Smart Cities, Volume 9, Issue 6 (June 2026) – 17 articles

Smart energy meters are widely deployed in modern distribution networks, extending their role beyond revenue billing to real-time monitoring and data-driven smart city applications. However, conventional legal metrology frameworks rely on periodic recalibration and are not intended for the detection of accuracy drift or unexpected malfunctions between scheduled inspections. In scientific publications, various techniques for remote smart meters’ error surveillance are presented, but experimental verification on real distribution network data remains limited. The objective of this study is to experimentally verify two previously proposed power event-driven methods for remote estimation of active power measurement error in individual consumer meters, using a feeder-level sum meter as a reference instrument. One-second resolution electrical readings were collected from a real low-voltage distribution branch using ESP32-based local adapters communicating via MQTT over Wi-Fi, with SNTP-based clock synchronization for power event correlation. Under optimized detection parameters, the linear regression method achieved 0.20% RMSE and 0.75% maximum absolute error, and the neural network method 0.09% RMSE and 0.31%, confirming suitability for Class 1 m accuracy surveillance. Feasibility analysis of three MQTT-based deployment scenarios demonstrates that binary encoding limits local adapter buffers to 2.8 kB and worst-case daily channel demand to 2000 kB, confirming the practical viability of the proposed architecture. Full article

Accurate solar irradiance forecasting is important for efficient planning of solar energy systems, renewable energy integration, and data-driven energy management in smart cities. This becomes more essential in regions with limited measured data availability and varying climatic conditions, where reliable forecasting can support urban energy planning and smart grid operation. Pakistan faces a scarcity of available solar data and has varying climatic conditions, which makes it ideal for such a study. This study utilizes nine geographically diverse stations to develop a benchmark framework for direct one-step-ahead hourly solar irradiance forecasting. The dataset was subjected to data preprocessing, feature engineering, and multi-model evaluation. A staged approach was adopted for feature selection, starting from a base model comprising three input variables: extraterrestrial radiation, solar zenith angle, and relative humidity. Features were added in an incremental order, which resulted in an optimized four-variable input set through the addition of a lagged clearness index to the base model. The forecasting models evaluated in this study, using these input variables, were ANN, NAR, NARX, LSTM, GRU, SARIMA, and Prophet. Deep learning models outperformed the other considered approaches, with LSTM showing the best overall benchmark performance with an average RMSE of 92.93 W/m², MAE of 66.56 W/m², and R-Squared of 0.872. The performance trends were broadly consistent across the evaluated stations, indicating stable behaviour within the adopted dataset and experimental setup. The study shows that a compact and physically interpretable input feature set, used with recurrent deep learning models, provides an effective solution for hourly solar irradiance forecasting, especially in locations with varying climatic conditions. The proposed benchmark can support smart city applications related to distributed solar generation, energy-aware urban planning, and intelligent operation of renewable-rich power systems. Full article

This paper examines the role of Multimodal Journey Planners (MJPs) as a link between user-oriented personalization and the broader societal goals of sustainable urban mobility. In smart cities, MJPs may serve as digital decision-support tools that connect individual mobility choices with broader sustainability objectives. Although contemporary journey planners increasingly display multiple criteria, such as travel time, cost, CO₂ emissions, and number of transfers, they still generally rely on predefined and non-personalized criterion weights and rarely infer travellers’ actual preferences from observed choices. The paper therefore proposes a transparent methodological proof-of-concept that combines multicriteria decision-making and inverse optimization to discover individual preference weights and enable personalized, preference-aware planning of multimodal routes. The Weighted Sum Method (WSM) is adopted as the basic ranking framework, and the proposed approach is evaluated within a controlled methodological testbed based on multimodal journey scenarios in Vienna. The results indicate that, within the available methodological testbed, the preference-discovery-based model achieved closer in-sample agreement with user-provided route evaluations than the model based on explicitly rated criteria. This was observed in the ranking-agreement analysis, where a more favourable penalty-point ratio was obtained in 19/21 cases (90.5%) and in the numerical error comparison, where lower in-sample reconstruction errors were obtained for 18/21 users (85.71%) across all scenarios. The paper further considers the tension between individual and system-level goals, as well as a conceptual extension toward system-aware re-ranking of alternatives. Within the broader framework of smart mobility, the importance of interoperability and open data is also recognized, with National Access Points (NAPs) for multimodal travel information potentially representing an important precondition for the development of advanced and transparent MJP solutions. Full article

Modern smart cities increasingly depend on resilient and intelligent energy infrastructures to maintain critical urban services during large-scale disturbances and multi-fault conditions. Conventional restoration approaches are often limited by centralized operation, delayed response, and inadequate coordination of distributed energy resources (DERs) under emergency conditions. To address these challenges, this paper proposes a Federated AI-Driven Urban Energy Resilience Framework for Smart City Critical Infrastructure Restoration using Virtual Power Plant (VPP) coordination, blockchain-enabled peer-to-peer (P2P) energy trading, and intelligent distributed energy management. The proposed framework is validated on the IEEE 118-bus radial distribution system under severe dual-fault outage conditions, representing urban disaster-induced infrastructure interruptions. Critical urban service zones, including healthcare support systems, emergency loads, smart residential sectors, and EV charging corridors, are considered during the restoration process. The Seagull Optimization Algorithm (SOA) is employed to optimize DER dispatch and improve restoration performance under operational constraints. A progressive restoration strategy comprising conventional outage conditions, VPP-assisted restoration, blockchain-enabled decentralized energy trading, and AI-driven coordinated restoration is analyzed. Simulation results demonstrate that the proposed framework significantly enhances urban energy resilience by increasing load restoration from 55.05% to 94.20%, reducing Energy Not Supplied (ENS), improving voltage stability, and lowering interruption-related economic losses. The minimum bus voltage improves to 0.965 p.u. under the proposed coordinated restoration strategy. The results show that coordinated VPP operation and blockchain-based energy sharing can support reliable restoration of critical urban infrastructure during major outage conditions. The results indicate that integrating AI-assisted VPP coordination with secure decentralized energy trading can effectively support smart city critical infrastructure continuity during extreme outage conditions. The proposed framework provides a scalable and resilient solution for future intelligent urban energy systems and disaster-resilient smart city applications. Full article

Urban mobility modeling plays a critical role in understanding transport infrastructure and improving its efficiency and sustainability. While existing tools are effective for modeling, they typically require extensive data acquisition, such as surveys, questionnaires, or tracking, as well as domain knowledge for calibration. We propose the Mobility Oracle, a framework that can algorithmically approximate urban mobility by incorporating human preferences in the routing process. The framework relies on open-source data and generates synthetic datasets for further analysis. It can be adapted to different contexts as it is reproducible, modular, and flexible. Both the theoretical components and the practical implementation are presented, along with a case study that illustrates the framework’s potential applications. Validation is carried out for Vienna (Austria) and Munich (Germany), comparing our approach against the official city-wide modal splits and a smaller tracked dataset within one of the cities. The resulting mode shares show an average difference of 4.7% at the city scale and a maximum of 1.9% for the tracked sample. These results demonstrate that the Mobility Oracle can be a useful tool to approximate human mobility. City planners and decision-makers can use it to systematically test and evaluate alternative planning scenarios across different urban contexts. Full article

Water-soil gushing caused by tunnel leakage can induce severe ground disturbance and threaten the safety of shield tunnels, yet rapid prediction remains difficult because high-fidelity numerical simulations are computationally expensive. This study develops an interpretable machine-learning framework for predicting gushing-induced ground disturbance around shield tunnels based on a validated two-phase Material Point Method database. Six governing variables are considered, including the tunnel depth ratio, gushing location, soil friction angle, Young’s modulus, intrinsic permeability, and soil gushing mass. Three representative response variables were selected, namely the maximum ground settlement, flow-zone width, and flow-zone centroid angle. Five algorithms, including MLP, RF, XGBoost, SVR, and Ridge, were established and compared, with hyperparameters optimised using Optuna. The results show that nonlinear models consistently outperform the linear baseline, among which MLP, RF, and XGBoost achieve the best overall accuracy and robustness. Error-distribution analysis further indicates that MLP and RF yield the highest proportion of low-error predictions. SHAP interpretation shows that SGM is the dominant factor governing maximum settlement and flow-zone width, whereas gushing location primarily controls the flow-zone centroid angle. The proposed framework provides an efficient and physically interpretable surrogate for rapid hazard assessment of gushing-induced ground disturbance in shield tunnelling. Full article

Public transport reliability largely determines the performance of smart urban mobility systems, as it directly affects passenger satisfaction and network efficiency. However, the strategic planning of public transport infrastructure is often carried out without dynamic, data-driven insights into operational performance, instead relying solely on static historical records of network operations. This study develops a data-driven framework based on the XGBoost machine learning algorithm to support the prioritization of infrastructure interventions by predicting delay severity and identifying reliability hotspots along an urban bus route. Delay severity is categorized into three classes (minor, moderate, and severe), using a model that incorporates spatial, temporal, operational, and meteorological variables. The XGBoost framework achieves a high predictive performance, with classification accuracies of 91.5% and 89.7% for the outbound and inbound bus route directions, respectively. Feature importance analysis indicates that seasonal and meteorological variables are critical factors influencing delay severity, highlighting the role of broader external environmental conditions on corridor performance. Furthermore, spatial analysis identifies specific bus stops with high delay probabilities, indicating hotspots where infrastructure upgrades should be prioritized at the stop and corridor levels. This study proposes a decision-support tool that enables targeted infrastructure investments at locations where they are most needed, contributing to more efficient and resilient public transport systems in smart cities. Full article

Smart city development depends on the readiness of Connected, Autonomous, Shared, and Electric (C.A.S.E.) mobility systems to deliver sustainable, data-driven urban transportation. This paper assesses C.A.S.E. mobility readiness in Thailand—Southeast Asia’s largest automotive manufacturing economy and an active smart city developer—situating each dimension within Thailand’s national seven-pillar smart city framework. A dual-axis supply–demand positioning framework synthesises peer-reviewed evidence, Thailand-specific infrastructure assessments, consumer surveys, and Monte Carlo simulation outputs across all four dimensions. Electric mobility is the most advanced dimension, with Thailand positioned as a regional production hub; Monte Carlo Total Cost of Ownership (TCO) analysis confirms 23–38% savings per route for electric bus adoption and fleet-wide net savings of approximately 236 million THB over ten years. Shared mobility is constrained by absent Mobility-as-a-Service (MaaS) governance, though mode choice evidence confirms a 24–36% car trip reduction potential through congestion pricing and shared taxi deployment. Connected mobility occupies a demand-led position; Autonomous mobility remains nascent on road, with trust identified as the dominant adoption barrier in a Technology Acceptance Model (TAM) survey of 797 Bangkok residents. Thailand’s seven-pillar smart city framework—particularly the Smart Mobility and Smart Governance pillars—provides the institutional architecture for an integrated C.A.S.E. National Mobility Strategy that could resolve governance fragmentation and accelerate sustainable urban mobility transition. Full article

Urban green spaces play a critical role in mitigating heat stress and enhancing urban livability, in line with the objectives and expectations of the United Nations Sustainable Development Goals 10 (Reduced Inequalities) and 11 (Sustainable Cities and Communities). This study employs Physarealm (Grasshopper), a lightweight agent-based simulation (ABS) model, to dynamically simulate pedestrian behaviors for different mobility groups. Together with Space Syntax, the results—time-extended movement and interaction patterns—are conceptualized as a relational configuration of green space provision (supply), pedestrian activity intensity (demand), and thermal exposure (environmental resistance). Three contrasting urban areas in northern Italy (Lambrate, Bolognina, and Ispra) are selected as case studies. The results demonstrate that urban inequality cannot be sufficiently explained by the inadequacy of single components, but emerges from imbalanced relational configurations of supply, demand, and environmental resistance. In May, 100% and 95% of traversed cells in Lambrate and Bolognina fall within the high-heat-stress range (>32 °C), compared with 59% in Ispra. Correspondingly, average green provision within the 5 min walking range is 5.4% in Lambrate, 7.2% in Bolognina, and 37% in Ispra. By uncovering relational mismatch patterns that are often overlooked in conventional urban analyses, this study enables a multi-dimensional diagnosis of imbalances. By positioning ABS as a front-end process generator and Space Syntax as a structural interpretation step, it demonstrates how dynamic behavioral processes can be reorganized into network-scale diagnostic representations. The study supports a climate-sensitive and human-centered diagnosis of walkability and green space accessibility, while contributing a transferable analytical approach for identifying relational inequality patterns within open urban data science contexts. Full article

Smart cities increasingly depend on electrical grid infrastructures capable of operating under high levels of digitalization, decentralization, and intelligence while maintaining reliability, security, and governance at the city scale. However, conventional power systems, historically designed for centralized generation and passive operation, are poorly aligned with the operational complexity, multi-actor coordination, and cross-sector integration characteristic of urban energy systems. This review develops an architecture-first perspective on smart-city electrical grids, tracing their evolution from digitalized power networks to decentralized and AI-enabled urban energy ecosystems. Rather than focusing on individual technologies, the study evaluates grid architectures using a multi-layer framework that integrates physical grid infrastructure, distributed energy resources and microgrids, communication and data platforms, intelligence placement, cybersecurity exposure, and governance accountability. Smart-city grid architectures are assessed using deployability beyond pilot projects, auditability, and regulatory alignment as primary evaluation criteria alongside conventional technical considerations. Through this perspective, the review explains a recurring pattern observed in the literature: many technically mature smart-grid solutions fail to scale in real urban deployments due to architectural fragmentation and governance constraints. By synthesizing insights from power systems engineering, information and communication technologies, and smart-city research, the paper highlights architectural trade-offs related to decentralization, interoperability, resilience under compound threats, and assisted autonomy. The resulting framework supports researchers, system designers, and policymakers in the coordinated development of resilient, secure, and governable electrical grids for future smart-city energy systems. Full article

While Low Emission Zone (LEZ) enforcement cameras provide a constant stream of traffic data, such resources remain significantly underexploited for urban mobility planning, as their current application is restricted to enforcing vehicle access regulations and issuing fines. This paper presents MobiCugat, a framework demonstrating that Automatic Number Plate Recognition (ANPR) camera data from a municipal LEZ network can serve as the calibration backbone for high-fidelity, city-scale traffic simulations for a policy-testing Digital Twin. The case study is Sant Cugat del Vallès (Barcelona), where the local council sought to evaluate new scenarios for the area using an evidence-based, data-driven approach. Vehicle detection records from 102 LEZ ANPR cameras were processed into 15-min traffic intensity time series through a General Data Protection Regulation (GDPR)-compliant pipeline. The Realistic Urban Traffic Generator (RUTGe), a Deep Reinforcement Learning-based tool, was used to generate SUMO-compatible traffic demand whose simulated detector counts reproduce the observed camera-based intensities. The resulting simulations reproduced the observed detector-level traffic intensities with ${\mathrm{MARE}}_{\%}$ values between $2.29\%$ and $2.90\%$ across representative morning peak, midday off-peak, and evening peak traffic conditions. Additionally, camera analysis of over $470,000$ vehicle records revealed that resident traffic ($37.4\%$) dominates over through-traffic ($3.8\%$), significantly refining prior survey-based estimates. Our high-fidelity simulation tool based on SUMO, features realistic traffic patterns calibrated through AI-driven techniques, enabling the evaluation of diverse ’what-if’ scenarios—such as road closures, pedestrianization, changes in traffic direction, or relocation of bus stops. By quantifying the impact of these interventions, our tool facilitates informed decision-making prior to physical implementation. The proposed pipeline is cost-effective, privacy-preserving, and directly replicable for any municipality operating an LEZ camera network, offering a scalable template for evidence-based urban mobility planning, aligned with the European Strategy for Data and the EU Green Deal goals for sustainable mobility. Full article

Much of the research and proposed industrial deployment of Remote Operations (ROs) in support of automated vehicles is founded on the optimistic premise that in-vehicle standby drivers and Safety Officers (SOs) can easily be replaced with ROs, with some commercial models proposing that a single RO supervise over 30 vehicles. However, emerging evidence suggests that the RO task is fundamentally different from the in-vehicle driving task. Furthermore, communications latency and reliability constraints, coupled with fragmented attention and altered task demands, introduce distinctive human factor challenges. These include degraded situational awareness, increased cognitive workload, and reduced capacity for timely intervention. The result is a widening gap between what is commercially desirable and what may be operationally appropriate. This paper argues that the central question for remote operation in support of automated vehicles is not one of technical feasibility but of human-centred appropriateness, and debates which RO roles should continue to be developed and which should be constrained or avoided. We present a synthesis of research on remote vehicle operations, identifying recurring human-factor limitations and mapping them to proposed remote tasks. The paper concludes with targeted recommendations for designers, operators, and regulators intended to question the scaling of teleoperation models and to reframe the debate from “Can we teleoperate?” to “Under what conditions should we?” Full article

Temperature has a significant impact on the operation and performance of electronic systems. Conventional approaches focus on stabilizing electronic systems to maintain functionality under unfavorable thermal conditions, typically at the expense of increased consumption. This paper adopts a multi-objective approach to identify the Pareto-optimal (PO) trade-off across varying temperatures between functionality and consumption of low-power radio transceivers used in the Internet of Things (IoT) and wireless sensor networks. Building upon the established two-segment PO trade-off controlled by supply voltage and output power settings, between engaged and achieved transmission power, parameters directly associated with energy consumption and transmission quality, we analyze the influence of temperature on the Pareto front. We find that decreasing the temperature improves both engaged power and achieved transmission power simultaneously. Therefore, we propose a novel Pareto-optimal temperature-opportunistic wireless communication approach that exploits temperature variability by selecting favorable temperature conditions for transmission. We also identify the spatio-temporal potential of temperature variations across a four-dimensional network deployment space, particularly in temperature-dynamic urban environments of smart city infrastructure supporting massive IoT. Experiments on a modern Texas Instruments CC1200 transceiver confirm that the power savings of approx 30% and nearly 450 times increase in achieved transmission power are attainable for a temperature difference of 60 °C, corresponding to realistic conditions between the ambient air and a black-painted surface. Full article

The growing environmental crisis and rapid urbanization have made the shift to renewable energy systems even more important for smart city development. In today’s cities, such renewable energy sources as solar photovoltaics, wind energy, hybrid systems, and battery energy storage are no longer just separate assets. They are now important parts of smart grids, intelligent buildings, and urban infrastructure that work together. However, putting these systems in cities on a large scale makes it harder to monitor, control, integrate, scale, and work with them in real time. In this setting, the Internet of Things (IoT) and edge computing are technologies that make it possible to turn traditional renewable energy systems into smart, responsive, and self-sufficient urban energy systems. IoT-based monitoring and control systems let city operators, utilities, and policymakers gather real-time data, improve grid stability, optimize energy flows, and better integrate distributed renewable energy sources into smart city ecosystems. Edge computing makes these features even better by allowing for low-latency processing, more localized decision-making, and less reliance on centralized cloud infrastructures. This paper offers a thorough and methodical examination of contemporary IoT- and edge-enabled technologies used to monitor, control, and integrate renewable energy systems; specifically highlighting their significance in smart city and smart grid applications. The review combines the most recent research on hardware platforms, communication protocols, data processing architectures, and edge–cloud coordination mechanisms used in solar, wind, and hybrid energy systems. Additionally, this review synthesizes architectural design principles extracted from analyzed studies to guide the development of scalable, resilient, and cost-efficient renewable energy monitoring systems. This study offers a structured foundation for the design of scalable, resilient, and cost-effective renewable energy management systems that align with the sustainability, efficiency, and intelligence goals of future smart cities by analyzing cutting-edge solutions and pinpointing significant technological trends, challenges, and research deficiencies. This review also highlights its contribution vis-à-vis previous surveys by stressing the inter-domain comparison across solar, wind, and hybrid systems. It focuses, in particular, on edge–cloud coordination and architecture-level trade-offs pertinent to smart grid and smart city deployments. Full article

This paper presents a closed-loop, CPU-aware traffic-control framework for SDN in 5G/6G multi-tenant edge environments based on commodity KVM/OVS infrastructures. It couples fine-grained data-plane telemetry via eBPF with adaptive XDP rate limiting, coordinated by a PID controller in the OVS datapath. Unlike control-plane polling, it provides real-time feedback between CPU utilization and traffic regulation. Experiments in a virtualized multi-tenant OVS testbed (KVM/virtio-net) keep CPU below per-slice CPU targets (e.g., 1.02% for a 3% setpoint), with an under-target bias that avoids overshoot while preserving stable forwarding. We attribute this bias, at light load, to a supply-limited regime, conservative per-slice CPU accounting, and stability-oriented PID tuning, and introduce a low-latency profile that mitigates this bias for latency-sensitive slices. The XDP datapath achieves 1–3 $\mathsf{\mu }$s per-packet processing with 5–10% additional CPU overhead relative to an uninstrumented baseline, while using less CPU than OVS policing at comparable throughput. A 3% per-slice CPU target balances isolation and throughput, while 2% yields stricter isolation at the cost of higher packet loss. Software-based rate limiting can induce cross-slice interference; effective isolation holds below 1 Gbps aggregate load. Above this, shared Linux kernel overhead degrades isolation, causing significant loss; thus, XDP alone cannot ensure line-rate isolation, motivating SmartNICs. The design improves efficiency, predictability, and isolation, laying a foundation for intelligent traffic management in future resource-intensive applications. Full article

Medical resources are primary public goods, but the nature of their distribution across different-sized cities is unclear. Here, we examined the nonlinear scaling relationship between urban populations and medical resources in China, moving beyond the limitations of traditional linear evaluation metrics. Taking 296 Chinese cities as samples, we constructed scaling law models between population size and three medical resource indicators: the numbers of hospital beds, doctors, and hospitals. The results show that the number of doctors maintained a linear scaling relationship on the whole (scaling exponent $\beta$: 0.98–1.06), while the numbers of hospitals ($\beta$: 0.79–0.91) and hospital beds ($\beta$: 0.91–0.99) both exhibited sublinear scaling (2000–2022), confirming the existence of economies of scale in basic medical facilities. The Scale-Adjusted Metropolitan Indicator (SAMI) further reveals spatial agglomeration characteristics: the northern and southwestern regions of China perform notably better than expected in hospital availability, while provincial cites show advantages in terms of the numbers of beds and doctors. This study quantifies the nonlinear allocation of medical resources across Chinese cities and advocates for a reasonable allocation mechanism to promote medical equity. Full article

Background: Smart-city road and intersection management increasingly aims to smooth bus operations and reduce stop-and-go driving, but cities often lack auditable indicators linking routine fleet data with comparable energy and environmental KPIs. Methods: This study develops a Monitoring–Reporting–Verification (MRV) workflow for daily bus records from a 2024 Polish metropolitan fleet (diesel, compressed natural gas (CNG), hybrid, and battery-electric buses). Records were quality checked, harmonized to MJ/km, aggregated to bus-month observations, and analyzed using a linear mixed-effects model with propulsion technology, season, and activity level as fixed effects and vehicle-level random intercepts. Environmental impacts were then calculated under well-to-wheel (WTW) boundaries using Environmental Footprint 3.1 (EF 3.1) impact categories, Poland’s 2024 electricity mix, and illustrative electricity-mix scenarios through 2050. Results: Relative to diesel, BEV and HEV were associated with lower adjusted energy intensity (ratios 0.272 and 0.681, respectively), whereas the CNG–diesel contrast was directionally higher but statistically inconclusive under the available CNG sample. BEV energy intensity more than doubled in winter in descriptive terms, and vehicle-specific heterogeneity remained high (ICC ≈ 0.61). The BEV climate profile improved under electricity decarbonization, while some EF categories showed mix-dependent trade-offs. The 3–10% traffic-management variants are interpreted as screening assumptions rather than measured ITS effects. Conclusions: Routine bus records can support auditable MRV and preliminary screening of fleet and corridor interventions, but causal traffic-management evaluation requires route-level trajectory, congestion, and before–after data. Full article