Smart Cities, Volume 8, Issue 6 (December 2025) – 22 articles

Urban air mobility (UAM) introduces electric vertical takeoff and landing (eVTOL) systems, creating new requirements for infrastructure planning. Vertiport siting is central, yet existing approaches such as multi-criteria decision analysis and optimization often rely on fixed criteria and seldom integrate ground transportation, which is critical for first- and last-mile access. Large language models (LLMs) show strong capabilities in reasoning and tool orchestration, but their role in siting tasks remains underexplored. This study proposes a strategy-aware LLM-based framework that connects heterogeneous spatial data with planning strategies expressed in natural language. A reflective loop connects the planner, executor, and validator for iterative refinement using two methods: multi-criteria decision analysis for interpretable mapping and a genetic algorithm for nonlinear optimization. Experiments in Los Angeles highlight both the potential and challenges of applying LLM agents to siting: outcome evaluation shows that strategies can be translated into distinct trade-offs, while process evaluation demonstrates the benefits of iterative refinement. The study suggests that LLM-based agents can formalize qualitative strategies into reproducible workflows, indicating their potential for UAM siting and promise for broader use in urban planning. Full article

This paper presents a comprehensive review of emerging innovations and future research directions leveraging Large Language Models (LLMs) for urban data analytics, examining how cities generate, structure, and use information to support planning and operational decisions. While LLMs show promise in addressing critical challenges faced by urban stakeholders—including data integration, accessibility, and cross-domain analysis—their applications and effectiveness in urban contexts remain largely unexplored and fragmented across disciplines. Through our systematic analysis of 178 papers, we examine the impact of LLMs across the four key stages of urban data analytics: collection, preprocessing, modeling, and post-analysis. Our review encompasses various urban domains, including transportation, urban planning, disaster management, and environmental monitoring, identifying how LLMs can transform analytical approaches in these fields. We identify current trends, innovative applications, and challenges in integrating LLMs into urban analytics workflows. Based on our findings, we propose a 3E framework for future research directions: Expanding information dimensions, Enhancing model capabilities, and Executing advanced applications. This framework provides a structured approach to emphasize key opportunities in the field. Our study concludes by discussing critical challenges, including hallucination, scalability, fairness, and ethical concerns, emphasizing the need for interdisciplinary collaboration to fully realize the potential of LLMs in creating smarter, more sustainable urban environments for researchers and urban practitioners working to integrate LLMs into data-driven decision processes. Full article

Indoor air quality (IAQ) in residential settings is often dominated by high-concentration pollutant events from activities such as cooking and occupancy, which are overlooked by traditional 24 h average assessments. In this, we have designed and implemented a low-cost unit for remote IAQ monitoring. We deployed these units for high-resolution remote monitoring of CO₂, particulate matter (PM), and volatile organic compounds (VOCs) in three different domestic environments: a kitchen, a living room, and a bedroom. The monitoring campaign confirmed that, while daily averages frequently remained below guideline limits, transient peaks (e.g., CO₂ exceeding 2800 ppm in bedrooms and significant increases in PM during cooking) posed acute exposure risks. This dataset was used to train and evaluate machine learning models for 10 min ahead pollutant forecasting. Ensemble tree-based methods (Random Forest) and gradient boosting algorithms (XGBoost, LGBM, and CatBoost) were effective and robust. The predictability of the models correlated with room dynamics: performance improved under clear cyclical patterns (bedroom) and remained stable under stochastic events (kitchen). This work shows that integrating low-cost IoT sensing with machine learning enables proactive IAQ management, supporting health interventions driven by predictive risk rather than static averages. Full article

Accurate short-term load forecasting is vital for smart-city energy management, enabling real-time grid stability and sustainable demand response. This study introduces a cloud-enabled hybrid forecasting framework that integrates Seasonal Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX), Random Forest (RF), and Long Short-Term Memory (LSTM) models, unified through a residual-correction mechanism to capture both linear seasonal and nonlinear temporal dynamics. The framework performs fine-grained 5 min forecasting at both appliance and aggregate levels, revealing that the aggregate forecast achieves higher stability and accuracy than the sum of appliance-level predictions. To ensure operational resilience, three independent hybrid models are deployed across distinct cloud platforms with a two-out-of-three voting scheme, that guarantees continuity if a single-cloud interruption occurs. Using a real residential dataset from a house in Summerlin, Las Vegas (2022), the proposed system achieved a Root Mean Squared Logarithmic Error (RMSLE) of 0.0431 for aggregated load prediction representing a 35% improvement over the next-best model (Random Forest) and maintained consistent prediction accuracy during simulated cloud outages. These results demonstrate that the proposed framework provides a scalable, fault-tolerant, and accurate energy forecasting. Full article

A critical task in infrastructure security is to model user traffic in transportation systems to alert whenever anomalous behavior is observed. Discerning those abnormal samples is possible by auditing the available data, which then enables proper policy making to guarantee fair tariffs and the design of strategies to tackle problems such as passenger congestion. In this paper, we present an offline cybersecurity approach for the multimodal modeling of user traffic for the Colombian metro. To identify the anomalies, we design custom Deep Autoencoders based on the embeddings produced by the Self-Supervised Learning TabNet architecture. Additionally, we provide explainability through a SHAP-based component and the analysis of external image data using LLaVA as the selected Large Multimodal Model. The results indicate that most problems that occur on one metro line also affect the other, demonstrating the interconnectivity of the metro system, a crucial aspect that motivates the coordinated emergency response to improve the passenger travel experience. Although the detected problems might already have been identified and reported on social media, the transparency provided helps create confidence when an abnormality is observed, and in case there is no backup information on our official external data sources, it represents an alert to examine it more deeply, becoming an intelligent assessment tool for the metro. This article also sheds light on the potential of the publicly available dataset used and the importance of expanding its existing variables and information. Full article

This paper presents a framework that integrates real-time onboard (ego vehicle) perception module with edge processing capabilities and a cloud-based digital twin for intelligent transportation systems (ITSs) in smart city applications. The proposed system combines onboard 3D object detection and tracking with low latency edge-to-cloud communication, achieving an average end-to-end latency below 0.02 s at 10 Hz update frequency. Experiments conducted on a real autonomous vehicle platform demonstrate a mean Average Precision (mAP@40) of 83.5% for the 3D perception module. The proposed system enables real-time traffic visualization while enabling scalable data management by reducing communication overhead. Future work will extend the system to multi-vehicle deployments and incorporate additional environmental semantics such as traffic signal states, road conditions, and predictive Artificial Intelligence (AI) models to enhance decision support in dynamic urban environments. Full article

This study investigates the intersection of digital tools and methods with the built environment disciplinary framework, focusing on Urban Planning and Development (UPD), Architecture, Engineering, and Construction (AEC), and Cultural Heritage (CH) domains. Using a systematic literature review of 29 solution-oriented documents, the research applies both bibliometric and in-depth content analysis to identify methodological patterns. Co-occurrence mapping revealed four thematic clusters—Data Integration and User-Centric Analysis, Advanced 3D Spatial Analysis and Processing, Real-Time Interaction and Digital Twin Support, and 3D Visualization—each corresponding to distinct stages in a digital workflow, from data acquisition to interactive communication. Comparative and interdependency analyses demonstrated that these clusters operate in a sequential yet interconnected manner, with Data Integration forming the foundation for analysis, simulation, and visualization tasks. While current solutions are robust within individual stages, they remain fragmented, indicating a need for systemic interoperability. The findings underscore the opportunity to develop integrated digital platforms that synthesize these clusters, enabling more comprehensive observation, management, and planning of the built environment. Such integration could strengthen decision-making frameworks, enhance public participation, and advance sustainable, smart city development. Full article

Traffic anomaly detection plays a crucial role in improving road safety and enabling timely responses to abnormal events. Recent research has explored generative and predictive models to enhance detection accuracy; however, the dynamic and complex nature of traffic scenes often introduces noise and uncertainty, reducing reliability. This work presents TGU-Net, a Temporal Generative U-Net framework designed for real-time traffic anomaly detection in urban environments. The proposed model integrates two key innovations: (1) a temporal modeling component that captures dependencies across consecutive frames, and (2) contextual scene enrichment that enhances the distinction between normal and anomalous behaviors. These additions mitigate reconstruction noise and improve detection robustness without compromising computational efficiency. Experimental evaluations on a synthetically generated CARLA-based dataset demonstrate that TGU-Net achieves strong performance in precision, recall, and early anomaly detection, confirming its potential as a scalable and reliable framework for real-world traffic monitoring systems. Full article

Accurate traffic modeling is essential for effective urban mobility planning within Smart Cities. Conventional capacity assessment methods assume rule-based driver behavior and therefore neglect psychological priority, an informal interaction in which drivers negotiate right-of-way contrary to traffic regulations. This study investigates how the absence of this behavioral factor affects the accuracy of delay and capacity evaluation at unsignalized intersections. A 12 h field observation was conducted at an intersection in Prešov, Slovakia, and 28 driver interactions were analyzed using linear regression modeling. The derived model (R² = 0.83, p < 0.05) demonstrates that incorporating psychological priority significantly improves the agreement between calculated and observed waiting times. Unrealistic results occurring under oversaturated conditions in standard methodologies were eliminated. The findings confirm that behavioral variability has a measurable impact on traffic performance and should be reflected in analytical and simulation models. Integrating these behavioral parameters into Smart City traffic modeling contributes to more realistic and human-centered decision-making in intersection design and capacity management, supporting the development of safer and more efficient urban mobility systems. Full article

Efficient scheduling of virtual power plants (VPPs) is essential for the integration of distributed energy resources into modern power systems. This study presents a CUDA-accelerated Multiple-Chain Simulated Annealing (MC-SA) algorithm tailored for optimizing VPP scheduling. Traditional Simulated Annealing algorithms are inherently sequential, limiting their scalability for large-scale applications. The proposed MC-SA algorithm mitigates this limitation by executing multiple independent annealing chains concurrently, enhancing the exploration of the solution space and reducing the requisite number of sequential cooling iterations. The algorithm employs a dual-level parallelism strategy: at the prosumer level, individual energy producers and consumers are assessed in parallel; at the algorithmic level, multiple Simulated Annealing chains operate simultaneously. This architecture not only expedites computation but also improves solution accuracy. Experimental evaluations demonstrate that the CUDA-based MC-SA achieves substantial speedups—up to 10× compared to a single-chain baseline implementation while maintaining or enhancing solution quality. Our analysis reveals an empirical power-law relationship between parallel chains and required sequential iterations (iterations ∝ chains^{−0.88±0.17}), demonstrating that using 50 chains reduces the required number of sequential iterations by approximately 10× compared to single-chain SA while maintaining equivalent solution quality. The algorithm demonstrates scalable performance across VPP sizes from 250 to 1000 prosumers, with approximately 50 chains providing the optimal balance between solution quality and computational efficiency for practical applications. Full article

Rio de Janeiro’s favelas house over 20% of the city’s population in just 5% of its territory, with Rio das Pedras emerging as a critical case study: ranking as Brazil’s fifth most populous favela and its most vertically intensified. This study quantifies how uncontrolled vertical growth in informal settlements disrupts microclimate dynamics, directly impacting thermal comfort. Using high-resolution geospatial analytics, we integrated digital surface models (DSMs) derived from LiDAR and photogrammetric data (2013, 2019, and 2024) with microclimatic simulations to assess urban morphology changes and their thermal effects. A spatiotemporal cadastral analysis tracked vertical expansion (new floors) and demolition patterns, while ENVI-met simulations mapped air temperature anomalies across decadal scenarios. Results reveal two key findings: (1) rapid, unregulated construction has significantly altered local airflow and surface energy balance, exacerbating the urban heat island (UHI) effect; (2) microclimatic simulations consistently recorded elevated temperatures, with the most pronounced impacts in densely built zones. These findings underscore the need for public policies to mitigate such negative effects observed in informal settlement areas. Full article

Traditional multimodal public transportation recommenders often overlook in-vehicle crowding, a critical factor that causes passenger discomfort and leads to an inefficient distribution of people across the network that affects its reliability. To address this, we propose a proof of concept for a novel framework that directly integrates crowding into its optimization process, balancing it with user preferences such as travel habits, travel time, and line changes. Built on the Behavior-Enabled IoT (BeT) paradigm, our system is designed to manage the crucial QoE and QoS trade-off inherent in smart mobility. We validate our balanced strategy using real-world data from Lyon, comparing it against two baselines: a QoE-driven model that prioritizes user habits and a QoS-driven model that focuses solely on network efficiency. Our Wilcoxon-based statistical analysis demonstrates that a balanced strategy is the most effective approach for substantially mitigating public transit crowding. Our Wilcoxon-based statistical analysis demonstrates that a balanced strategy is the most effective approach for mitigating public transit crowding, since it leads to a substantial decrease in crowding. Despite a potential increase in travel times, our solution respects user habits and avoids excessive transfers, providing significant operational improvements without compromising passenger convenience. Full article

The inspiration and main goal for creating smart cities is to improve the quality of urban life. However, this ambitious task is not always successful as urban stakeholders are not homogeneous. Their experiences and expectations can vary significantly, which ultimately affects their level of satisfaction with life in the city. This article assesses the quality of life in 19 cities with county rights located in the Silesian province of Poland. The assessment takes into account stakeholders’ age, gender, education, and household size. The study also assesses the geographical variation in the quality of life in individual cities in the region with a view to individualizing the management approach. The research methodology is based on a survey conducted in a representative sample of 1863 residents of Silesian cities. The results are analyzed using descriptive statistics and nonparametric tests. The conclusions indicate a lower quality of life for women, residents aged 31 to 40, and people with primary education and a bachelor’s degree. The quality of life is significantly worse in post-mining towns where economic transformation has not been successfully implemented. The quality of urban life is rated highest by men, older people, and residents with basic and secondary education. Communities living in cities with modern industry and a stable economic situation are very satisfied with their standard of living. The results of the study imply the need for an individualized approach to shaping living conditions in cities and the implementation of remedial measures for groups and cities at risk of a lower quality of life. This will help to balance the quality of urban life and prevent various forms of exclusion. Full article

Technological advancements alone, without addressing public responses to social changes cannot ensure inclusive and sustainable smart city transitions as cities and societies comprise diverse individuals and communities with varied socio-demographic backgrounds. Thus, this research investigates social responses to smart city transitions aiming to understand individuals’ social reactions to the changes across diverse socio-demographic profiles, and identify socio-demographic group-specific change management strategies to enhance public engagement and minimise resistance during the transition. Through a questionnaire survey using multivariate analysis, correlations between socio-demographic profiles and social reactions are identified. Age and frustration showed a positive correlation indicating that elderly individuals express greater concerns about unfamiliar smart technologies. Weak negative correlations emerged between income levels and transition-related stress including shock, frustration and depression. Significant differences were revealed between income groups (AUD 126,000+ and below AUD 90,000) associated with job security due to smart technologies and digital automation. Improving digital proficiency through free local government-led training, and reinforcing the benefits of digitally transformed urban environments through timely technical support were identified as the most essential change management strategies. Thus, this research will contribute to enabling local governments and policymakers to have practical insights in developing socially inclusive and community-centric transition plans with minimised social resistance. Full article

Urban tree detection is fundamental to effective forestry management, biodiversity preservation, and environmental monitoring—key components of sustainable smart city development. This study introduces a deep learning framework for urban tree crown segmentation that exclusively leverages high-resolution satellite imagery from GeoEye-1, WorldView-2, and WorldView-3, thereby eliminating the need for additional data sources such as LiDAR or UAV imagery. The proposed framework employs a Residual U-Net architecture augmented with Attention Gates (AGs) to address major challenges, including class imbalance, overlapping crowns, and spectral interference from complex urban structures, using a custom composite loss function. The main contribution of this work is to integrate data from three distinct satellite sensors with varying spatial and spectral characteristics into a single processing pipeline, demonstrating that such well-established architectures can yield reliable, high-accuracy results across heterogeneous resolutions and imaging conditions. A further advancement of this study is the development of a hybrid ground-truth generation strategy that integrates NDVI-based watershed segmentation, manual annotation, and the Segment Anything Model (SAM), thereby reducing annotation effort while enhancing mask fidelity. In addition, by training on 4-band RGBN imagery from multiple satellite sensors, the model exhibits generalization capabilities across diverse urban environments. Despite being trained on a relatively small dataset comprising only 1200 image patches, the framework achieves state-of-the-art performance (F1-score: 0.9121; IoU: 0.8384; precision: 0.9321; recall: 0.8930). These results stem from the integration of the Residual U-Net with Attention Gates, which enhance feature representation and suppress noise from urban backgrounds, as well as from hybrid ground-truth generation and the combined BCE–Dice loss function, which effectively mitigates class imbalance. Collectively, these design choices enable robust model generalization and clear performance superiority over baseline networks such as DeepLab v3 and U-Net with VGG19. Fully automated and computationally efficient, the proposed approach delivers cost-effective, accurate segmentation using satellite data alone, rendering it particularly suitable for scalable, operational smart city applications and environmental monitoring initiatives. Full article

Networked Microgrids (NMGs) have revolutionized the energy landscape by enhancing grid flexibility and decentralizing power generation, playing a pivotal role in the development of smart cities. Distributed Energy Resources (DERs) are a fundamental component of a typical NMG; hence, their trustworthiness is of utmost importance for the reliable and efficient operation of NMGs within smart city environments. However, the processing and analysis of unstructured data when performing trust assessments of these DERs is still not well explored. This research fills this gap by proposing a new trust framework that leverages the advanced capabilities of Neural Networks to assess the trustworthiness of DERs in NMGs. Furthermore, the proposed framework analyzes and converts the unstructured data from DERs into a structured format for generating trust scores for DERs. There are two primary components of this framework: (1) an SLM (Small Language Model)-based module for data analysis, (2) a neural network-based module for trust score calculation. These two components provide an end-to-end process for transforming an unstructured input into meaningful trust metrics. Several experiments were conducted to evaluate the performance of the proposed framework, and it turned out that the results produced by the proposed framework were highly precise, accurate and consistent. Furthermore, the proposed framework outperformed the existing frameworks in size and efficiency, making it a promising solution for trustworthy DER management in smart city microgrid ecosystems. Full article

Bike lending offers a service that enables individuals to borrow bicycles for short-term use (i.e., ranging from 2 hours to 36 months), typically from designated locations within cities, campuses, or communities. Unlike bikesharing systems that typically rely on automated kiosks and/or undocked and free-floating devices for public access, bike lending involves a managed program with staff, similar to a library model. These programs can be administered by community organizations, bike shops, public libraries, and other local entities. They are typically community- or membership-based, with many programs associated with non-profit organizations or publicly owned and operated. In this paper, we investigate bike lending in the United States and Canada as of Spring 2024, including a literature review, the identification and characterization of bike lending programs (n = 55), expert interviews (n = 24), a survey of bike lending operators (n = 31), and 2 focus groups with a total of 12 participants. Insights from expert interviews and operator surveys highlight the experiences of professionals involved in bike lending. The focus groups capture the experiences of bike lending users. This paper finds that North American bike lending is often tailored to the specific needs of communities, such as youth, low-income individuals, and the general population. More sustained funding could support program expansion and diversify bike offerings. Enhancing cycling infrastructure, such as adding dedicated bike lanes and paths, could improve overall cycling safety and increase participation in bike lending programs. This study’s findings could help strengthen existing bike lending programs, guide the development of new initiatives and supportive policies, and enhance safe bicycle use for participants. Full article

Extreme weather events, such as storms, pose significant challenges to the reliability and efficiency of urban road networks, making intersection design and management critical to maintaining mobility. This paper addresses the dual objectives of traffic efficiency and resilience by evaluating the performance of roundabouts, signalized, and two-way stop-controlled (TWSC) intersections under normal and storm-disrupted conditions. A mixed-method approach was adopted, combining a heuristic framework from the Highway Capacity Manual with microsimulations in AIMSUN Next. Three Polish case studies were examined; each was modeled under alternative control strategies. The findings demonstrate the superior robustness of roundabouts, which retain functionality during power outages, while signalized intersections reveal vulnerabilities when control systems fail, reverting to less efficient TWSC behavior. TWSC intersections consistently exhibited the weakest performance, particularly under high or uneven traffic demand. Despite methodological differences in delay estimation, the convergence of results through Level of Service categories strengthens the reliability of findings. Beyond technical evaluation, the study underscores the importance of resilient intersection design in climate-vulnerable regions and the value of integrating analytical and simulation-based methods. By situating intersection performance within urban resilience, this research provides actionable insights for policymakers, planners, and engineers seeking to balance efficiency with adaptability in infrastructure planning. Full article

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