Smart Cities

Spain’s National Integrated Energy and Climate Plan (PNIEC) addresses the policies and measures needed to contribute to the European target of a 23% reduction in greenhouse gas emissions by 2030 compared to 1990 levels. To this end, the decarbonization of the transport sector is very important in order to increase electric mobility. Electric mobility depends on the conditions of the electrical infrastructure. This research focuses on the electrical distribution network in terms of its current capacity for recharging electric vehicles, which are estimated to account for 20.7% of vehicles, which is about 4 million vehicles. This, therefore, illustrates the need to legislate to improve the electrical infrastructure for recharging electric vehicles, with the aim of deploying electric vehicles on a larger scale and, ultimately, allowing society to benefit from the advantages of this technology. Full article

Traffic simulation, a tool for recreating real-life traffic scenarios, acts as an important platform in transportation research. Considering the growing complexity of urban mobility, various large-scale regional simulators are designed and used for research and applications. Calibration is a key issue in the traffic simulation: it finds the optimal system pattern to decrease the gap between the simulator output and the real data, making the system much more reliable. This paper proposes DRBO, a calibration framework for large-scale traffic simulators. This framework combines the travel behavior adjustment with black box optimization, better exploring the structure of the regional scale mobility. The motivation of the framework is based on the decomposition of the regional scale mobility dynamic. We decompose the mobility dynamic into the car-following dynamic and the routing dynamic. The prior dynamic imitates how vehicles propagate as time flows while the latter one reveals how vehicles choose their route according to their own information. Based on the decomposition, the DRBO framework uses iterative algorithms to find the best dynamic combinations. It utilizes the Bayesian optimization and day-to-day routing update to separately calibrate the dynamic, then combine them sequentially in an iterative way. Compared to the prior arts, the DRBO framework is efficient for capturing multiple perspectives of traffic conditions. We further tested our simulator on SFCTA demand to further validate the speed distribution from our simulation and observed data. Full article

The transition towards a Circular Economy (CE) in the construction sector is essential to achieving sustainable, inclusive smart cities. This study examines the integration of CE principles into construction policies across four key global contexts: the European Union (focusing on Italy and Germany), the United States, and Japan. Through a comparative policy analysis, the research identifies best practices, implementation barriers, and the role of digitalization in advancing CE strategies. In Europe, CE is embedded in policy frameworks such as the Green Deal and the New Circular Economy Action Plan, driving the shift toward sustainable urban development. The United States, while in the early stages of CE adoption, is fostering circular initiatives at local levels, particularly in waste management and building deconstruction. Japan’s policy landscape integrates CE within a broader strategy for resource efficiency, emphasizing technological innovation. The findings highlight the necessity of a research-driven approach to inform policies that leverage digital tools, such as Building Information Modeling and Digital Product Passports, to enhance material traceability and urban circularity. This study contributes to the global effort of designing smart cities that are not only technologically advanced but also environmentally and socially sustainable through the adoption of CE principles in the built environment. Full article

This work focuses on optimizing the scheduling of virtual power plants (VPPs)—as implemented in the Portuguese national project New Generation Storage (NGS)—to maximize social welfare and enhance energy trading efficiency within modern energy grids. By integrating distributed energy resources (DERs), including renewable energy sources and energy storage systems, VPPs represent a pivotal element of sustainable urban energy systems. The scheduling problem is formulated as a Mixed-Integer Linear Programming (MILP) task and addressed by using a parallelized simulated annealing (SA) algorithm implemented on high-performance computing (HPC) infrastructure. This parallelization accelerates solution space exploration, enabling the system to efficiently manage the complexity of larger DER networks and more sophisticated scheduling scenarios. The approach demonstrates its capability to align with the objectives of smart cities by ensuring adaptive and efficient energy distribution, integrating dynamic pricing mechanisms, and extending the operational lifespan of critical energy assets such as batteries. Rigorous simulations highlight the method’s ability to reduce optimization time, maintain solution quality, and scale efficiently, facilitating real-time decision making in energy markets. Moreover, the optimized coordination of DERs supports grid stability, enhances market responsiveness, and contributes to developing resilient, low-carbon urban environments. This study underscores the transformative role of computational infrastructure in addressing the challenges of modern energy systems, showcasing how advanced algorithms and HPC can enable scalable, adaptive, and sustainable energy optimization in smart cities. The findings demonstrate a pathway to achieving socially and environmentally responsible energy systems that align with the priorities of urban resilience and sustainable development. Full article

Modern and smart cities prioritize providing sufficient facilities for inclusive and bicycle-friendly streets. Several methods have been developed to assess city bicycle environments at street, neighborhood, and city levels. However, the importance of micro-level indicators and bicyclists’ perceptions cannot be neglected when developing a bikeability index (BI). Therefore, this paper proposes a new BI method for evaluating and providing suggestions for improving city streets, focusing on bicycle infrastructure facilities. The proposed BI is an analytical system aggregating multiple bikeability indicators into a structured index using weighed coefficients and scores. In addition, the study introduces bicycle infrastructure indicators using five bicycle design principles acknowledged in the literature, experts, and city authorities worldwide. A questionnaire was used to collect data from cyclists to find the weights and scores of the indicators. The survey of 383 participants showed a balanced gender distribution and a predominantly younger population, with most respondents holding bachelor’s or master’s degrees and 57.4% being students. Most participants travel 2–5 km per day and cycle 3 to 5 days per week. Among the criteria, respondents graded safety as the most important, followed by comfort on bicycle paths. Confirmatory factor analysis (CFA) is used to estimate weights of the bikeability indicators, with the values of the resultant factor loadings used as their weights. The highest-weight indicator was the presence of bicycle infrastructure (0.753), while the lowest-weight indicator was slope (0.302). The proposed BI was applied to various bike lanes and streets in Hasselt, Belgium. The developed BI is a useful tool for urban planners to identify existing problems in bicycle streets and provide potential improvements. Full article

A coordinated control for the volt-var optimization (VVO) problem is presented using load tap changer transformers, voltage regulators, and capacitor banks with the integration of a PV-based microgrid. The harmony search (HS) algorithm, which is a metaheuristic-based optimization algorithm, was used to determine global optimum settings of related devices to operate efficiently under changing conditions. The major objectives of volt-var optimization were to reduce power losses, peak power demands, and voltage variations in the distribution circuit while maintaining voltages within the permitted range at all nodes and under all loading conditions. The problem was a mixed integer nonlinear problem with discrete integer variables; binary variables for the capacitor status on/off, voltage regulator taps as integers, and continuous variables; the current output of the microgrid; and nonlinear electric circuit equations. The simulations were verified using the IEEE 13-node test circuit. Daily load profiles of the main power system grid and the microgrid’s PV were used with a 15 min resolution. Power flow solutions were produced using the OpenDSS (version 9.5.1.1, year 2022) power distribution system solver. It can be applied to operational and planning purposes. The results showed that active power loss, peak power demand, and voltage fluctuation were significantly reduced by the coordinated control of the volt-var problem. Full article

The rapid urbanization of recent decades has intensified climate change challenges, demanding sophisticated solutions to build resilient and sustainable cities. A key aspect of sustainable urban planning is decentralizing and democratizing its processes, which requires citizen involvement from the early design stages. While current solutions such as digital tools, participatory workshops, gamification, and social media can enhance participation, they often exclude non-experts or those lacking digital skills. To address these limitations, this manuscript proposes a VR/AR gamified solution using open-source software and open GIS data. Specifically, it investigates the euPOLIS game as an innovative participatory tool offering an alternative to traditional approaches. This game decentralizes urban planning by shifting technical tasks to experts while citizens engage interactively, focusing solely on proposing solutions. To explore the potential of the proposed methodology, the euPOLIS game was demonstrated as a workshop activity in TNOC 2024 Festival, where 30 individuals from different academic background (i.e., citizens, architects, planners, etc.) voluntarily engaged and provided their impressions and feedback. The findings suggest that gamified solutions such as serious/simulation AR/VR games can effectively promote co-design, co-participation, and co-creation in urban planning in an inclusive and engaging manner. Full article

With the evolving urbanization process in modern cities, the tertiary industry load and residential load start to take up a major proportion of the total urban power load. These loads are more dependent on stochastic factors such as human behaviors and weather events, demonstrating frequent abnormal variations that deviate from the normal pattern and causing consequent large forecasting errors. In this paper, a hybrid forecasting framework is proposed focusing on improving the forecasting accuracy of the urban power load during abnormal load variation periods. First, a quantitative method is proposed to define and characterize the abnormal load variations based on the residual component decomposed from the original load series. Second, a sample augmentation method is established based on Generative Adversarial Nets to boost the limited abnormal samples to a larger quantity to assist the forecasting model’s training. Last, an advanced forecasting model, TimesNet, is introduced to capture the complex and nonlinear load patterns during abnormal load variation periods. Simulation results based on the actual load data of Chongqing, China demonstrate the effectiveness of the proposed method. Full article

An essential aspect of any government in a smart city is to examine the issues of internal and external migration. Migration is a complex phenomenon. In order to effectively manage it, it is not only necessary to be able to accurately predict migration patterns but also to understand which factors influence these patterns. Current approaches to the development of migration models rely on macroeconomic indicators without considering the specificities of intraregional interactions among individuals. In this paper, we propose a method for determining the dynamics of migration balance based on Lagrangian mechanics. We derive and interpret the potential energy of a migration network by introducing specific functions that determine migration patterns. The solution of the migration equations and selection of parameters, as well as external forces, are achieved through the use of physics-informed neural networks. We also use external factors to explain the non-homogeneity in the dynamic equation through the use of a regression model. We analyze settlement priorities using transfer operator theory and invariant density. The findings obtained enable the assessment of migration flows and analysis of external migration factors. Full article

The global challenges that cities must face regarding sustainability, efficiency, integration, and resilience have found in the smart city concept a guideline of action as a model for urban development and transformation. The multidimensional nature of the smart city, along with the importance of identifying key urban stakeholders and ensuring their engagement, are two widely recognized characteristics within the scientific community. However, proposals for the identification, classification, and management of urban stakeholders are very scarce and almost non-existent when considered in conjunction with the holistic nature of smart cities. Thus, the significant importance attributed to stakeholder engagement contrasts with the lack of clear guidelines to develop it properly. Based on an iterative analysis of the scientific literature combined with the cross-referencing of smart city dimensions, statistical analysis tools, and multi-criteria analysis methods, this paper proposes a new methodology for the identification and management of urban stakeholders. The proposal includes a comprehensive classification and a new framework for developing urban stakeholder identification processes at their early stages or the monitoring and assessment of ongoing or completed processes, including tools for analyzing the extent and homogeneity achieved. The practical application of the methodology to a specific case study is also discussed. Full article

This study proposes an optimal matching problem between drivers and riders as a generalised Nash equilibrium problem, which finds a solution where no drivers and riders have the incentive to change their strategy. The proposed model is formulated as a (D + R) person pure strategy game, where D and R are the number of drivers and riders, respectively. We further reformulate the proposed model as a two-person pure strategy game. A solution algorithm that iteratively solves the drivers’ and riders’ sub-problem is proposed, which is proven to converge to a Nash equilibrium solution within a finite number of iterations. Finally, we numerically confirm that the proposed model yields a Nash equilibrium solution and then perform sensitivity analysis over the parameters of the proposed model. Full article

To meet the requirements of a smart city in supporting a sustainable high-quality lifestyle for people, there is a need for many smart technologies and smart grids. A smart grid integrates electrical and digital technologies, information, and communication. Microgrids (MGs) are the main components of smart grids. The proposed control scheme introduces a robust control mechanism against model uncertainties and is secure against communication cyberattacks. A novel design criterion is formulated as linear matrix inequalities (LMIs) to provide the required state feedback with integral control. In contrast to the conventional H∞ control approach, the suggested tracker offers enhanced disturbance attenuation and a faster response. Different testing scenarios demonstrate the successful performance of the suggested controller. Full article

Smart city operation assumes dynamic infrastructure in various aspects. However, organization and process modelling require domain expertise and significant efforts from modelers. As a result, such processes are still not well supported by IT systems and still mostly remain manual tasks. Today, machine learning technologies are capable of performing various tasks including those that have normally been associated with people; for example, tasks that require creativeness and expertise. Generative adversarial networks (GANs) are a good example of this phenomenon. This paper proposes an approach to generating organizational and process models using a GAN. The proposed GAN architecture takes into account both tacit expert knowledge encoded in the training set sample models and the symbolic knowledge (rules and algebraic constraints) that is an essential part of such models. It also pays separate attention to differentiable functional constraints, since learning those just from samples is not efficient. The approach is illustrated via examples of logistic system modelling and smart tourist trip booking process modelling. The developed framework is implemented in a publicly available open-source library that can potentially be used by developers of modelling software. Full article

A virtual power plant (VPP) is a potential alternative that aggregates the distributed energy resources (DERs) and addresses the prosumer’s power availability, quality, and reliability requirements. This paper reports the optimized scheduling of an interconnected VPP in a multi-area framework established through a tie-line connection comprising multiple renewable resources. The scheduling was initially performed on a day ahead (hourly basis) interval, followed by an hour ahead interval (intra-hour and real time), i.e., a 15 min and 5 min time interval for the developed VPP in a multi-area context. The target objective functions for the selected problem were two-fold, i.e., net profit and emission, for which maximization was performed for the former and reduction for the later, respectively. Since renewables are involved in the energy mix and the developed problem was complex in nature, the proposed multi-area-based VPP was tested with an advanced nature-inspired metaheuristic technique. Moreover, the proposed formulation was extended to a multi-objective context, and multiple scheduling strategies were performed to reduce the generated emissions and capitalize on the cumulative profit associated with the system by improving the profit margin simultaneously. Furthermore, a comprehensive numeric evaluation was performed with different optimization intervals, which revealed the rapid convergence in minimal computational time to reach the desired solution. Full article

Smart cities aim to enhance the quality of life by advancing infrastructure, leveraging technology, and promoting sustainability, balancing environmental, societal, and economic needs for long-term efficiency. Given resource scarcity and environmental regulations, advanced supply chains play a crucial role in developing smart cities by adopting the circular economy concept, which emphasizes maximizing resource efficiency through recycling and remanufacturing. This study delves into the competition between two types of supply chains in the context of reverse logistics: the hybrid supply chain, which utilizes a dual channel including traditional and e-channels for collecting used products, and the traditional supply chain, which relies solely on a traditional channel. Both supply chain models are actively involved in remanufacturing and recycling used products, and each considers varied policies, including incentive-based policies, advertising investments, the acceptance return quality level, the return processing time, and transportation investments, to enhance their performance. Specifically, this research has two primary objectives: (1) evaluating the economic and environmental outcomes across three competitive scenarios, and (2) analyzing the impact of varied policy settings on these outcomes. These objectives frame the analysis of optimal incentive values, return rates, and profitability across the Nash equilibrium and Nash–Stackelberg structures, providing insights into both the strategic and policy dimensions of supply chain operations. The findings indicate that a hybrid supply chain in this case achieves higher return rates and profitability, highlighting the success of its dual-channel strategy and associated policies. Regarding economic goals, both supply chains achieve the highest profits under the Nash–Stackelberg traditional supply chain leadership structure. However, for environmental goals, the traditional supply chain favors Nash equilibrium for higher return rates, while the hybrid supply chain prefers Nash–Stackelberg with traditional leadership. These scenario-specific results emphasize the importance of aligning economic and environmental goals through tailored strategies. A sensitivity analysis, supported by Pareto prioritization, identifies the return quality level and processing time as critical for the hybrid supply chain, and advertisement investments and the return processing time as key for the traditional supply chain. These insights suggest that H-SCs should prioritize stricter quality standards, efficient inspection protocols, and automation (e.g., AI or optical scanning) to improve the quality and processing time efficiency. Meanwhile, T-SCs should focus on advertising traditional channels by emphasizing faster processing time and less restrictive quality standards, while adopting automated time management strategies similar to H-SCs to enhance engagement and profitability. These findings show that by integrating smart city internet-based initiatives and managing related policies, supply chains can enhance circular economy objectives by optimizing both the economic and environmental performance, ultimately fostering more resilient and sustainable supply chains. Full article

The increasing adoption of smart home devices has raised significant concerns regarding privacy, security, and vulnerability to cyber threats. This study addresses these challenges by presenting a federated learning framework enhanced with blockchain technology to detect intrusions in smart home environments. The proposed approach combines knowledge distillation and transfer learning to support heterogeneous IoT devices with varying computational capacities, ensuring efficient local training without compromising privacy. Blockchain technology is integrated to provide decentralized, tamper-resistant access control through Role-Based Access Control (RBAC), allowing only authenticated devices to participate in the federated learning process. This combination ensures data confidentiality, system integrity, and trust among devices. This framework’s performance was evaluated using the N-BaIoT dataset, showcasing its ability to detect anomalies caused by botnets such as Mirai and BASHLITE across diverse IoT devices. Results demonstrate significant improvements in intrusion detection accuracy, particularly for resource-constrained devices, while maintaining privacy and adaptability in dynamic smart home environments. These findings highlight the potential of this blockchain-enhanced federated learning system to offer a scalable, robust, and privacy-preserving solution for securing smart homes against evolving threats. Full article

Smart cities are Hyper-Connected Digital Environments (HCDEs) that transcend the boundaries of natural, human-made, social, virtual, and artificial environments. Human activities are no longer confined to a single environment as our presence and interactions are represented and interconnected across HCDEs. The data streams and repositories of HCDEs provide opportunities for the responsible application of Artificial Intelligence (AI) that generates unique insights into the constituent environments and the interplay across constituents. The translation of data into insights poses several complex challenges originating in data generation and then propagating through the computational layers to decision outcomes. To address these challenges, this article presents the design and development of a Hyper-Automated AI framework with Generative AI agents for sustainable smart cities. The framework is empirically evaluated in the living lab setting of a ‘University City of the Future’. The developed AI framework is grounded on the core capabilities of acquisition, preparation, orchestration, dissemination, and retrospection, with an independent cognitive engine for hyper-automation of these AI capabilities using Generative AI. Hyper-automation output feeds into a human-in-the-loop process prior to decision-making outcomes. More broadly, this framework aims to provide a validated pathway for university cities of the future to take up the role of prototypes that deliver evidence-based guidelines for the development and management of sustainable smart cities. Full article

Increasing adoption of electric vehicles (EVs) and the expansion of EV charging infrastructure present opportunities for enhancing sustainable transportation within smart cities. However, the interconnected nature of EV charging stations (EVCSs) exposes this infrastructure to various cyber threats, including false data injection, man-in-the-middle attacks, malware intrusions, and denial of service attacks. Financial attacks, such as false billing and theft of credit card information, also pose significant risks to EV users. In this work, we propose a Hyperledger Fabric-based blockchain network for EVCSs to mitigate these risks. The proposed blockchain network utilizes smart contracts to manage key processes such as authentication, charging session management, and payment verification in a secure and decentralized manner. By detecting and mitigating malicious data tampering or unauthorized access, the blockchain system enhances the resilience of EVCS networks. A comparative analysis of pre- and post-implementation of the proposed blockchain network demonstrates how it thwarts current cyberattacks in the EVCS infrastructure. Our analyses include performance metrics using the benchmark Hyperledger Caliper test, which shows the proposed solution’s low latency for real-time operations and scalability to accommodate the growth of EV infrastructure. Deployment of this blockchain-enhanced security mechanism will increase user trust and reliability in EVCS systems. Full article

Urban digital twins (UDTs) were first discussed in 2018. Seven years later, we ask: What has been their contribution to urban planning and decision making so far? Here, we systematically review 88 peer-reviewed articles to map and compare UDTs’ ambitions with their realized contributions. Our results indicate that despite the vast technical developments, socio-technical challenges have remained largely unaddressed, causing many of UDTs’ ambitions to remain unrealized. We identify three categories in these socio-technical challenges: interdisciplinary integration (II), consensual contextualization (CC), and procedural operationalization (PO). Accordingly, we consolidate a socio-technical research and development agenda to realize the ambitions of UDTs for urban planning and decision making: Augmented Urban Planning (AUP). Full article