interactions. To address this challenge, we propose a hybrid automaton-based control framework for autonomous vehicle groups that integrates both normal and emergency operational modes to jointly guarantee safety and performance. In this paper, we present the formal specification and verification of the proposed system using rewriting logic and the Maude tool. Our main contributions are threefold: (1) the construction of detailed hybrid automata models that capture vehicle dynamics and decision-making; (2) the development of formal specifications in Maude from these models; and (3) the systematic verification of key system properties, including core safety invariants, such as collision avoidance, obstacle stopping, and velocity bounds. The verification results demonstrate that the proposed model consistently upholds safety conditions, ensures that vehicles come to a safe stop before encountering obstacles, and effectively prevents collisions within the group. Full article

The global shift toward an aging population presents significant challenges for healthcare systems. This is compounded by rising disability rates, fragmented care models that struggle to meet complex needs, and a digital divide caused by the emergence of smart technologies. This work aims to evaluate how the digital divide affects the adoption, usability, and perceived benefits of smart home technologies designed to support activities of daily living and health monitoring. The influences of socioeconomic status and geographic location (urban versus rural) on the digital divide are considered. Four databases (Web of Science™, Scopus^®, PubMed^®, and IEEE Xplore^®) were searched between 2014 and 2026, resulting in 71 studies that examined older adults, smart home technologies for daily living or health monitoring, and factors related to the digital divide. Findings were synthesized using the Technology Acceptance Model, Van Dijk’s Digital Divide Framework, and Health Behavior Models. Three key barriers were identified—economic, technical, and social—which disproportionately affected vulnerable groups. Smart Home Health Technologies (SH2Techs) present adoption challenges distinct from standalone devices because they require integrated infrastructure and sustained engagement. Limited research addressing the usability of non-clinical SH2Techs has identified the need for co-design, simplified interfaces, targeted training, and policy reforms to support equitable aging in place. Full article

Advanced wireless sensor networks powered by artificial intelligence are increasingly required for applications demanding continuous monitoring, autonomous operation, reliable communication, and fast decision support. This systematic review examines recent work from 2023 to 2026 on energy-efficient, AI-enabled wireless sensor networks (WSNs) in mission-critical environments, with particular focus on power electronics and urban infrastructure systems. The authors synthesize a corpus of 50 DOI-indexed studies satisfying inclusion criteria that received qualitative thematic coding and comparative analysis. Other references were only cited to provide historical, methodological, or technical context and were not included in the systematic review corpus. As such, our results show that AI can improve WSN energy behaviour through routing and clustering, edge AI, reinforcement learning, fuzzy logic, metaheuristic optimization, and AI-based security. At the same time, energy efficiency cannot be treated as an isolated performance target. In mission-critical systems, security, latency, and reliability are closely interlinked requirements. The review concludes that future work should move away from optimizing protocols in isolation, and instead focus on building lightweight, explainable, secure, and field-tested AI-driven WSN architectures suited to real operational environments. Full article

Urban mobility systems generate large volumes of heterogeneous geospatial data that differ in temporal resolution, spatial coverage, update frequency, and semantic structure, making integrated near-real-time analysis difficult. This paper presents a geospatial big-data framework for integrating and analyzing multimodal urban mobility data from the Norwegian transport ecosystem, including public transport, micromobility, road infrastructure, weather sensing, and civil aviation. The framework is implemented as a modular pipeline for data ingestion, source-specific normalization, temporal alignment, and analytical processing, enabling minute-level comparison across heterogeneous operational feeds. The proposed approach preserves source-level semantics while supporting unified spatiotemporal analysis across transport modes with different operational characteristics. The framework is evaluated through analytical scenarios focused on peak and off-peak mobility dynamics, weather-related multimodal variability, and spatial autocorrelation of public transport activity and delay across four analysis windows and six Norwegian cities. The results show that mobility–weather relationships vary across transport modes and temporal windows, particularly in public transport activity, cycling behavior, and delay patterns, and that spatial clustering of public transport activity and delay is itself city- and window-dependent, with some cities showing strong, stable clustering and others showing none. The findings indicate that multimodal urban mobility should be interpreted as a context-dependent and interconnected spatiotemporal system rather than through isolated modal indicators. The study demonstrates how geospatial big-data integration can support near-real-time urban mobility monitoring and operational analytics in smart-city environments. Full article

The smart city concept promotes the use of innovative solutions to improve residents’ quality of life while supporting sustainable urban development. In the context of climate change and rapid technological advancement, there is a growing demand for energy-efficient cooling systems that use natural resources. This study evaluates the influence of the hydrological regime of the Warta River on its cooling potential in Poznań, one of the largest cities in Poland. Based on hydrological data collected between 1971 and 2024, the distributions of river discharge and water temperature were analysed, as these represent the two key parameters determining the feasibility of river-based free-cooling systems. Considering environmental flow requirements and water temperature thresholds, several operating scenarios were developed to simulate cooling capacities of 100, 150, and 200 MW at temperature differences (ΔT) of 3 and 5 K. Among the analysed variants, the lowest cooling demand scenario (100 MW, ΔT = 3) provided suitable operating conditions for a river-based free-cooling system during 10,582 days, corresponding to 53.6% of the study period. In contrast, the highest cooling demand scenario (200 MW, ΔT = 5) was feasible during 43.9% of the analysed period. The results indicate that the Warta River has considerable potential as a natural cooling source for free-cooling applications, although this potential exhibits pronounced seasonal variability. The highest cooling capacity can be achieved during spring and autumn, while lower capacities are available in summer and the lowest in winter. River water temperature was identified as the dominant limiting factor, accounting for approximately 96% of all cases in which free-cooling operation was not feasible. Furthermore, the observed increase in river water temperature has reduced the number of summer days during which the required cooling capacity can be achieved. The findings enable the identification of periods when river water can fully or partially replace conventional mechanical cooling systems. They also provide a framework for assessing the seasonal and operational potential of surface waters in support of future investments integrating rivers into urban cooling infrastructure. Full article

Green-building research has examined energy efficiency and indoor environmental quality in depth, whereas site-related strategies are often represented through aggregate certification outcomes with limited visibility into criterion-level evidence and weighting assumptions. This study develops the Climate-Referenced SS–LT Site-Performance Assessment Framework (CR-SSAF) for an exploratory documentation-based comparison of a combined Sustainable Sites, Location, and Transportation (SS–LT) construct across five green-certified smart office buildings in three Köppen climate zones. Six SS–LT criteria were assessed using a four-level operational rubric, an exact-normalized weighted Site-Performance Index (SSPI), a descriptive Technology Enablement Factor (TEF), ordinal inter-rater agreement analysis, three weighting schemes, and a TOPSIS ranking-concordance check. SSPI values ranged from 2.00 to 2.65. The Af case recorded the highest SSPI in this sample, while the lowest scores occurred where no qualifying project-specific heat-mitigation or green-/open-space evidence could be verified. The first, second, and last ranks remained unchanged across the three weighting schemes, while The Edge and Shanghai Tower were tied under the equal and ecology-sensitive schemes. Because the sample is small and heterogeneous, and does not control for urban form, building scale, infrastructure, certification system, or documentation availability, differences cannot be attributed independently to climate. CR-SSAF is therefore presented as a transparent exploratory workflow, not as a validated climate effects model or as evidence of transferability. Full article

Artificial Intelligence-driven detection mechanisms present both opportunities and challenges in modern systems, particularly within smart cities that rely on complex operational technologies. In the context of Saudi urban infrastructure, rapidly evolving and multidimensional cyber threats require advanced, energy-efficient security solutions. This research proposes an Artificial Intelligence-based Threat Detection Mechanism designed to proactively identify and mitigate cyber threats while maximizing energy efficiency and minimizing cost and system complexity. Purpose: The proposed theoretical framework focuses on securing sustainable smart cities by integrating Artificial Intelligence-based anomaly detection with quantum-enhanced algorithms to address high-dimensional and emerging cyber threats across interconnected urban systems. The Artificial Intelligence-based Threat Detection Mechanism enables early and proactive threat detection across sustainable smart city networks, including connections to external and global infrastructures, ensuring continuous monitoring, resilience, and service continuity. Methods: The methodology emphasizes the development of energy-efficient Artificial Intelligence models and quantum protocols, incorporating intelligent risk assessment, adaptive calibration, and automated response mechanisms. In addition, the framework introduces distributed security hubs to enhance cybersecurity robustness and scalability. Anticipated Results and Conclusions: Anticipated outcomes include improved security management policies, automated threat detection and response, and adaptive protection against evolving cyber risks. The proposed framework provides a scalable and cost-effective solution aligned with sustainability objectives. Ultimately, this research contributes a proactive and intelligent framework for securing smart city ecosystems, supporting long-term development goals and aligning with Saudi Vision 2030. Full article