Smart Cities, Volume 8, Issue 3 (June 2025) – 30 articles

The development of electric mobility offers new perspectives in the energy sector and improves resource efficiency and sustainability. This paper proposes a new strategy for synchronizing the energy requirements of home, commercial, and vehicle mobility, with a focus on the batteries of electric cars. In particular, this paper describes the coordination between a battery management algorithm that optimally assigns its capacity so that at least a part is reserved for mobility and a vehicle-to-building (V2B) service algorithm that uses a share of EV battery energy to improve user participation in renewable energy exploitation at home and at work. The system offers the user the choice of always maintaining a minimum charge for mobility or providing more flexible use of energy for business needs while maintaining established vehicle autonomy. Suitable management at home and at work allows always charging the vehicle to the required level of charge with renewable power excess, highlighting how the cooperation of home and work charging may provide novel frameworks for a smarter and more sustainable integration of electric mobility, reducing energy consumption and providing more effective energy management. The effectiveness of the proposed solution is demonstrated in a realistic configuration with real data and an experimental setup. Full article

Urban Air Mobility (UAM) requires reliable communication and surveillance infrastructures to ensure safe Unmanned Aerial Vehicle (UAV) operations in dense metropolitan environments. However, urban infrastructure is inherently heterogeneous, leading to significant spatial variations in monitoring performance. This study proposes a unified framework that integrates infrastructure readiness assessment with Deep Reinforcement Learning (DRL)-based UAV path planning. Using Singapore as a representative case, we employ a data-driven methodology combining clustering analysis and in situ measurements to estimate the citywide distribution of surveillance quality. We then introduce an infrastructure-aware path planning algorithm based on a Double Deep Q-Network (DQN) with a convolutional architecture, which enables UAVs to learn efficient trajectories while avoiding surveillance blind zones. Extensive simulations demonstrate that the proposed approach significantly improves path success rates, reduces traversal through poorly monitored regions, and maintains high navigation efficiency. These results highlight the potential of combining infrastructure modeling with DRL to support performance-aware airspace operations and inform future UAM governance systems. Full article

While digital transformation in e-commerce receives the most publicity, applications in energy and water utilities have been ongoing for decades. Using a methodology based on a systematic review, the paper offers a model of how it occurs in water utilities, reviews experiences from the field, and derives lessons learned to create a road map for future research and implementation. Innovation in water utilities occurs more in the field than through organized research, and utilities share their experiences globally through networks such as water associations, focus groups, and media outlets. Their digital transformation journeys are evident in business practices, operations, and asset management, including methods like decision support systems, SCADA systems, digital twins, and process optimization. Meanwhile, they operate traditional regulated services while being challenged by issues like aging infrastructure and workforce capacity. They operate complex and expensive distribution systems that require grafting of new controls onto older systems with vulnerable components. Digital transformation in utilities is driven by return on investment and regulatory and workforce constraints and leads to cautious adoption of innovative methods unless required by external pressures. Utility adoption occurs gradually as digital tools help utilities to leverage system data for maintenance management, system renewal, and water loss control. Digital twins offer the advantages of enterprise data, decision support, and simulation models and can support distribution system optimization by integrating advanced metering infrastructure devices and water loss control through more granular pressure control. Models to anticipate water main breaks can also be included. With such advances, concerns about cyber security will grow. The lessons learned from the review indicate that research and development for new digital tools will continue, but utility adoption will continue to evolve slowly, even as many utilities globally are too stressed with difficult issues to adopt them. Rather than rely on government and academics for research support, utilities will need help from their support community of regulators, consultants, vendors, and all researchers to navigate the pathways that lie ahead. Full article

Mediterranean island cities face unique challenges in implementing smart city initiatives due to fragmented governance structures, seasonal economic pressures, and evolving societal expectations. This study investigates how strategic aspirations and public discourse shape the feasibility of smart city policies in insular contexts. Specifically, it combines SOAR (Strengths, Opportunities, Aspirations, Results) analysis with the Overton Window framework to examine both the strategic capacities and normative acceptability of technological interventions. The Overton Window, a model originally developed in political theory, is applied here to evaluate how public and policy acceptance of smart technologies, ranging from digital governance systems to AI-based mobility, varies across different islands. While this study draws on cross-case comparisons of multiple Mediterranean island contexts, the primary data were collected in Athens, Greece, through surveys and focus groups with citizens and stakeholders. The findings reveal disparities in institutional maturity, stakeholder coordination, and levels of citizen support. This study concludes that successful smart city transformation requires both strategic coherence and alignment with evolving public values. It proposes the ‘Ecopolis’ model as a conceptual planning framework that integrates sustainability, citizen participation, and data-driven governance in tourism-dependent island settings. Full article

Healthcare expenditures in Australia have grown steadily in recent years, intensifying fiscal pressures and exposing challenges related to unequal resource distribution. While traditional statistical methods struggle to capture complex data relationships, machine learning offers a more robust approach to handling intricate and non-linear data. This study employs machine learning techniques to investigate the key determinants of healthcare expenditures in Australia from 2011 to 2021. Using advanced models, including Random Forest, XGBoost, and Multi-Layer Perceptron (MLP), along with SHAP (SHapley Additive exPlanations) analysis, we identify the most influential factors driving healthcare spending. The results reveal that funding sources, public hospital services, and geographic disparities are the primary predictors of expenditure trends. Notably, funding allocation mechanisms and regional inequities emerge as critical influences on spending patterns. By integrating feature importance metrics with SHAP analysis, this study enhances model interpretability and offers actionable insights for policymakers. The findings underscore the urgent need to optimize resource allocation and address regional disparities to promote the sustainability and equity of Australia’s healthcare system. Full article

Urban documents like city planning reports and environmental data often feature complex charts and texts that require effective summarization tools, particularly in smart city management systems. These documents increasingly use graphical abstracts alongside textual summaries to enhance readability, making automated abstract generation crucial. This study explores the application of summarization technology using scientific paper abstract generation as a case. The challenge lies in processing the longer multimodal content typical in research papers. To address this, a deep multimodal-interactive network is proposed for accurate document summarization. This model enhances structural information from both images and text, using a combination module to learn the correlation between them. The integrated model aids both summary generation and significant image selection. For the evaluation, a dataset is created that encompasses both textual and visual components along with structural information, such as the coordinates of the text and the layout of the images. While primarily focused on abstract generation and image selection, the model also supports text–image cross-modal retrieval. Experimental results on the proprietary dataset demonstrate that the proposed method substantially outperforms both extractive and abstractive baselines. In particular, it achieves a Rouge-1 score of 46.55, a Rouge-2 score of 16.13, and a Rouge-L score of 24.95, improving over the best comparison abstractive model (Pegasus: Rouge-1 43.63, Rouge-2 14.62, Rouge-L 24.46) by approximately 2.9, 1.5, and 0.5 points, respectively. Even against strong extractive methods like TextRank (Rouge-1 30.93) and LexRank (Rouge-1 29.63), our approach shows gains of over 15 points in Rouge-1, underlining its effectiveness in capturing both textual and visual semantics. These results suggest significant potential for smart city applications—such as accident scene documentation and automated environmental monitoring summaries—where rapid, accurate processing of urban multimodal data is essential. Full article

Autonomous vehicles are one of the key components of smart mobility that leverage innovative technology to navigate and operate safely in urban environments. Traffic light detection systems, as a key part of autonomous vehicles, play a key role in navigation during challenging traffic scenarios. Nighttime driving poses significant challenges for autonomous vehicle navigation, particularly in regard to the accuracy of traffic lights detection (TLD) systems. Existing TLD methodologies frequently encounter difficulties under low-light conditions due to factors such as variable illumination, occlusion, and the presence of distracting light sources. Moreover, most of the recent works only focused on daytime scenarios, often overlooking the significantly increased risk and complexity associated with nighttime driving. To address these critical issues, this paper introduces a novel approach for nighttime traffic light detection using the LNT-YOLO model, which is based on the YOLOv7-tiny framework. LNT-YOLO incorporates enhancements specifically designed to improve the detection of small and poorly illuminated traffic signals. Low-level feature information is utilized to extract the small-object features that have been missing because of the structure of the pyramid structure in the YOLOv7-tiny neck component. A novel SEAM attention module is proposed to refine the features that represent both the spatial and channel information by leveraging the features from the Simple Attention Module (SimAM) and Efficient Channel Attention (ECA) mechanism. The HSM-EIoU loss function is also proposed to accurately detect a small traffic light by amplifying the loss for hard-sample objects. In response to the limited availability of datasets for nighttime traffic light detection, this paper also presents the TN-TLD dataset. This newly curated dataset comprises carefully annotated images from real-world nighttime driving scenarios, featuring both circular and arrow traffic signals. Experimental results demonstrate that the proposed model achieves high accuracy in recognizing traffic lights in the TN-TLD dataset and in the publicly available LISA dataset. The LNT-YOLO model outperforms the original YOLOv7-tiny model and other state-of-the-art object detection models in mAP performance by 13.7% to 26.2% on the TN-TLD dataset and by 9.5% to 24.5% on the LISA dataset. These results underscore the model’s feasibility and robustness compared to other state-of-the-art object detection models. The source code and dataset will be available through the GitHub repository. Full article

The adoption of wind energy is pivotal for advancing sustainable power systems, particularly in off-grid microgrids where infrastructure limitations hinder conventional energy solutions. The inherent variability of wind generation, however, challenges grid reliability and demand–supply balance, necessitating accurate forecasting models. This study proposes a hybrid framework for short-term wind speed prediction, integrating deep learning (Long Short-Term Memory, LSTM) and ensemble methods (random forest, Extra Trees) to exploit their complementary strengths in modeling temporal dependencies. A multivariate approach is adopted using meteorological data (including wind speed, temperature, humidity, and pressure) to capture complex weather interactions through a structured time-series design. The framework also includes a feature selection stage to identify the most relevant predictors and a hyperparameter optimization process to improve model generalization. Three wind speed variables, maximum, average, and minimum, are forecasted independently to reflect intra-day variability and enhance practical usability. Validated with real-world data from Cuenca, Ecuador, the LSTM model achieves superior accuracy across all targets, demonstrating robust performance for real-world deployment. Comparative results highlight its advantage over tree-based ensemble techniques, offering actionable strategies to optimize wind energy integration, enhance grid stability, and streamline renewable resource management. These insights support the development of resilient energy systems in regions reliant on sustainable microgrid solutions. Full article

Intelligent transport systems are essential for urban residents in large cities, facilitating not only vehicular mobility but also the movement of residents. Urban mobility is a significant concern, particularly in the context of the Internet of Things, where vehicles evolve into intelligent nodes within sensor networks. This convergence of the mobile Internet and the Internet of Vehicles (IoV) redefines urban mobility. In the context of smart cities, it examines the evolving IoV and communication models, unveiling both current and emerging trends. This research paper offers insights into global market trends and conducts bibliographic data analysis to illuminate the present and future potential of the IoV. It highlights IoV applications, the layered architecture, and connected and autonomous vehicle levels (Level 0 to Level 5). The communication model is explained, along with addressing research challenges and future directions. The conclusion summarizes the key findings and emphasizes the main points addressed in the study. Full article

Rapid urban growth and the spread of the concept of smart cities force an increasing need to understand how cities become “smart” and apply their experience where it will best take root. Understanding which experience will be most suitable is not a trivial task and requires labor-intensive analysis. This study aims to identify smart cities that are most similar to Almaty and Astana in terms of key indicators by applying quantitative methods. Using a sample of smart cities, this paper successively employs three methods—principal component analysis, hierarchical cluster analysis, and t-distributed stochastic neighbor embedding. The results showed that Denver and Ottawa are the closest to Almaty and Astana, followed by Ankara and Phoenix. The proposed methodology allowed us to assess the similarity of urban development conditions, with an assumption that similar development conditions determine approaches to the development of smart cities, and thus the relevance of experiences from other smart cities worldwide could be applied to Almaty and Astana. This approach is intended to contribute to the effectiveness of transferring advanced solutions of smart city development to the context of Kazakhstan. The obtained conclusions can be used to form recommendations for the development strategy of Almaty and Astana, as well as other cities facing similar challenges. Full article

Climate-induced extreme rainfall events are increasing the intensity and frequency of flash floods, highlighting the urgent need for advanced flood management systems in climate-resilient cities. This study introduces an Intelligent Flood Control Decision Support System (IFCDSS), a novel AI-driven solution for real-time flood forecasting and automated pump operations. The IFCDSS integrates multiple advanced tools: machine learning for rapid short-term water level forecasting, NSGA-III for multi-objective optimization, the TOPSIS for robust multi-criteria decision-making, and the ANFIS for real-time pump control. Implemented in the flood-prone Zhongshan Pumping Station catchment in Taipei, the IFCDSS leveraged real-time sensor data to deliver accurate water level forecasts within five seconds for the next 10–30 min, enabling proactive and informed operational responses. Performance evaluations confirm the system’s scientific soundness and practical utility. Specifically, the ANFIS achieved strong accuracy (R² = 0.81), with most of the prediction errors being limited to a single pump unit. While the conventional manual operations slightly outperformed the IFCDSS in minimizing flood peaks—due to their singular focus—the IFCDSS excelled in balancing multiple objectives: flood mitigation, energy efficiency, and operational reliability. By simultaneously addressing these dimensions, the IFCDSS provides a robust and adaptable framework for urban environments. This study highlights the transformative potential of intelligent flood control to enhance urban resilience and promote sustainable, climate-adaptive development. Full article

Effectively managing carbon monoxide (CO) pollution in complex industrial cities like Jubail remains challenging due to the diversity of emission sources and local environmental dynamics. This study analyzes spatiotemporal CO patterns and builds accurate predictive models using five years (2018–2022) of data from ten monitoring stations, combined with meteorological variables. Exploratory analysis revealed distinct diurnal and moderate weekly CO cycles, with prevailing northwesterly winds shaping dispersion. Spatial correlation of CO was low (average 0.14), suggesting strong local sources, unlike temperature (0.92) and wind (0.5–0.6), which showed higher spatial coherence. Seasonal Trend decomposition (STL) confirmed stronger seasonality in meteorological factors than in CO levels. Low wind speeds were associated with elevated CO concentrations. Key predictive features, such as 3-h rolling mean and median values of CO, dominated feature importance. Spatiotemporal analysis highlighted persistent hotspots in industrial areas and unexpectedly high levels in some residential zones. A range of models was tested, with ensemble methods (Extreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost)) achieving the best performance ($R^2>0.95$) and XGBoost producing the lowest Root Mean Squared Error (RMSE) of 0.0371 ppm. This work enhances understanding of CO dynamics in complex urban–industrial areas, providing accurate predictive models ($R^2>0.95$) and highlighting the importance of local sources and temporal patterns for improving air quality forecasts. Full article

Wireless sensor networks (WSNs) have emerged to address unique challenges in urban environments. This survey dives into the challenges faced in urban areas and explores how WSN applications can help overcome these obstacles. The diverse applications of WSNs in urban settings discussed in this paper include gas monitoring, traffic optimization, healthcare, disaster response, and security surveillance. The innovative research is considered in an urban environment, where WSNs such as energy efficiency, throughput, and scalability are deployed. Every application scenario is distinct and examined in details within this paper. In particular, smart cities represent a major domain where WSNs are increasingly integrated to enhance urban living through intelligent infrastructure. This paper emphasizes how WSNs are pivotal in realizing smart cities by enabling real-time data collection, analysis, and communication among interconnected systems. Applications such as smart transportation systems, automated waste management, smart grids, and environmental monitoring are discussed as key components of smart city ecosystems. The synergy between WSNs and smart city technologies highlights the potential to significantly improve the quality of life, resource management, and operational efficiency in modern cities. This survey specifies existing work objectives with results and limitations. The aim is to develop a methodology for evaluating the quality of performance analysis. Various performance metrics are discussed in existing research to determine the influence of real-time applications on energy consumption, network lifetime, end-to-end delay, efficiency, routing overhead, throughput, computation cost, computational overhead, reliability, loss rate, and execution time. The observed outcomes are that the proposed method achieves a higher 16% accuracy, 36% network lifetime, 20% efficiency, and 42% throughput. Additionally, the proposed method obtains 36%, 30%, 46%, 35%, and 32% reduction in energy consumption, computation cost, execution time, error rate, and computational overhead, respectively, compared to conventional methods. Full article

The higher integration of photovoltaic (PV) units is an inevitable component of smart city development. Thanks to smart meter devices that can record the exchange of power between the grid and customers, it is expected that homeowners and businesses will tend to install PV arrays on their rooftops and parking lots to benefit from selling power back to the grid. However, the overvoltage issue resulting from high PV penetration is a major challenge that necessitates the active power curtailment of PV units to ensure power grid stability. Fairness-oriented methods aim to minimize the active power of PV units as much as possible, adopting a fairer approach, and then address the PV owner’s satisfaction with fair profit and loss. Maintaining voltage within a limited standard range under very low load conditions while prioritizing PV inverters’ participation in reactive power contribution and attempting to ensure fairer curtailment of active power presents challenges to existing control design approaches. This paper presents twelve new volt–watt curve design methods to achieve these goals and address the challenges. The methods yield polynomial curves, piecewise linear curves, and single linear curves. A unique voltage sensitivity value for each PV inverter is used to determine the control region area and the slope of the curve at the starting point in certain instances. The effectiveness of the proposed methods is discussed by evaluating their capabilities on the 37-bus IEEE system. Full article

Accurate and adaptive bus dispatching is vital for medium-sized urban centers, where static schedules often fail to accommodate fluctuating passenger demand. In this work, we propose a dynamic heuristic that integrates machine learning-based demand forecasts into a discrete-time planning horizon, thereby enabling real-time adjustments to dispatch decisions. Additionally, we introduce a tailored mathematical model—grounded in mixed-integer linear programming and space-time flows—that serves as a benchmark to evaluate our heuristic’s performance under the operational constraints typical of traditional public transportation systems in Colombian mid-sized cities. A key contribution of this research lies in combining predictive modeling (using Prophet for passenger demand) with operational optimization, ensuring that dispatch frequencies adapt promptly to varying ridership levels. We validated our approach using a real-world case study in Montería (Colombia), covering eight representative routes over a full day (5:00–21:00). Numerical experiments show that: 1. Our heuristic matches or surpasses 95% of the optimal solution’s operational utility on most routes, with an average gap of 4.7%, relative to the benchmark mathematical model. 2. It maintains high service levels—above 90% demand coverage on demanding corridors—and robust bus utilization, without incurring excessive operating costs. 3. It reduces computation times by up to 98% compared to the optimization model, making it practically viable for daily scheduling where solving large-scale models exactly can be prohibitively time-consuming. Overall, these results underscore the heuristic’s practical effectiveness in boosting profitability, optimizing resource use, and rapidly adapting to demand fluctuations. The proposed framework thus serves as a scalable and implementable tool for transportation operators seeking data-driven dispatch solutions that balance operational efficiency and service quality. Full article

This paper presents a microsimulation-driven framework to analyze the performance of connected and automated vehicles (CAVs) alongside vehicles with human drivers (VHDs), channeled towards assessing project alternatives in road intersection design. The transition to fully automated mobility is driving the development of new intersection geometries and traffic configurations, influenced by increasing market entry rates (MERs) for CAVs (CAV-MERs), which were analyzed in a microsimulation environment. A suburban signalized intersection from the Polish road network was selected as a representative case study. Two alternative design hypotheses regarding the intersection’s geometric configurations were proposed. The Aimsun micro-simulator was used to hone the driving model parameters by calibrating the simulated data with reference capacity functions (RCFs) based on CAV factors derived from the Highway Capacity Manual 2022. Cross-referencing the conceptualized geometric design solutions, including a two-lane roundabout and an innovative knee-turbo roundabout, allowed the experimental results to demonstrate that CAV operation is influenced by the intersection layout and CAV-MERs. The research provides an overview of potential future traffic settings featuring CAVs and VHDs operating within various intersection designs. Additionally, the findings can support project proposals for the geometric and functional design of intersections by highlighting the potential benefits expected from smart driving. Full article

The Electric Vehicle Scheduling Problem (E-VSP) addresses the challenge of efficiently assigning predetermined trips to an electric vehicle fleet while accounting for charging infrastructure and battery range constraints. Despite numerous optimisation approaches proposed in the literature, comparative analyses of these methods remain scarce, with researchers typically focusing on developing novel algorithms rather than evaluating existing algorithms. Moreover, studies often employ convenient assumptions tailored to improve the performance of their optimisation technique. This study presents a comprehensive comparison of several optimisation techniques (mixed integer linear programming (MILP) using the branch-and-cut algorithm, metaheuristics, and heuristics) applied to the E-VSP under identical assumptions and constraints. The techniques are evaluated across multiple metrics, including solution quality, computational efficiency, and implementation complexity. Findings reveal that the branch-and-cut algorithm cannot solve instances with more than 10 trips in a reasonable time. Among metaheuristics, only genetic algorithms and simulated annealing demonstrate competitive performance, but both struggle with instances exceeding 100 trips. Our recently developed heuristic algorithm consistently found better solutions in significantly shorter computation times than the metaheuristics due to its ability to efficiently navigate the solution space while respecting the unique constraints of the E-VSP. Full article

The rapid adoption of smart technologies is increasingly evident in both personal and business contexts. The ‘post-pandemic’ economic recovery of 2022 and 2023 coincided with a global energy supply shortage driven by heightened energy demand and supply chain disruptions stemming from the ongoing Russian-Ukrainian conflict. The implementation of smart metering systems is a central component of European policies aimed at enhancing the competitiveness and environmental sustainability of energy markets. However, limited research exists on the acceptance of Smart Meter Technology (SMT) in general, specifically in Norway, as compared to other nations. SMT devices offer the potential for real-time energy consumption monitoring, enabling users to track and modify their usage patterns for optimized consumption. This study employs a mixed-methods research design to gather insights from both SMT consumers and vendors. Findings underscore the pivotal roles of familiarity, cost, social influence, and perceived usefulness in shaping consumer adoption of SMT. This article provides critical insights and implications for researchers, network operators, electricity companies, and government agencies. Full article

Streets, as critical public space nexuses, require synergistic quality–utilization alignment—where quality without use signifies institutional inefficiency, and use without quality denotes operational ineffectiveness. Focusing on high-frequency pedestrian commuting streets (HFPCSs) that not only crucially mediate metropolitan mobility patterns but also shape citizens’ daily urban experiences and satisfaction, this study proposes a data-driven diagnostic framework for street quality–utilization assessment, integrating multi-source urban big data through a case study of Shenzhen. By integrating multi-source urban big data, we identify HFPCSs using LBS data and develop a multi-dimensional evaluation system that incorporates 1.07 million Points of Interest (POIs) for assessing convenience, utilizes DeepLabv3+ for the semantic segmentation of street view imagery to evaluate comfort, and leverages 15,374 km of road network data for accessibility analysis. The results expose dual mismatches: merely 2.15% of HFPCSs achieve balanced comfort–convenience–accessibility benchmarks, while over 70% of these are clustered in northern districts, exhibiting systematically inferior quality metrics across dimensions. Diagnostic analysis reveals specific planning and spatial configurations contributing to these disparities, informing targeted retrofitting strategies for priority street typologies. This approach establishes a replicable model for megacity street renewal, deploying supply–demand diagnostics to synchronize infrastructure upgrades with pedestrian flow realities. By bridging data insights with human-centric urban improvements, this framework demonstrates how smart city technologies can concretely address the quality–utilization paradox—advancing sustainable urbanism through evidence-based street transformations. Full article

High-quality data are foundational to reliable environmental monitoring and urban planning in smart cities, yet challenges like missing values and outliers in air pollution and meteorological time series data are critical barriers. This study developed and validated a dual-phase framework to improve data quality using a 60-month gas and weather dataset from Jubail Industrial City, Saudi Arabia, an industrial region. First, outliers were identified via statistical methods like Interquartile Range and Z-Score. Machine learning algorithms like Isolation Forest and Local Outlier Factor were also used, chosen for their robustness to non-normal data distributions, significantly improving subsequent imputation accuracy. Second, missing values in both single and sequential gaps were imputed using linear interpolation, Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), and Akima interpolation. Linear interpolation excelled for short gaps (R² up to 0.97), and PCHIP and Akima minimized errors in sequential gaps (R² up to 0.95, lowest MSE). By aligning methods with gap characteristics, the framework handles real-world data complexities, significantly improving time series consistency and reliability. This work demonstrates a significant improvement in data reliability, offering a replicable model for smart cities worldwide. Full article

The rapid growth in natural gas consumption by gas-fired generators and the emergence of power-to-hydrogen (P2H) technology have increased the interdependency of natural gas and power systems, presenting new challenges to energy system operators due to the heterogeneous uncertainties associated with power loads, renewable energy sources (RESs), and gas loads. These uncertainties can easily spread from one infrastructure to another, increasing the risk of cascading outages. Given the erratic nature of RESs, P2H technology provides a valuable solution for large-scale energy storage systems, crucial for the transition to economic, clean, and secure energy systems. This paper proposes a new approach for the co-optimized operation of gas and electric power systems, aiming to reduce combined operating costs by 10–15% without jeopardizing gas and energy supplies to customers. A mixed integer non-linear programming (MINLP) model is developed for the optimal day-ahead operation of these integrated systems, with a case study involving the IEEE 24-bus power system and a 20-node natural gas system. Simulation results demonstrate the model’s effectiveness in minimizing total costs by up to 20% and significantly reducing renewable energy curtailment by over 50%. The proposed approach supports UN Sustainable Development Goals by ensuring sustainable energy (SDG 7), fostering innovation and resilient infrastructure (SDG 9), enhancing energy efficiency for resilient cities (SDG 11), promoting responsible consumption (SDG 12), contributing to climate action (SDG 13), and strengthening partnerships (SDG 17). It promotes clean energy, technological innovation, resilient infrastructure, efficient resource use, and climate action, supporting the transition to sustainable energy systems. Full article

This study develops a comprehensive roadmap for Ecuador’s energy transition using a hybrid governance model that balances top–down and bottom–up approaches. By integrating national directives with local participation, this framework aims to enhance energy consumption and drive sustainable transitions. This research employs a mixed methodology, combining bibliometric analysis and governance structure assessment to evaluate Ecuador’s centralized energy system and its challenges. A three-phase strategy is proposed: Phase 1 introduces short-term interventions such as efficiency improvements and public awareness campaigns. Phase 2 focuses on decentralization, fostering local renewable energy production and community involvement. Phase 3 envisions a fully decentralized system where local entities operate autonomously within a supportive regulatory framework. The central research question is, how can a balanced governance framework foster sustainable ECB in Ecuador? By aligning national policies with local needs, this approach enhances policy adaptability, inclusivity, and long-term sustainability. Anticipated outcomes include improved energy efficiency, reduced reliance on fossil fuels, and increased community engagement in decision making. The findings contribute to global discussions on energy governance, demonstrating how hybrid models can facilitate sustainable energy transitions, particularly in developing countries with historically centralized systems. Full article

Autonomous driving technology is advancing rapidly, driven by integrating advanced intelligent systems. Autonomous vehicles typically follow a modular structure, organized into perception, planning, and control components. Unlike previous surveys, which often focus on specific modular system components or single driving environments, our review uniquely compares both settings, highlighting how deep learning and reinforcement learning methods address the challenges specific to each. We present an in-depth analysis of local and global planning methods, including the integration of benchmarks, simulations, and real-time platforms. Additionally, we compare various evaluation metrics and performance outcomes for current methodologies. Finally, we offer insights into emerging research directions based on the latest advancements, providing a roadmap for future innovation in autonomous driving. Full article

Smart cities integrate advanced technologies, data-driven decision-making, and interconnected infrastructure to enhance urban living and resource efficiency. Among these, Smart Water Management (SWM) is crucial for optimizing water distribution and reducing Non-Revenue Water (NRW) losses, a persistent challenge for utilities worldwide. Water leaks contribute significantly to NRW, necessitating real-time leak detection and management systems to minimize detection time and human effort. Achieving this requires seamless integration of SWM technologies, including advanced metering infrastructure, the Internet of Things (IoT), and Artificial Intelligence (AI). While previous studies have explored various leak detection techniques, many lack a focused analysis of real-time data integration and automated alerts in SWM systems. This Systematic Literature Review (SLR) addresses this gap by examining advancements in automatic data collection, leak detection models, and real-time alert mechanisms. The findings highlight the growing potential of data-driven approaches to enhance leak detection accuracy and efficiency, particularly those leveraging flow and pressure data. Despite advancements, model accuracy, scalability, and real-world applicability remain. This review provides critical insights for future research, guiding the development of automated, AI-driven leak management systems to improve water distribution, minimize losses, and enhance sustainability in smart cities. Full article

The smart-city revolution has been promoted as the next step in urban development, leveraging technology to achieve enhanced development standards amid the increasingly complex challenges of urbanization. However, despite the implementation of more efficient urban services, issues regarding their tangible effects and impact on people’s lives remain unresolved. In this context, the concept of intelligent cities is seen as a necessary evolution of the smart-city paradigm, positioning human factors as the driving forces behind urban technological evolution. This integrative concept embodies advanced technology to enhance essential urban functions, with sustainability, equity, and resilience as macro-development goals. This study reviews the multifaceted dimensions of intelligent cities, from designing and deploying smart infrastructure to implementing citizen-centric decision-making processes. Additionally, it critically examines the digital divide and highlights the importance of equitable development policies as essential for enabling transformative urban change. By linking technological advancement to social issues, this article provides practical insights and case studies from the cities of Helsinki, Barcelona, and Buenos Aires, demonstrating that smart-city initiatives are still failing to bridge the equity service distribution gap. This comprehensive assessment approach ultimately serves as a reference for future evaluations of intelligent urban transformations. Full article

Climate change has emerged as a significant driver of the increasing frequency and severity of power outages. Rising global temperatures place additional stress on electrical grids that must meet substantial electricity demands, while extreme weather events such as hurricanes, floods, heatwaves, and wildfires frequently damage vulnerable electrical infrastructure. Ensuring the resilient operation of distribution systems under these conditions poses a major challenge. This paper presents a comprehensive two-stage techno-economic strategy to enhance the resilience of modern distribution systems. The approach optimizes the scheduling of distributed energy resources—including distributed generation (DG), wind turbines (WTs), battery energy storage systems (BESSs), and electric vehicle (EV) charging stations—along with the strategic placement of remotely controlled switches. Key objectives include preventing damage propagation through the isolation of affected areas, maintaining power supply via islanding, and implementing prioritized load shedding during emergencies. Since improving resilience incurs additional costs, it is essential to strike a balance between resilience and economic factors. The performance of our two-stage multi-objective mixed-integer linear programming approach, which accounts for uncertainties in vulnerability modeling based on thresholds for line damage, market prices, and renewable energy sources, was evaluated using the IEEE 33-bus test system. The results demonstrated the effectiveness of the proposed methodology, highlighting its ability to improve resilience by enhancing system robustness, enabling faster recovery, and optimizing operational costs in response to high-impact low-probability (HILP) natural events. Full article

Personalized urban services are becoming increasingly significant in smart city systems. This shift from intelligent transportation to smart cities broadens the scope of personalized services, encompassing not just travel but a wide range of urban activities and needs. This study proposes a knowledge graph-based Hidden Markov Model (KHMM) to improve personalized route recommendations by incorporating both spatial and semantic relationships between Points of Interest (POIs) in a unified decision-making framework. The KHMM expands the state space of the traditional Hidden Markov Model using a knowledge graph, enabling the integration of multi-dimensional POI information and higher-order relationships. This approach reflects the spatial complexity of urban environments while addressing user-specific preferences. The model’s empirical evaluation, focused on Changsha, China, examined how temporal variations in public attention to POIs influence route selection. The results show that incorporating dynamic temporal and spatial data significantly enhances the model’s adaptability to changing user behaviors, supporting real-time, personalized route recommendations. By bridging individual preferences and road network structures, this research provides key insights into the factors shaping travel behavior and contributes to the development of adaptive and responsive urban transportation systems. These findings highlight the potential of the KHMM to advance intelligent travel services, offering improved spatial accuracy and personalized route planning. Full article

I conducted an integrative literature review by utilizing theoretical and methodological elements of critical realism (i.e., the distinction between ontology and epistemology) to evaluate the significance of triple-helix intermediaries. This review involved examining all published research on smart cities in “elite” ABS (Chartered Association of Business Schools) journals (4, 4*). My findings indicate that the philosophical foundations of the examined literature are predominantly grounded on “positivism”, “postmodernism”, “interpretivism”, and “pragmatism”, without delving into the ontological reinforcement of capitalist institutions through innovation creation and diffusion—a central concern of critical realism. I argue that this oversight stems from the prevailing “paradigm” within these “elite” journals, which often excludes historical and critical perspectives. In response, I propose a reoriented intermediary, the Triple-Helix Business Clinic, grounded in critical-realist assumptions. This new theoretical framework can guide practical policy development aimed at reinforcing business innovation and driving broader socioeconomic progress. Full article

Intersection sight distance (ISD) requirements, currently designed for driver-operated vehicles (DVs), will be affected once automated vehicles (AVs) enter the driving environment. This paper examines the ISD for intersections with a yield control on a minor road in a mixed DV-AV environment. Five potential conflict types with different ISD requirements are modeled as a minor-road vehicle proceeds to cross the intersection, turns right, or turns left. Furthermore, different models are developed for each conflict type depending on the vehicle types on the minor and major roads. These models, along with the intersection geometry, establish the system demand and supply models for ISD reliability analysis. A surrogate safety measure is developed and used to measure ISD non-compliance and is denoted by the probability of unresolved conflicts (PUC). The models are applied to a case study intersection, where PUC values are estimated using Monte Carlo Simulation and compared to an established target value relating to the DV-only traffic of 0.00674. The results show that AV-related traffic has higher overall PUC values than those of DV-only traffic. A corrective measure, reducing the AV speed limit on the minor-road approaches by 3 to 4 km/h, decreases the overall PUC to values below those of the target PUC. Full article

Rural public transport networks face significant challenges, often characterised by suboptimal service quality. With advancements in technology, various applications have been explored to address these issues. Autonomous Demand-Responsive Transits (ADRTs) represent a promising solution that has been investigated over recent years. Their potential to enhance the overall quality of transport systems and promote sustainable transportation is well-recognised. In our research study, we evaluated the viability of ADRTs for rural networks. Our methodology focused on two primary areas: the suitability of ADRTs (considering vehicle type, service offerings, trip purposes, demographic groups, and land use) and the broader impacts of ADRTs (including passenger performance, social impacts, and environmental impacts). Perceptions of ADRT suitability peaked for university precincts and 24/7 operations. However, they were less favoured by mobility-disadvantaged groups (disabled, seniors, and school children). We also examined demographic heterogeneity and assessed the influence of demographic factors (age, gender, education, occupation, household income level, and disability status) on the implementation of ADRTs in rural settings. The findings delineate the varied perceptions across these socio-demographic strata, underscoring the necessity for demographic-specific trials. Consequently, we advocate for the implementation of ADRT services tailored to accommodate the diverse needs of these demographic cohorts. Full article