Authors:Nikolay Shilov, Andrew Ponomarev, Dmitry Ryumin, Alexey Karpov First page: 38 Abstract: 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. Citation: Smart Cities PubDate: 2025-02-28 DOI: 10.3390/smartcities8020038 Issue No:Vol. 8, No. 2 (2025)
Authors:Hisham Soliman, Ehab Bayoumi, Sangkeum Lee First page: 39 Abstract: 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. Citation: Smart Cities PubDate: 2025-03-03 DOI: 10.3390/smartcities8020039 Issue No:Vol. 8, No. 2 (2025)
Authors:Hiroshi Shimamoto First page: 40 Abstract: 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. Citation: Smart Cities PubDate: 2025-03-04 DOI: 10.3390/smartcities8020040 Issue No:Vol. 8, No. 2 (2025)
Authors:Rafael Esteban-Narro, Vanesa G. Lo-Iacono-Ferreira, Juan Ignacio Torregrosa-López First page: 41 Abstract: 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. Citation: Smart Cities PubDate: 2025-03-07 DOI: 10.3390/smartcities8020041 Issue No:Vol. 8, No. 2 (2025)
Authors:Kirill Zakharov, Anton Kovantsev, Alexander Boukhanovsky First page: 42 Abstract: 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. Citation: Smart Cities PubDate: 2025-03-07 DOI: 10.3390/smartcities8020042 Issue No:Vol. 8, No. 2 (2025)
Authors:Yiyan Li, Zizhuo Gao, Zhenghao Zhou, Yu Zhang, Zelin Guo, Zheng Yan First page: 43 Abstract: 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. Citation: Smart Cities PubDate: 2025-03-07 DOI: 10.3390/smartcities8020043 Issue No:Vol. 8, No. 2 (2025)
Authors:Ioannis Kavouras, Ioannis Rallis, Emmanuel Sardis, Eftychios Protopapadakis, Anastasios Doulamis, Nikolaos Doulamis First page: 44 Abstract: 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. Citation: Smart Cities PubDate: 2025-03-10 DOI: 10.3390/smartcities8020044 Issue No:Vol. 8, No. 2 (2025)
Authors:Gulcihan Ozdemir, Pierluigi Siano, Smitha Joyce Pinto, Mohammed AL-Numay First page: 45 Abstract: 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. Citation: Smart Cities PubDate: 2025-03-10 DOI: 10.3390/smartcities8020045 Issue No:Vol. 8, No. 2 (2025)
Authors:Tufail Ahmed, Ali Pirdavani, Geert Wets, Davy Janssens First page: 46 Abstract: 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. Citation: Smart Cities PubDate: 2025-03-12 DOI: 10.3390/smartcities8020046 Issue No:Vol. 8, No. 2 (2025)
Authors:Ali Abbasi, Filipe Alves, Rui A. Ribeiro, João L. Sobral, Ricardo Rodrigues First page: 47 Abstract: 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. Citation: Smart Cities PubDate: 2025-03-12 DOI: 10.3390/smartcities8020047 Issue No:Vol. 8, No. 2 (2025)
Authors:Giulia Marzani, Simona Tondelli, Yuko Kuma, Fernanda Cruz Rios, Rongbo Hu, Thomas Bock, Thomas Linner First page: 48 Abstract: 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. Citation: Smart Cities PubDate: 2025-03-12 DOI: 10.3390/smartcities8020048 Issue No:Vol. 8, No. 2 (2025)
Authors:Xuan Jiang, Yibo Zhao, Chonghe Jiang, Junzhe Cao, Alexander Skabardonis, Alex Kurzhanskiy, Raja Sengupta First page: 49 Abstract: 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. Citation: Smart Cities PubDate: 2025-03-13 DOI: 10.3390/smartcities8020049 Issue No:Vol. 8, No. 2 (2025)
Authors:Juan Antonio Martínez-Lao, Antonio García-Chica, Silvia Sánchez-Salinas, Eduardo José Viciana-Gámez, Alejandro Cama-Pinto First page: 50 Abstract: 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. Citation: Smart Cities PubDate: 2025-03-13 DOI: 10.3390/smartcities8020050 Issue No:Vol. 8, No. 2 (2025)
Authors:Arvind Mukundan, Riya Karmakar, Jumana Jouhar, Muhamed Adil Edavana Valappil, Hsiang-Chen Wang First page: 51 Abstract: Smart cities are urban areas that use advanced technologies to make urban living better through efficient resource management, sustainable development, and improved quality of life. Hyperspectral imaging (HSI) is a noninvasive and nondestructive imaging technique that is revolutionizing smart cities by offering improved real-time monitoring and analysis capabilities across multiple urban sectors. In contrast with conventional imaging technologies, HSI is capable of capturing data across a wider range of wavelengths, obtaining more detailed spectral information, and in turn, higher detection and classification accuracies. This review explores the diverse applications of HSI in smart cities, including air and water quality monitoring, effective waste management, urban planning, transportation, and energy management. This study also examines advancements in HSI sensor technologies, data-processing techniques, integration with Internet of things, and emerging trends, such as combining artificial intelligence and machine learning with HSI for various smart city applications, providing smart cities with real-time, data-driven insights that enhance public health and infrastructure. Although HSI may generate complex data and tends to cost much, its potential to transform cities into smarter and more sustainable environments is vast, as discussed in this review. Citation: Smart Cities PubDate: 2025-03-14 DOI: 10.3390/smartcities8020051 Issue No:Vol. 8, No. 2 (2025)
Authors:Salit Azoulay Kochavi, Oz Kira, Erez Gal First page: 7 Abstract: Climatic changes lead to many extreme weather events throughout the globe. These extreme weather events influence our behavior, exposing us to different environmental conditions, such as poor indoor quality. Poor indoor air quality (IAQ) poses a significant concern in the modern era, as people spend up to 90% of their time indoors. Ventilation influences key IAQ elements such as temperature, relative humidity, and particulate matter (PM). Children, considered a vulnerable group, spend approximately 30% of their time in educational settings, often housed in old structures with poorly maintained ventilation systems. Extreme weather events lead young students to stay indoors, usually behind closed doors and windows, which may lead to exposure to elevated levels of air pollutants. In our research, we aim to demonstrate how real-time monitoring of air pollutants and other environmental parameters under extreme weather is important for regulating the indoor environment. A study was conducted in a school building with limited ventilation located in an arid region near the Red Sea, which frequently suffers from high PM concentrations. In this study, we tracked the indoor environmental conditions and air quality during the entire month of May 2022, including an extreme outdoor weather event of sandstorms. During this month, we continuously monitored four classrooms in an elementary school built in 1967 in Eilat. Our findings indicate that PM2.5 was higher indoors (statistically significant) by more than 16% during the extreme event. Temperature was also elevated indoors (statistically significant) by more than 5%. The parameters’ deviation highlights the need for better indoor weather control and ventilation systems, as well as ongoing monitoring in schools to maintain healthy indoor air quality. This also warrants us as we are approaching an era of climatic instability, including higher occurrence of similar extreme events, which urge us to develop real-time responses in urban areas. Citation: Smart Cities PubDate: 2025-01-03 DOI: 10.3390/smartcities8010007 Issue No:Vol. 8, No. 1 (2025)
Authors:Alexandra Catalina Lazaroiu, Mariacristina Roscia, George Cristian Lazaroiu, Pierluigi Siano First page: 8 Abstract: The Clean Energy package recognizes and offers a favorable regulatory framework for citizens and energy communities with renewable energy sources. However, various countries’ national regulations will be highly important for the successful development of energy communities in existing cities and surrounding areas. Energy communities represent a way in which citizens and local authorities can invest in clean energy sources and energy efficiency, with several benefits in addition to the financial ones, like strengthening the concept of community and individual contributions to reductions in the overall carbon footprint. In this paper, an overview of recent developments in financial incentives in energy communities, their organization, and typologies, as well as benefits shared among the participants, is performed. The overview reveals the potential of energy communities in contributing to the economic, energetic, and social development of cities towards sustainable and smart cities. Citation: Smart Cities PubDate: 2025-01-06 DOI: 10.3390/smartcities8010008 Issue No:Vol. 8, No. 1 (2025)
Authors:Radu Miron, Mihai Hulea, Vlad Muresan, Iulia Clitan, Andrei Rusu First page: 9 Abstract: As cities evolve into smarter and more connected environments, there is a growing need for innovative solutions to improve urban mobility. This study examines the potential of integrating blockchain technology into passenger transportation systems within smart cities, with a particular emphasis on a blockchain-enabled Mobility-as-a-Service (MaaS) solution. In contrast to traditional technologies, blockchain’s decentralized structure improves data security and guarantees transaction transparency, thus reducing the risk of fraud and errors. The proposed MaaS framework enables seamless collaboration between key transportation stakeholders, promoting more efficient utilization of services like buses, trains, bike-sharing, and ride-hailing. By improving integrated payment and ticketing systems, the solution aims to create a smoother user experience while advancing the urban goals of efficiency, environmental sustainability, and secure data handling. This research evaluates the feasibility of a Hyperledger Fabric-based solution, demonstrating its performance under various load conditions and proposing scalability adjustments based on pilot results. The conclusions indicate that blockchain-enabled MaaS systems have the potential to transform urban mobility. Further exploration into pilot projects and the expansion to freight transportation are needed for an integrated approach to city-wide transport solutions. Citation: Smart Cities PubDate: 2025-01-07 DOI: 10.3390/smartcities8010009 Issue No:Vol. 8, No. 1 (2025)
Authors:Tarek Al-Rimawi, Michael Nadler First page: 10 Abstract: This study aims to identify the added value of smart city technologies in real estate development, one of the most significant factors that would transform traditional real estate into smart ones. In total, 16 technologies utilized at both levels have been investigated. The research followed an integrative review methodology; the review is based on 168 publications. The compiled results based on metadata analysis displayed the state of each technology’s added values and usage in both scales. A total of 131 added values were identified. These added values were categorized based on the real estate life cycle sub-phases and processes. Moreover, the value of the integration between these technologies was revealed. The review and results proved that these technologies are mature enough for practical use; therefore, real estate developers, city management, planners, and experts should focus on implementing them. City management should invest in Big Data and geodata and adopt several technologies based on the aspects required for development. This study can influence stakeholders, enhance their decision-making on which technology would suit their needs, and provide recommendations on who to utilize them. Also, it provides a starting point for stakeholders who aim to establish a road map for incorporating smart technologies in future smart real estate. Citation: Smart Cities PubDate: 2025-01-07 DOI: 10.3390/smartcities8010010 Issue No:Vol. 8, No. 1 (2025)
Authors:Nasrin Einabadi, Mehrdad Kazerani First page: 11 Abstract: Nanogrids are becoming an essential part of modern home power systems, offering sustainable solutions for residential areas. These medium-to-low voltage, small-scale grids, operating at medium-to-low voltage, enable the integration of distributed energy resources such as wind turbines, solar photovoltaics, and battery energy storage systems. However, ensuring power quality, stability, and effective energy management remains a challenge due to the variability of renewable energy sources and evolving customer demands, including the increasing charging load of electric vehicles. This paper reviews the current research on nanogrid architecture, functionality in low-voltage distribution systems, energy management, and control systems. It also explores power-sharing strategies among nanogrids within a microgrid framework, focusing on their potential for supplying off-grid areas. Additionally, the application of blockchain technology in providing secure and decentralized energy trading transactions is explored. Potential challenges in future developments of nanogrids are also discussed. Citation: Smart Cities PubDate: 2025-01-16 DOI: 10.3390/smartcities8010011 Issue No:Vol. 8, No. 1 (2025)
Authors:Zi-An Zhao, Shidan Wang, Min-Xin Chen, Ye-Jiao Mao, Andy Chi-Ho Chan, Derek Ka-Hei Lai, Duo Wai-Chi Wong, James Chung-Wai Cheung First page: 12 Abstract: Natural disasters create complex environments where effective human detection is both critical and challenging, especially when individuals are partially occluded. While recent advancements in computer vision have improved detection capabilities, there remains a significant need for efficient solutions that can enhance search-and-rescue (SAR) operations in resource-constrained disaster scenarios. This study modified the original DINO (Detection Transformer with Improved Denoising Anchor Boxes) model and introduced the visibility-enhanced DINO (VE-DINO) model, designed for robust human detection in occlusion-heavy environments, with potential integration into SAR system. VE-DINO enhances detection accuracy by incorporating body part key point information and employing a specialized loss function. The model was trained and validated using the COCO2017 dataset, with additional external testing conducted on the Disaster Occlusion Detection Dataset (DODD), which we developed by meticulously compiling relevant images from existing public datasets to represent occlusion scenarios in disaster contexts. The VE-DINO achieved an average precision of 0.615 at IoU 0.50:0.90 on all bounding boxes, outperforming the original DINO model (0.491) in the testing set. The external testing of VE-DINO achieved an average precision of 0.500. An ablation study was conducted and demonstrated the robustness of the model subject when confronted with varying degrees of body occlusion. Furthermore, to illustrate the practicality, we conducted a case study demonstrating the usability of the model when integrated into an unmanned aerial vehicle (UAV)-based SAR system, showcasing its potential in real-world scenarios. Citation: Smart Cities PubDate: 2025-01-16 DOI: 10.3390/smartcities8010012 Issue No:Vol. 8, No. 1 (2025)
Authors:Jowaria Khan, Rana Elfakharany, Hiba Saleem, Mahira Pathan, Emaan Shahzad, Salam Dhou, Fadi Aloul First page: 13 Abstract: Intrusion detection systems are essential for detecting network cyberattacks. As the sophistication of cyberattacks increases, it is critical that defense technologies adapt to counter them. Multi-step attacks, which need several correlated intrusion operations to reach the desired target, are a rising trend in the cybersecurity field. System administrators are responsible for recreating whole attack scenarios and developing improved intrusion detection systems since the systems at present are still designed to generate alerts for only single attacks with little to no correlation. This paper proposes a machine learning approach to identify and classify multi-step network intrusion attacks, with particular relevance to smart cities, where interconnected systems are highly vulnerable to cyber threats. Smart cities rely on these systems seamlessly functioning with one another, and any successful cyberattack could have devastating effects, including large-scale data theft. In such a context, the proposed machine learning model offers a robust solution for detecting and mitigating multi-step cyberattacks in these critical environments. Several machine learning algorithms are considered, namely Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Light Gradient-Boosting Machine (LGBM), Extreme Gradient Boosting (XGB) and Random Forest (RF). These models are trained on the Multi-Step Cyber-Attack Dataset (MSCAD), a recent dataset that is highly representative of real-world multi-step cyberattack scenarios, which increases the accuracy and efficiency of such systems. The experimental results show that the best performing model was XGB, which achieved a testing accuracy of 100% and an F1 Score of 88%. The proposed model is computationally efficient and easy to deploy, which ensures a fast, sustainable and low power-consuming intrusion detection system at the cutting edge. Citation: Smart Cities PubDate: 2025-01-20 DOI: 10.3390/smartcities8010013 Issue No:Vol. 8, No. 1 (2025)
Authors:Aliaksey A. Kapanski, Roman V. Klyuev, Aleksandr E. Boltrushevich, Svetlana N. Sorokova, Egor A. Efremenkov, Anton Y. Demin, Nikita V. Martyushev First page: 14 Abstract: For large cities with developing infrastructures, optimising water supply systems plays a crucial role. However, without a clear understanding of the network structure and water consumption patterns, addressing these challenges becomes significantly more complex. This paper proposes a methodology for geospatial data analysis aimed at solving two key tasks. The first is the delineation of service zones for infrastructure objects to enhance system manageability. The second involves the development of an approach for the optimal placement of devices to collect and transmit hydraulic network parameters, ensuring their alignment with both water supply sources and serviced areas. The study focuses on data from the water supply network of a city with a population exceeding half a million people, where hierarchical clustering using Ward’s method was applied to analyse territorial distribution. Four territorial clusters were identified, each characterised by unique attributes reflecting consumer concentration and water consumption volumes. The cluster boundaries were compared with the existing service scheme of the system, confirming their alignment with real infrastructure. The quality of clustering was further evaluated using the silhouette coefficient, which validated the high accuracy and reliability of the chosen approach. The paper demonstrates the effectiveness of cluster boundary visualisation for assessing the uniform distribution of pressure sensors within the urban water supply network. The results of the study show that integrating geographic data with water consumption information not only facilitates effective infrastructure planning and resource allocation but also lays the foundation for the digitalization of the hydraulic network, a critical component of sustainable development in modern smart cities. Citation: Smart Cities PubDate: 2025-01-21 DOI: 10.3390/smartcities8010014 Issue No:Vol. 8, No. 1 (2025)
Authors:Jamal Raiyn, Galia Weidl First page: 15 Abstract: This paper proposes a new strategy for a collision avoidance system leveraging time-to-collision (TTC) metrics for handling cut-in scenarios, which are particularly challenging for autonomous vehicles (AVs). By integrating deep learning with TTC calculations, the system predicts potential collisions and determines appropriate evasive actions compared to traditional TTC-based approaches. The methodology is validated through extensive simulations, demonstrating a significant improvement in collision avoidance performance compared to traditional TTC-based approaches. By integrating deep learning models with TTC calculations, the system predicts potential collisions and determines appropriate evasive actions. The use of the Gaussian model to contributes to time-to-collision (TTC) analysis by providing a probabilistic framework to quantify collision risk under uncertainty. It calculates the likelihood that TTC will fall below a critical threshold (TTC_crit), indicating a potential collision. By modeling input variations—such as sensor inaccuracies, fluctuating vehicle velocity, and unpredictable driving behavior—as a Gaussian distribution, the system can handle real-world uncertainties more effectively. This enables continuous, real-time risk prediction, allowing for dynamic and adaptive collision avoidance decisions. The Gaussian approach enhances the robustness of TTC-based systems by improving their ability to predict and prevent collisions in uncertain driving conditions. Citation: Smart Cities PubDate: 2025-01-21 DOI: 10.3390/smartcities8010015 Issue No:Vol. 8, No. 1 (2025)
Authors:Radosław Wolniak, Katarzyna Turoń First page: 16 Abstract: The rapid urbanization and pursuit of sustainability have elevated shared mobility as a cornerstone of smart cities. Among its modalities, scooter-sharing has gained popularity for its convenience and eco-friendliness, yet it faces significant adoption barriers. This study investigates the challenges to scooter-sharing systems within smart cities, focusing on the Silesian region of Poland as a case study. It aims to identify region-specific barriers and opportunities for scooter-sharing adoption in Central and Eastern Europe and to provide insights into its long-term development trends and potential challenges. Using comprehensive statistical methods, including factor analysis and regression models, this study identifies key barriers such as insufficient bike paths, poor path conditions, inadequate signage, fleet maintenance issues, and complex rental processes. External factors like adverse weather and heavy traffic, coupled with health and safety concerns, further hinder adoption, particularly among vulnerable populations. Additionally, the study explores future trends in scooter-sharing, emphasizing the role of advanced technologies, adaptive urban planning, and sustainable fleet management in ensuring long-term feasibility. Drawing on global case studies, it underscores the need for tailored infrastructural investments, advanced fleet management, and user-centric policies to align scooter-sharing systems with smart city goals of sustainability, accessibility, and improved mobility. These findings offer actionable insights for policymakers and service providers striving to integrate scooter-sharing into the evolving landscape of urban mobility. Citation: Smart Cities PubDate: 2025-01-21 DOI: 10.3390/smartcities8010016 Issue No:Vol. 8, No. 1 (2025)
Authors:Oriol Gavaldà-Torrellas, Pilar Monsalvete, Saeed Ranjbar, Ursula Eicker First page: 17 Abstract: Building decarbonization is a major challenge for cities. Deciding which buildings to retrofit buildings, and when and how, is difficult, given the complex interaction between energy costs and investment requirements. Several tools have been developed in recent years to help public and private stakeholders with these decisions, but none cover aspects the authors think are fundamental. For this reason, an urban buildings retrofit tool was developed and is presented in this article. This new tool is based on a bottom-up approach, with all buildings simulated individually, considering aspects such as shading and adjacencies. As a second step, three scenarios with different levels of ambition were implemented in the tool, and the energy demand and emissions resulting from these scenarios were calculated. As a third step, the retrofitting scenarios’ initial investment and operational costs were implemented using a detailed Life Cycle Costing (LCC) approach. A robust and scalable structure was developed and applied to calculate the LCC of various retrofitting scenarios in Montréal, which will be described in detail. Citation: Smart Cities PubDate: 2025-01-24 DOI: 10.3390/smartcities8010017 Issue No:Vol. 8, No. 1 (2025)
Authors:Ning Xu, Xiao Zhang, Pu Wang First page: 18 Abstract: Spontaneous recreational activities in public spaces are a vital source of public vitality. Given the similarity between the walking patterns of recreational crowds in public spaces and the movement of electrons on a two-dimensional circuit surface, this study combines big data from various sources to create an “electrical conductivity surface” that attracts and aggregates recreational crowds. Using current flow simulation, we generate the path selection preferences of people as they move across public spaces. The results reveal an uneven distribution of public spaces in Nanjing’s main urban area, with high-vitality areas mostly concentrated in the urban center. The core demand for enhancing public vitality lies is improving connectivity between multiple spaces. Based on this, the public space plan for Nanjing’s main urban area emphasizes overall connectivity by aligning with the natural landscape, thus linking the city’s green and gray infrastructure. In this study, we have assessed current public space services and their development potential from a number of different angles, developing a digital approach for optimizing the urban layout. We aim to provide a human-centric, bottom-up perspective to complement the top-down city planning and management approach. This will enable urban planners to make informed decisions for creating and managing more vibrant cities. Citation: Smart Cities PubDate: 2025-01-24 DOI: 10.3390/smartcities8010018 Issue No:Vol. 8, No. 1 (2025)
Authors:Anna Kalyuzhnaya, Sergey Mityagin, Elizaveta Lutsenko, Andrey Getmanov, Yaroslav Aksenkin, Kamil Fatkhiev, Kirill Fedorin, Nikolay O. Nikitin, Natalia Chichkova, Vladimir Vorona, Alexander Boukhanovsky First page: 19 Abstract: This study investigates the implementation of LLM agents in smart city management, leveraging both the inherent language processing abilities of LLMs and the distributed problem solving capabilities of multi-agent systems for the improvement of urban decision making processes. A multi-agent system architecture combines LLMs with existing urban information systems to process complex queries and generate contextually relevant responses for urban planning and management. The research is focused on three main hypotheses testing: (1) LLM agents’ capability for effective routing and processing diverse urban queries, (2) the effectiveness of Retrieval-Augmented Generation (RAG) technology in improving response accuracy when working with local knowledge and regulations, and (3) the impact of integrating LLM agents with existing urban information systems. Our experimental results, based on a comprehensive validation dataset of 150 question–answer pairs, demonstrate significant improvements in decision support capabilities. The multi-agent system achieved pipeline selection accuracy of 94–99% across different models, while the integration of RAG technology improved response accuracy by 17% for strategic development queries and 55% for service accessibility questions. The combined use of document databases and service APIs resulted in the highest performance metrics (G-Eval scores of 0.68–0.74) compared to standalone LLM responses (0.30–0.38). Using St. Petersburg’s Digital Urban Platform as a testbed, we demonstrate the practical applicability of this approach to create integrated city management systems with support complex urban decision making processes. This research contributes to the growing field of AI-enhanced urban management by providing empirical evidence of LLM agents’ effectiveness in processing heterogeneous urban data and supporting strategic planning decisions. Our findings suggest that LLM-based multi-agent systems can significantly enhance the efficiency and accuracy of urban decision making while maintaining high relevance in responses. Citation: Smart Cities PubDate: 2025-01-24 DOI: 10.3390/smartcities8010019 Issue No:Vol. 8, No. 1 (2025)
Authors:Ehsan Naderi, Arash Asrari First page: 20 Abstract: This article investigates the impacts of coordinated false data injection attacks (FDIAs) on voltage profiles in smart microgrids integrated with renewable-based distributed energy resources (DERs), a critical component of urban energy infrastructure in smart cities. By leveraging simulation and experimental methods, a coordinated framework is developed for understanding and mitigating these threats, ensuring the stability of renewable-based DERs integral to modern urban systems. In the examined framework, a team of attackers independently identify the optimal times of two different cyberattacks leading to undervoltage and overvoltage in a smart microgrid. The objective function of each model is to increase the voltage violation in the form of either overvoltage or undervoltage caused by the corresponding FDIA. In such a framework, the attackers design a multi-objective optimization problem (MOOP) simultaneously resulting in voltage violations in the most vulnerable regions of the targeted microgrid. Considering the conflict between objective functions in the developed MOOP, a Pareto-based solution methodology is utilized to obtain a set of optimal solutions, called non-dominated solutions, as well as the best compromise solution (BCS). The effectiveness of the unified FDIA is verified based on simulation and experimental validations. In this regard, the IEEE 13-node test feeder has been modified as a microgrid for the simulation analysis, whereas the experimental validation has been performed on a lab-scale hybrid PV/wind microgrid containing renewable energy resources. Citation: Smart Cities PubDate: 2025-01-29 DOI: 10.3390/smartcities8010020 Issue No:Vol. 8, No. 1 (2025)
Authors:Oussama Yahia, Afaq Hyder Chohan, Mohammad Arar, Jihad Awad First page: 21 Abstract: In Dubai’s rapidly expanding urban landscape, addressing the adverse impacts of increasing automobile reliance is critical. Growing vehicle usage contributes to urban sprawl, prolonged commutes, infrastructure strain, and diminished green spaces. As a sustainable alternative, Transit-Oriented Development (TOD) promotes compact density, mixed-use environments, and transit-focused design, particularly suited for Dubai’s evolving context. This study evaluates the applicability of Transit-Adjusted Development (TAD) and TOD appraisal models, specifically the 3D and 6D frameworks, to stations on both the Red and Green Lines of the Dubai Metro. By examining Dubai’s complex urban form, the research identifies strategic interventions to enhance urban mobility and mitigate sprawl. Through an extensive literature review, key factors shaping sustainable urban transport such as accessibility, land-use diversity, density, design, distance to transit, and demand management are analyzed. This investigation highlights the suitability of implementing TOD principles at prominent metro stations, including Al Rashidiya, Al Qusais, and Mall of the Emirates. These stations hold significant potential for strengthening transit efficiency, fostering pedestrian-friendly neighborhoods, and reducing dependency on private vehicles. The findings underscore the importance of integrating TOD strategies into Dubai’s metropolitan planning. By doing so, Dubai can move toward a more connected, efficient, and environmentally responsible urban future. Citation: Smart Cities PubDate: 2025-01-30 DOI: 10.3390/smartcities8010021 Issue No:Vol. 8, No. 1 (2025)
Authors:Abdulaziz I. Almulhim First page: 22 Abstract: Smart city planning is crucial for enhancing urban resilience, especially with the contemporary challenges of rising urban population and climate change. This study conducts a systematic literature review (SLR) to examine the integration of urban resilience in smart city planning, synthesizing the current literature to identify key components, barriers, and enablers. The study found that technological integration, sustainability measures, and citizens’ participation are critical factors to the effective development of smart cities. The review emphasizes the need for an integrated approach to urban resilience, calling for continued research and collaboration among stakeholders. It highlights how urban sustainability and resilience should be addressed within an urban system and that interdisciplinary work, stakeholder consultation, and public engagement are required. It finally suggests the integration of creativity and diversity in urban planning practices and policies for improving vulnerability to modern-day challenges in urban contexts. It concludes by outlining implications for urban planning practices and policy development, advocating for innovative, inclusive strategies to enhance urban resilience. Citation: Smart Cities PubDate: 2025-01-31 DOI: 10.3390/smartcities8010022 Issue No:Vol. 8, No. 1 (2025)
Authors:Saleh Qanazi, Eric Leclerc, Pauline Bosredon First page: 23 Abstract: The rapid evolution of smart city technologies has expanded digital twin (DT) applications from industrial to urban contexts. However, current urban digital twins (UDTs) remain predominantly focused on the physical aspects of urban environments (“spaces”), often overlooking the interwoven social dimensions that shape the concept of “place”. This limitation restricts their ability to fully represent the complex interplay between physical and social systems in urban settings. To address this gap, this paper introduces the concept of the social digital twin (SDT), which integrates social dimensions into UDTs to bridge the divide between technological systems and the lived urban experience. Drawing on an extensive literature review, the study defines key components for transitioning from UDTs to SDTs, including conceptualization and modeling of human interactions (geo-individuals and geo-socials), social applications, participatory governance, and community engagement. Additionally, it identifies essential technologies and analytical tools for implementing SDTs, outlines research gaps and practical challenges, and proposes a framework for integrating social dynamics within UDTs. This framework emphasizes the importance of active community participation through a governance model and offers a comprehensive methodology to support researchers, technology developers, and policymakers in advancing SDT research and practical applications. Citation: Smart Cities PubDate: 2025-02-05 DOI: 10.3390/smartcities8010023 Issue No:Vol. 8, No. 1 (2025)
Authors:Halah Alabdouli, Mohamed S. Hassan, Akmal Abdelfatah First page: 24 Abstract: Due to its anticipated impacts on the performance of transportation systems, intelligent transport systems (ITS) have emerged as one of the most extensively investigated topics. The U.S. Department of Transportation has defined route guidance systems (RGSs) as one of the main categories within ITS. Systems like these are essential components when managing travel and transportation. While RGSs play a pivotal role in both present and future transportation, there has been limited research on evaluating the effectiveness and dependability of integrating them with vehicular communication frameworks. Therefore, this paper aims to evaluate the RGS architectures proposed to date in the literature, providing comparisons and classifications based on their structures and requirements for communication systems. Moreover, it explores existing, next generation, as well as prospective choices for V2X communication technologies, evaluating how well they contribute to the development of RGS applications by integrating them with potential communication systems. Specifically, this study assesses the suitability of communication technologies in meeting the requirements of RGS applications. In conclusion, it suggests a framework for integrating RGS and V2X systems and offers directions for future research in this area. Citation: Smart Cities PubDate: 2025-02-06 DOI: 10.3390/smartcities8010024 Issue No:Vol. 8, No. 1 (2025)
Authors:Aristeidis Mystakidis, Paraskevas Koukaras, Christos Tjortjis First page: 25 Abstract: The ongoing increase in urban populations has resulted in the enduring issue of traffic congestion, adversely affecting the quality of life, including commute duration, road safety, and local air quality. Consequently, recognizing and forecasting underlying traffic congestion patterns have become essential, with Traffic Congestion Prediction (TCP) emerging as an increasingly significant area of study. Advancements in Machine Learning (ML) and Artificial Intelligence (AI), as well as improvements in Internet of Things (IoT) sensor technologies have made TCP research crucial to the development of Intelligent Transportation Systems (ITSs). This review examines advanced TCP, emphasizing innovative forecasting methods and technologies and their importance for the ITS sector. This paper provides an overview of statistical, ML, Deep Learning (DL) approaches, and their ensembles that compose TCP. We examine several forecasting methods and discuss relative and absolute evaluation metrics from regression and classification perspectives. Finally, we present an overall step-by-step standard methodology that is often utilized in TCP problems. By combining these elements, this review highlights critical advancements and ongoing challenges in TCP, providing robust and detailed information for state-of-the-art ITS solutions. Citation: Smart Cities PubDate: 2025-02-07 DOI: 10.3390/smartcities8010025 Issue No:Vol. 8, No. 1 (2025)
Authors:Alessio Russo First page: 26 Abstract: In the biodiversity and climate emergency, a holistic approach is needed for the development of smart cities. This perspective paper proposed a novel conceptual framework for nature-positive smart cities in a socio-technical-ecological system (STES), which bridged the gap between technological advancement and ecological principles within the existing smart city approach, enabling cities to transition towards a biodiversity-led paradigm. Based on recent literature on smart cities and nature-positive cities, this framework combines the principles of nature-positive cities and smart cities with the technological capabilities of Nature 4.0, using tools such as AI, sensors, IoT, big data analytics, and machine learning. The literature shows that smart green spaces have already been developed worldwide; therefore, education is needed for personnel working in local government to effectively use this new technology. The paper presents examples of how smart technologies can be utilised within urban green spaces to maximise ecosystem services and biodiversity. Finally, it provides recommendations and areas for future research, concluding with a call for specific policy interventions to facilitate the transition towards nature-positive smart cities. Citation: Smart Cities PubDate: 2025-02-10 DOI: 10.3390/smartcities8010026 Issue No:Vol. 8, No. 1 (2025)
Authors:Francesca Maria Ugliotti, Muhammad Daud, Emmanuele Iacono First page: 27 Abstract: In an era of increasingly abundant and granular spatial and temporal data, the traditional divide between environmental GIS and building-centric BIM scales is diminishing, offering an opportunity to enhance natural hazard assessment by bridging the gap between territorial impacts and the effects on individual structures. This study addresses the challenge of integrating disparate data formats by establishing a centralised database as the foundation for a comprehensive risk assessment approach. A use case focusing on flood risk assessment for a public building in northwest Italy demonstrates the practical implications of this integrated methodology. The proposed TErritorial RIsk Management & Analysis Across Scale (TERIMAAS) framework utilises this centralised repository to store, process, and dynamically update diverse BIM and GIS datasets, incorporating real-time IoT-derived information. The GIS spatial analysis assesses risk scores for each hazard type, providing insights into vulnerability and potential impacts. BIM data further refine this assessment by incorporating building and functional characteristics, enabling a comprehensive evaluation of resilience and risk mitigation strategies tailored to dynamic environmental conditions across scales. The results of the proposed scalable approach could provide a valuable understanding of the territory for policymakers, urban planners, and any stakeholder involved in disaster risk management and infrastructure resilience planning. Citation: Smart Cities PubDate: 2025-02-11 DOI: 10.3390/smartcities8010027 Issue No:Vol. 8, No. 1 (2025)
Authors:Sebin Choi, Sungmin Yoon First page: 28 Abstract: The concept of digital twins (DTs) has expanded to encompass buildings and cities, with urban building energy modeling (UBEM) playing a crucial role in predicting urban-scale energy consumption via modeling individual energy use and interactions. As a virtual model within urban digital twins (UDTs), UBEM offers the potential for managing energy in sustainable cities. However, UDTs face challenges with regard to integrating large-scale data and relying on bottom-up UBEM approaches. In this study, we propose an AI agent-based intelligent urban digital twin (I-UDT) to enhance DTs’ technical realization and UBEM’s service functionality. Integrating GPT within the UDT enabled the efficient integration of fragmented city-scale data and the extraction of building features, addressing the limitations of the service realization of traditional UBEM. This framework ensures continuous updates of the virtual urban model and the streamlined provision of updated information to users in future studies. This research establishes the concept of an I-UDT and lays a foundation for future implementations. The case studies include (1) data analysis, (2) prediction, (3) feature engineering, and (4) information services for 3500 buildings in Seoul. Through these case studies, the I-UDT was integrated and analyzed scattered data, predicted energy consumption, derived conditioned areas, and evaluated buildings on benchmark. Citation: Smart Cities PubDate: 2025-02-11 DOI: 10.3390/smartcities8010028 Issue No:Vol. 8, No. 1 (2025)
Authors:Imane Serbouti, Jérôme Chenal, Saâd Abdesslam Tazi, Ahmad Baik, Mustapha Hakdaoui First page: 29 Abstract: The advent of digital transformation has redefined the preservation of cultural heritage and historic sites through the integration of Digital Twin technology. Initially developed for industrial applications, Digital Twins are now increasingly employed in heritage conservation as dynamic, digital replicas of physical assets and environments. These systems enable detailed, interactive approaches to documentation, management, and preservation. This paper presents a detailed framework for implementing Digital Twin technology in the management of heritage buildings. By utilizing advanced methods for data collection, processing, and analysis, the framework creates a robust data hub for Digital Twin Heritage Buildings (DTHB). This architecture enhances real-time monitoring, improves accuracy, reduces operational costs, and enables predictive maintenance while minimizing invasive inspections. Focusing on Bab Al-Mansour Gate in Meknes, Morocco, a significant cultural landmark, this research outlines the workflow for developing a Bab Al-Mansour DTHB platform. The platform monitors structural health and detects damage over time, offering a dynamic tool for conservation planning. By integrating innovative technologies with data-driven solutions, this study provides a replicable model for preserving heritage sites, addressing critical gaps in real-time monitoring, resource optimization, and environmental risk mitigation. Citation: Smart Cities PubDate: 2025-02-12 DOI: 10.3390/smartcities8010029 Issue No:Vol. 8, No. 1 (2025)
Authors:Charan Teja Madabathula, Kunal Agrawal, Vijen Mehta, Swathi Kasarabada, Sai Srimai Kommamuri, Guannan Liu, Jerry Gao First page: 30 Abstract: The increasing demand for energy efficiency and the integration of renewable energy sources have become crucial for sustainability in modern campuses. This work presents a smart green energy management system (SGEMS) that integrates a machine learning model and reinforcement learning (RL) to optimize energy consumption and solar generation across a green campus. Using historical data from three campus buildings, we developed a predictive model to forecast short-term energy consumption and solar generation. The XGBoost algorithm, combined with RL, demonstrated superior performance in predicting energy consumption and generation, outperforming other models with a root mean square error (RMSE) of 14.72, a mean absolute error (MAE) of 12.00, and a mean absolute percentage error (MAPE) of 2.18%. This work proposes a web-based interface for real-time energy monitoring and decision-making, helping users forecast power shortages and manage energy usage effectively. The proposed approach provides a scalable solution for campuses aiming to reduce reliance on external grids and increase energy efficiency, setting a benchmark for future green campus initiatives. Citation: Smart Cities PubDate: 2025-02-13 DOI: 10.3390/smartcities8010030 Issue No:Vol. 8, No. 1 (2025)
Authors:Ryota Kodera, Takanori Sakai, Tetsuro Hyodo First page: 31 Abstract: Developing policy instruments related to urban freight, such as congestion pricing, urban consolidation schemes, and off-hours delivery, requires an understanding of the distribution of shipment delivery times. Furthermore, agent-based urban freight simulators use relevant information (shipment delivery time distribution or vehicle tour start time distribution) as input to simulate tour generation. However, studies focusing on shipment delivery time-period selection modeling are very limited. In this study, we propose a method using GPS trajectory data from the Tokyo Metropolitan Area to estimate a shipment delivery time-period selection model based on pseudo-shipment records inferred from GPS data. The results indicate that shipment distance, size, and destination attributes can explain the delivery times of goods. Moreover, we demonstrate the practicality of the model by comparing the simulation result with the observed data for three areas with distinct characteristics, concluding that the model could be applied to urban freight simulation models for accurately reproducing spatial heterogeneity in shipment delivery time periods. This study contributes to promoting smart city development and management by proposing a method to use big data to better understand deliveries and support the development of relevant advanced city logistics solutions. Citation: Smart Cities PubDate: 2025-02-13 DOI: 10.3390/smartcities8010031 Issue No:Vol. 8, No. 1 (2025)
Authors:Shervin Azadi, Dena Kasraian, Pirouz Nourian, Pieter van Wesemael First page: 32 Abstract: 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). Citation: Smart Cities PubDate: 2025-02-13 DOI: 10.3390/smartcities8010032 Issue No:Vol. 8, No. 1 (2025)
Authors:Abdullahi Chowdhury, Sakib Shahriar Shafin, Saleh Masum, Joarder Kamruzzaman, Shi Dong First page: 33 Abstract: 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. Citation: Smart Cities PubDate: 2025-02-15 DOI: 10.3390/smartcities8010033 Issue No:Vol. 8, No. 1 (2025)
Authors:Daswin De Silva, Nishan Mills, Harsha Moraliyage, Prabod Rathnayaka, Sam Wishart, Andrew Jennings First page: 34 Abstract: 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. Citation: Smart Cities PubDate: 2025-02-17 DOI: 10.3390/smartcities8010034 Issue No:Vol. 8, No. 1 (2025)
Authors:Mohammed Shalan, Md Rakibul Hasan, Yan Bai, Juan Li First page: 35 Abstract: 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. Citation: Smart Cities PubDate: 2025-02-17 DOI: 10.3390/smartcities8010035 Issue No:Vol. 8, No. 1 (2025)
Authors:Asra Aghaei, Fulin Cai, Teresa Wu First page: 36 Abstract: 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. Citation: Smart Cities PubDate: 2025-02-18 DOI: 10.3390/smartcities8010036 Issue No:Vol. 8, No. 1 (2025)
Authors:Anubhav Kumar Pandey, Vinay Kumar Jadoun, Jayalakshmi N. Sabhahit, Sachin Sharma First page: 37 Abstract: 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. Citation: Smart Cities PubDate: 2025-02-18 DOI: 10.3390/smartcities8010037 Issue No:Vol. 8, No. 1 (2025)
Authors:Danila Parygin, Alexander Anokhin, Anton Anikin, Anton Finogeev, Alexander Gurtyakov First page: 1 Abstract: City services and infrastructures are focused on consumers and are able to effectively and qualitatively implement their functions only under conditions of normal workload. In this regard, the correct organization of a public service system is directly related to the knowledge of the quantitative and qualitative composition of people in the city during the day. The article discusses existing solutions for analyzing the distribution of people in a territory based on data collected by mobile operators, payment terminals, navigation systems and other network solutions, as well as the modeling methods derived from them. The scientific aim of the study is to propose a solution for modeling the daily distribution of people based on open statistics collected from the Internet and open-web mapping data. The stages of development of the modeling software environment and the methods for spatial analysis of available data on a digital cartographic basis are described. The proposed approach includes the use of archetypes of social groups, occupational statistics, gender and age composition of a certain territory, as well as the characteristics of urban infrastructure objects in terms of composition and purpose. Solutions for modeling the 48 h distribution of city residents with reference to certain infrastructure facilities (residential, public and working) during working and weekend days with an hourly breakdown of the simulated values were created as a result of the study. A simulation of the daily distribution of people in the city was carried out using the example of the city of Volgograd, Russian Federation. A picture of the daily distribution of city residents by district and specific buildings of the city was obtained as a result of the modeling. The proposed approach and the created algorithm can be applied to any city. Citation: Smart Cities PubDate: 2024-12-24 DOI: 10.3390/smartcities8010001 Issue No:Vol. 8, No. 1 (2024)
Authors:Fabian Schuhmann, Ngoc An Nguyen, Jörg Schweizer, Wei-Chieh Huang, Markus Lienkamp First page: 2 Abstract: Mobility digital twins (MDTs), which utilize multi-modal microscopic (micro) traffic simulations and an activity-based demand generation, are envisioned as flexible and reliable planning tools for addressing today’s increasingly complex and diverse transport scenarios. Hybrid models may become a resource-efficient solution for building MDTs by creating large-scale, mesoscopic (meso) traffic simulations, using simplified, queue-based network-link models, in combination with more detailed local micro-traffic simulations focused on areas of interest. The overall objective of this paper is to develop an efficient toolchain capable of automatically generating, calibrating, and validating hybrid scenarios, with the following specific goals: (i) an automated and seamless merge of the meso- and micro-networks and demand; (ii) a validation procedure that incorporates real-world data into the hybrid model, enabling the meso- and micro-sub-models to be validated separately and compared to determine which simulation, micro- or meso-, more accurately reflects reality. The developed toolchain is implemented and applied to a case study of Munich, Germany, with the micro-simulation focusing on the city quarter of Schwabing, using real-word traffic flow and floating car data for validation. When validating the simulated flows with the detected flows, the regression curve shows acceptable values. The speed validation with floating car data reveals significant differences; however, it demonstrates that the micro-simulation achieves considerably better agreement with real speeds compared to the meso-model, as expected. Citation: Smart Cities PubDate: 2024-12-24 DOI: 10.3390/smartcities8010002 Issue No:Vol. 8, No. 1 (2024)
Authors:David Cabezuelo, Izar Lopez-Ramirez, June Urkizu, Ander Goikoetxea First page: 3 Abstract: Power consumption prediction is a crucial component in enhancing the efficiency and sustainability of building operations. This study investigates the impact of data collection frequency and model selection on the predictive accuracy of power consumption in two distinct building types: an Academic one with 15-min interval data and an Industrial one with hourly data. Various machine learning models, including Support Vector Machine (SVM) with Radial and Sigmoid kernels, Random Forest (RF), and Deep Neural Networks (DNNs), across different data splits and feature sets, were considered. Our analysis reveals that higher data collection frequency generally improves model performance, as indicated by lower RMSE, MAPE, and CV values, alongside higher R² scores. The inclusion of more historical power consumption features was also found to have a more significant impact on the accuracy of predictions than including climate condition features. Moreover, the SVM-Radial model consistently outperformed others, particularly in capturing complex, non-linear patterns in the data. However, the DNN model, while competent in some metrics, showed elevated MAPE values, suggesting potential overfitting issues. These findings suggest that careful consideration of data frequency, features, and model selection is essential for optimizing power prediction, contributing to more efficient power management strategies in building operations. Citation: Smart Cities PubDate: 2024-12-24 DOI: 10.3390/smartcities8010003 Issue No:Vol. 8, No. 1 (2024)
Authors:Mouri Zakir, Gregor Wolbring, Svetlana Yanushkevich First page: 4 Abstract: This paper utilizes a methodological two-step process incorporating statistical and causal probabilistic modeling techniques to investigate factors affecting the accessibility experiences of persons with disabilities in Canada. We deploy a network-based approach using empirical data to perform a holistic assessment of the relations between various demographic features (e.g., age, gender and type of disability) and accessibility barriers. A statistical measurement method is applied that utilizes structural equation modeling supported by exploratory factor analysis. For causal probabilistic modeling, Bayesian networks are employed as a straightforward and compact way to interpret knowledge representation. This causal reasoning approach analyzes the nature and frequency of encountering barriers based on data to understand the risk factors contributing to pressing accessibility issues. Furthermore, to evaluate network performance and overcome any data limitations, synthetic data generation techniques are applied to create and validate artificial data built on real-world knowledge. The proposed framework strives to provide reasoning to understand the prevalence of physical, social, communication or technological barriers encountered by persons with disabilities in their daily lives. This study contributes to the identification of areas for prioritization in facilitating accessibility regulation and practices to realize an inclusive society. Citation: Smart Cities PubDate: 2024-12-24 DOI: 10.3390/smartcities8010004 Issue No:Vol. 8, No. 1 (2024)
Authors:Jun Liang, Zongjia Zhang, Yanpeng Zhi First page: 5 Abstract: Natural disasters (e.g., floods, earthquakes) significantly impact citizens, economies, and the environment worldwide. Due to their sudden onset, devastating effects, and high uncertainty, it is crucial for emergency departments to take swift action to minimize losses. Among these actions, planning the locations of relief supply distribution centers and dynamically allocating supplies is paramount, as governments must prioritize citizens’ safety and basic living needs following disasters. To address this challenge, this paper develops a three-layer emergency logistics network to manage the flow of emergency materials, from warehouses to transfer stations to disaster sites. A bi-objective, multi-period stochastic integer programming model is proposed to solve the emergency location, distribution, and allocation problem under uncertainty, focusing on three key decisions: transfer station selection, upstream emergency material distribution, and downstream emergency material allocation. We introduce a multi-armed bandit algorithm, named the Geometric Greedy algorithm, to optimize transfer station planning while accounting for subsequent dynamic relief supply distribution and allocation in a stochastic environment. The new algorithm is compared with two widely used multi-armed bandit algorithms: the ϵ-Greedy algorithm and the Upper Confidence Bound (UCB) algorithm. A case study in the Futian District of Shenzhen, China, demonstrates the practicality of our model and algorithms. The results show that the Geometric Greedy algorithm excels in both computational efficiency and convergence stability. This research offers valuable guidelines for emergency departments in optimizing the layout and flow of emergency logistics networks. Citation: Smart Cities PubDate: 2024-12-25 DOI: 10.3390/smartcities8010005 Issue No:Vol. 8, No. 1 (2024)
Authors:Min Hu, Pengpeng Zhao, Jing Lu, Bingjian Wu First page: 6 Abstract: Ground settlement is a crucial indicator for assessing the safety of shield tunneling and its impact on the surrounding environment. However, most existing settlement prediction methods are based on historical data, which can only be applied with effective monitoring conditions. To overcome this limitation, this paper proposes the mechanism-driven intelligent settlement prediction method (MISPM), which considers the mechanisms of settlement and attitude movements during construction to design new features that can indirectly reflect settlement. Simulation experiments were used to compare the impact of different candidate features and algorithms on prediction performance, verifying the validity and accuracy of the model. The efficacy of MISPM in predicting settlement changes in advance was substantiated by practical engineering applications. Results showed that MISPM could accurately predict settlement changes even without ground monitoring, thereby corroborating its reliability and applicability in supporting safe tunneling in complex geological environments. In the construction of urban infrastructure, this method has the potential to enhance the efficiency of tunnel construction and ensure environmental safety, which is of great significance for the development of smart cities. Citation: Smart Cities PubDate: 2024-12-27 DOI: 10.3390/smartcities8010006 Issue No:Vol. 8, No. 1 (2024)
Authors:Hualiang Fang, Jiaqi Liao, Shuo Huang, Maojie Zhang First page: 3055 Abstract: With the rapid development of electric vehicles, the infrastructure for charging stations is also expanding quickly, and the failure rate of charging piles is increasing. To address the effective operation and maintenance of charging stations, a method based on the XGBoost algorithm for electric vehicle DC charging stations is proposed. An operation and maintenance system is constructed based on state analysis, considering the operational status of the charging stations and users’ charging habits. Factors such as driving and charging habits, road traffic, and charging station equipment are taken into account. The training sample data are established using historical data, online monitoring data, and external environmental data, and the charging station status evaluation model is trained using the XGBoost algorithm. Based on the condition assessment results, a risk assessment model is established in combination with fault parameters. Risk tracking of the charging stations is conducted using the energy not charged (ENC), evaluating the risk level of each station and determining the operation and maintenance order. The optimal operation and maintenance model for DC charging stations, aimed at achieving both economic and reliability goals, is constructed to determine the operation and maintenance schedule for each station. The results of the case study demonstrate that the state evaluation and operation and maintenance strategy can significantly improve the reliability of the system and the overall benefits of operation and maintenance while meeting the required standards. Citation: Smart Cities PubDate: 2024-10-22 DOI: 10.3390/smartcities7060119 Issue No:Vol. 7, No. 6 (2024)
Authors:Junjun Xiang, Omid Ghaffarpasand, Francis D. Pope First page: 3071 Abstract: Employing vehicle telematics data, this study investigates the transport environment across urban and major road networks during a two-week period encompassing the Easter holidays, considered as a case study. The analysis spans four distinct years: 2016, 2018, 2021, and 2022. Geospatial and Temporal Mapping captured the dependencies of vehicle speed, acceleration, vehicle-specific power (VSP), and emission factors (EFs) for air pollutants (CO2 and NOx) on the studied calendar period. The results showed that during the Easter holiday, the median vehicle speeds exceeded annual averages by roughly 5%, indicating a clear deviation from regular traffic patterns. This deviation was particularly stark during the 2021 lockdown, with a significant drop in vehicle presence, leading to less congestion and thus higher speeds and vehicle acceleration. The emissions analyses revealed that individual cars emit higher levels of CO2 and NOx during Easter. Specifically, the median values of CO2 EF and NOx EF were 9% and 11% higher than the annual norm. When combined with road occupancy data, the results demonstrate that the Easter holidays in 2022 had a variable impact on NOx and CO2 emissions, with significant reductions on major roads during weekday rush hours (15–25%) but slight increases on urban roads during weekend periods. Citation: Smart Cities PubDate: 2024-10-24 DOI: 10.3390/smartcities7060120 Issue No:Vol. 7, No. 6 (2024)
Authors:Saverio Ieva, Davide Loconte, Giuseppe Loseto, Michele Ruta, Floriano Scioscia, Davide Marche, Marianna Notarnicola First page: 3095 Abstract: Digital-twin platforms are increasingly adopted in energy infrastructure management for smart grids. Novel opportunities arise from emerging artificial intelligence technologies to increase user trust by enhancing predictive and prescriptive analytics capabilities and by improving user interaction paradigms. This paper presents a novel data-driven and knowledge-based energy digital-twin framework and architecture. Data integration and mining based on machine learning are integrated into a knowledge graph annotating asset status data, prediction outcomes, and background domain knowledge in order to support a retrieval-augmented generation approach, which enhances a conversational virtual assistant based on a large language model to provide user decision support in asset management and maintenance. Components of the proposed architecture have been mapped to commercial-off-the-shelf tools to implement a prototype framework, exploited in a case study on the management of a section of the high-voltage energy infrastructure in central Italy. Citation: Smart Cities PubDate: 2024-10-24 DOI: 10.3390/smartcities7060121 Issue No:Vol. 7, No. 6 (2024)
Authors:Narayanamoorthi Rajamanickam, Pradeep Vishnuram, Dominic Savio Abraham, Miroslava Gono, Petr Kacor, Tomas Mlcak First page: 3121 Abstract: The rapid advancement and adoption of electric vehicles (EVs) necessitate innovative solutions to address integration challenges in modern charging infrastructure. Dynamic wireless charging (DWC) is an innovative solution for powering electric vehicles (EVs) using multiple magnetic transmitters installed beneath the road and a receiver located on the underside of the EV. Dynamic charging offers a solution to the issue of range anxiety by allowing EVs to charge while in motion, thereby reducing the need for frequent stops. This manuscript reviews several pivotal areas critical to the future of EV DWC technology such as authentication techniques, blockchain applications, driver identification systems, economic aspects, and emerging communication technologies. Ensuring secure access to this charging infrastructure requires fast, lightweight authentication systems. Similarly, blockchain technology plays a critical role in enhancing the Internet of Vehicles (IoV) architecture by decentralizing and securing vehicular networks, thus improving privacy, security, and efficiency. Driver identification systems, crucial for EV safety and comfort, are analyzed. Additionally, the economic feasibility and impact of DWC are evaluated, providing essential insights into its potential effects on the EV ecosystem. The paper also emphasizes the need for quick and lightweight authentication systems to ensure secure access to DWC infrastructure and discusses how blockchain technology enhances the efficiency, security, and privacy of IoV networks. The importance of driver identification systems for comfort and safety is evaluated, and an economic study confirms the viability and potential benefits of DWC for the EV ecosystem. Citation: Smart Cities PubDate: 2024-10-24 DOI: 10.3390/smartcities7060122 Issue No:Vol. 7, No. 6 (2024)
Authors:Miguel Islas-Toski, Erik Cuevas, Marco Pérez-Cisneros, Héctor Escobar First page: 3165 Abstract: Buildings and their supporting infrastructure are vulnerable to both natural and human-made disasters, which pose significant risks to the safety of the occupants. Evacuation models are essential tools for assessing these risks and for ensuring the safety of individuals during emergencies. The primary objective of an evacuation model is to realistically simulate the process by which a large group of people can reach available exits efficiently. This paper introduces an agent-based evacuation model that represents the environment as a rectangular grid, where individuals, obstacles, and exits interact dynamically. The model employs only five rules to simulate evacuation dynamics while also accounting for complex factors such as movement and stagnation. Different from many evacuation models, this approach includes rules that account for common behaviors exhibited in stressful evacuation situations such as accidents, hysteria, and disorientation. By incorporating these behavioral conditions, the model more accurately reflects the real-life reactions of individuals during evacuation, leading to more realistic and applicable results. To validate the effectiveness of the proposed model, comprehensive experiments and case studies are conducted in diverse urban settings. The results of these experiments demonstrate that the model offers valuable insights into the evacuation process and provides a more precise assessment of its behavior in emergency scenarios. Citation: Smart Cities PubDate: 2024-10-25 DOI: 10.3390/smartcities7060123 Issue No:Vol. 7, No. 6 (2024)
Authors:Águeda Veloso, Fernando Fonseca, Rui Ramos First page: 3188 Abstract: Urbanization growth poses various challenges, such as congestion, pollution, and resource consumption, prompting city planners and governments to adopt smart systems to manage these issues more efficiently. Despite widespread adoption, there is no consensus on the defining attributes of smart cities, particularly regarding their role in urban sustainability and contemporary urbanism. This paper provides a literature review to understand the implications of smart city initiatives for sustainable urban planning, focusing on practices in Singapore, Helsinki, Barcelona, and Medellin. Based on 71 publications surveyed from Scopus and Web of Science, this paper evaluates smart, sustainable initiatives undertaken in these four cities across six smart domains: mobility, governance, environment, people, living, and economy. This review shows that most studies focus on Barcelona and Singapore, particularly in the domains of smart environment and governance. Despite differing urban contexts, the notion of “smart” is closely tied to using information and communication technologies to drive urban operations. This analysis identifies a lack of assessment studies on the benefits of smart cities in terms of urban sustainability and a lack of holistic approaches to address the complex challenges cities face in achieving sustainable development. Citation: Smart Cities PubDate: 2024-10-28 DOI: 10.3390/smartcities7060124 Issue No:Vol. 7, No. 6 (2024)
Authors:Alberto Robles-Enciso, Ricardo Robles-Enciso, Antonio F. Skarmeta Gómez First page: 3210 Abstract: Reducing carbon emissions is a critical issue for the near future as climate change is an imminent reality. To reduce our carbon footprint, society must change its habits and behaviours to optimise energy consumption, and the current progress in embedded systems and artificial intelligence has the potential to make this easier. The smart building concept and intelligent energy management are key points to increase the use of renewable sources of energy as opposed to fossil fuels. In addition, cyber-physical systems (CPSs) provide an abstraction of the management of services that allows the integration of both virtual and physical systems in a seamless control architecture. In this paper, we propose to use multiagent reinforcement learning (MARL) to model the CPS services control plane in a smart house, with the purpose of minimising, by shifting or shutdown services, the use of non-renewable energy (fuel generator) by exploiting solar production and batteries. Furthermore, our proposal dynamically adapts its behaviour in real time according to current and historic energy production, thus being able to handle occasional changes in energy production due to meteorological phenomena or unexpected energy consumption. In order to evaluate our proposal, we have developed an open-source smart building energy simulator and deployed our use case. Finally, several simulations with different configurations are evaluated to verify the performance. The simulation results show that the reinforcement learning solution outperformed the priority-based and the heuristic-based solutions in both power consumption and adaptability in all configurations. Citation: Smart Cities PubDate: 2024-10-29 DOI: 10.3390/smartcities7060125 Issue No:Vol. 7, No. 6 (2024)
Authors:Gonzalo Abad, Ander Plaza, Gorka Kerejeta First page: 3241 Abstract: Small wind turbines placed at city locations are affected by variable-speed winds that frequently change direction. Architectural constructions, buildings of different heights and abrupt orography of Cities make the winds that occur at City locations more variable than in flat lands or at sea. However, the performance of Small-wind turbines under this type of variable wind has not been deeply studied in the specialised literature. Therefore, this article analyses the behaviour of small wind turbines under variable and gusty winds of cities, also considering three types of power electronics conversion configurations: the generally used Maximum Power Point Tracking (MPPT) configuration, the simple only-rectifier configuration and an intermediate configuration in terms of complexity called pseudo-MPPT. This general-purpose analysis is applied to a specific type of wind turbine, i.e., the Ayanz wind turbine with screw blades, which presents adequate characteristics for city locations such as; safety, reduced visual and acoustic impacts and bird casualties avoidance. Thus, a wide simulation and experimental tests-based analysis are carried out, identifying the main factors affecting the maximisation of energy production of small wind turbines in general and the Ayanz turbine in particular. It is concluded that the mechanical inertia of the wind turbine, often not even considered in the energy production analysis, is a key factor that can produce decrements of up to 25% in energy production. Then, it was also found that electric factors related to the power electronics conversion system can strongly influence energy production. Thus, it is found that an adequate design of a simple pseudo-MPPT power conversion system could extract even 5% more energy than more complex MPPT configurations, especially in quickly varying winds of cities. Citation: Smart Cities PubDate: 2024-10-30 DOI: 10.3390/smartcities7060126 Issue No:Vol. 7, No. 6 (2024)
Authors:Anila Kousar, Saeed Ahmed, Abdullah Altamimi, Zafar A. Khan First page: 3289 Abstract: The automotive industry has evolved enormously in recent years, marked by the proliferation of smart vehicles furnished with avant-garde technologies. These intelligent automobiles leverage cutting-edge innovations to deliver enhanced connectivity, automation, and convenience to drivers and passengers. Despite the myriad benefits of smart vehicles, their integration of digital systems has raised concerns regarding cybersecurity vulnerabilities. The primary components of smart cars within smart vehicles encompass in-vehicle communication and intricate computation, in addition to conventional control circuitry. In-vehicle communication is facilitated through a controller area network (CAN), whereby electronic control units communicate via message transmission across the CAN-bus, omitting explicit destination specifications. This broadcasting and non-delineating nature of CAN makes it susceptible to cyber attacks and intrusions, posing high-security risks to the passengers, ultimately prompting the requirement of an intrusion detection system (IDS) accepted for a wide range of cyber-attacks in CAN. To this end, this paper proposed a novel machine learning (ML)-based scheme employing a Pythagorean distance-based algorithm for IDS. This paper employs six real-time collected CAN datasets while studying several cyber attacks to simulate the IDS. The resilience of the proposed scheme is evaluated while comparing the results with the existing ML-based IDS schemes. The simulation results showed that the proposed scheme outperformed the existing studies and achieved 99.92% accuracy and 0.999 F1-score. The precision of the proposed scheme is 99.9%, while the area under the curve (AUC) is 0.9997. Additionally, the computational complexity of the proposed scheme is very low compared to the existing schemes, making it more suitable for the fast decision-making required for smart vehicles. Citation: Smart Cities PubDate: 2024-11-02 DOI: 10.3390/smartcities7060127 Issue No:Vol. 7, No. 6 (2024)
Authors:Somaye Moghari, Mohammad K. Fallah, Saeid Gorgin, Seokjoo Shin First page: 3315 Abstract: The increasing use of mobile networks is an opportunity to collect and model users’ movement data for extracting knowledge about life and health while considering privacy leakage risk. This study aims to approximate the lifestyles of urban residents, employing statistical information derived from their movements among various Points of Interest (PoI). Our investigations comprehend a multidimensional analysis of key urban factors to provide insights into the population’s daily routines, preferences, and characteristics. To this end, we developed a framework called LEAF that models lifestyles by interpreting anonymized cell phone mobility data and integrating it with information from other sources, such as geographical layers of land use and sets of PoI. LEAF presents the information in a vector space model capable of responding to spatial queries about lifestyle. We also developed a consolidated lifestyle pattern framework to systematically identify and analyze the dominant activity patterns in different urban areas. To evaluate the effectiveness of the proposed framework, we tested it on movement data from individuals in a medium-sized city and compared the results with information collected through surveys. The RMSE of 5.167 between the proposed framework’s results and survey-based data indicates that the framework provides a reliable estimation of lifestyle patterns across diverse urban areas. Additionally, summarized patterns of criteria ordering were created, offering a concise and intuitive representation of lifestyles. The analysis revealed high consistency between the two methods in the derived patterns, underscoring the framework’s robustness and accuracy in modeling urban lifestyle dynamics. Citation: Smart Cities PubDate: 2024-11-02 DOI: 10.3390/smartcities7060128 Issue No:Vol. 7, No. 6 (2024)
Authors:Alessandro Zini, Roberta Roberto, Patrizia Corrias, Bruna Felici, Michel Noussan First page: 3334 Abstract: The transport sector worldwide relies heavily on oil products, and private cars account for the largest share of passenger mobility in several countries. Public transport could represent an interesting alternative under many perspectives, including a decrease in traffic, pollutants, and climate emissions. However, for public transport to succeed, it should be attractive for final users, representing a viable alternative to private mobility. In this work, we analyse the spatial distribution of public transport service provision within two metropolitan cities, considering the three key dimensions of mobility, competitiveness, and accessibility of public transport. The results show that private car performs better than public transport in all scopes considered, and that performance indicators are highly variable among city areas, indicating inequalities in social and environmental sustainability in urban systems. The outcomes of the analysis provide interesting insights for policy makers and researchers that deal with similar topics, and can also be extended to other cities and countries. Citation: Smart Cities PubDate: 2024-11-02 DOI: 10.3390/smartcities7060129 Issue No:Vol. 7, No. 6 (2024)
Authors:Olga Kolotouchkina, Laura Ripoll González, Warda Belabas First page: 3355 Abstract: While cities on a global scale embrace smartness as a roadmap for efficient urban governance, disparities persist in the domain of digital accessibility, literacy, and skills, with manifestations of digital exclusion, ageism, and ableism prevalent in most digital urban experiences. Despite their commitment to bridging the digital divide, governments lack comprehensive frameworks to inform policymaking and action for inclusion in smart cities. This review paper aims to deepen the understanding of the multifaceted challenges confronting the governance of inclusion in smart cities. Drawing from a review of research encompassing digital inclusion, digital transitions, smart cities, and urban governance, we discuss who is included and excluded in the governance of smart cities; what are the necessary conditions to be met for smart cities to be considered inclusive; and how can smart city governance deliver public value and equal benefits for all. As a novel contribution, this paper outlines a reflective framework to inform citizen inclusion in the governance of smart cities. This framework is meant to act as a starting point in the design of inclusive digital urban policies, and can also be employed to assess the directions taken to date in public organizations towards more inclusive urban practices. Citation: Smart Cities PubDate: 2024-11-04 DOI: 10.3390/smartcities7060130 Issue No:Vol. 7, No. 6 (2024)
Authors:Sachin Kahawala, Nuwan Madhusanka, Daswin De Silva, Evgeny Osipov, Nishan Mills, Milos Manic, Andrew Jennings First page: 3371 Abstract: The United Nations Sustainable Development Goal 11 aims to make cities and human settlements inclusive, safe, resilient and sustainable. Smart cities have been studied extensively as an overarching framework to address the needs of increasing urbanisation and the targets of SDG 11. Digital twins and artificial intelligence are foundational technologies that enable the rapid prototyping, development and deployment of systems and solutions within this overarching framework of smart cities. In this paper, we present a novel AI approach for hypervector approximation of complex manifolds in high-dimensional datasets and data streams such as those encountered in smart city settings. This approach is based on hypervectors, few-shot learning and a learning rule based on single-vector operation that collectively maintain low computational complexity. Starting with high-level clusters generated by the K-means algorithm, the approach interrogates these clusters with the Hyperseed algorithm that approximates the complex manifold into fine-grained local variations that can be tracked for anomalies and temporal changes. The approach is empirically evaluated in the smart city setting of a multi-campus tertiary education institution where diverse sensors, buildings and people movement data streams are collected, analysed and processed for insights and decisions. Citation: Smart Cities PubDate: 2024-11-07 DOI: 10.3390/smartcities7060131 Issue No:Vol. 7, No. 6 (2024)
Authors:Ovanes Petrosian, Yuyi Zhang First page: 3388 Abstract: The application of black-box models, namely ensemble and deep learning, has significantly advanced the effectiveness of solar power generation forecasting. However, these models lack explainability, which hinders comprehensive investigations into environmental influences. To address this limitation, we employ explainable artificial intelligence (XAI) techniques to enhance the interpretability of these black-box models, while ensuring their predictive accuracy. We carefully selected 10 prominent black-box models and deployed them using real solar power datasets. Within the field of artificial intelligence, it is crucial to adhere to standardized usage procedures to guarantee unbiased performance evaluations. Consequently, our investigation identifies LightGBM as the model that requires explanation. In a practical engineering context, we utilize XAI methods to extract understandable insights from the selected model, shedding light on the varying degrees of impact exerted by diverse environmental factors on solar power generation. This approach facilitates a nuanced analysis of the influence of the environment. Our findings underscore the significance of “Distance from the Noon” as the primary factor influencing solar power generation, which exhibits a clear interaction with “Sky Cover.” By leveraging the outcomes of our analyses, we propose optimal locations for solar power stations, thereby offering a tangible pathway for the practical. Citation: Smart Cities PubDate: 2024-11-07 DOI: 10.3390/smartcities7060132 Issue No:Vol. 7, No. 6 (2024)
Authors:Mohd Hafizuddin Bin Kamilin, Shingo Yamaguchi, Mohd Anuaruddin Bin Ahmadon First page: 3412 Abstract: In a real-world implementation, machine learning models frequently experience concept drift when forecasting the electricity load. This is due to seasonal changes influencing the scale, mean, and median values found in the input data, changing their distribution. Several methods have been proposed to solve this, such as implementing automated model retraining, feature engineering, and ensemble learning. The biggest drawback, however, is that they are too complex for simple implementation in existing projects. Since the drifted data follow the same pattern as the training dataset in terms of having different scale, mean, and median values, radian scaling was proposed as a new way to scale without relying on these values. It works by converting the difference between the two sequential values into a radian for the model to compute, removing the bounding, and allowing the model to forecast beyond the training dataset scale. In the experiment, not only does the constrained gated recurrent unit model with radian scaling have shorter average training epochs, but it also lowers the average root mean square error from 158.63 to 43.375, outperforming the best existing normalization method by 72.657%. Citation: Smart Cities PubDate: 2024-11-08 DOI: 10.3390/smartcities7060133 Issue No:Vol. 7, No. 6 (2024)
Authors:Özlem Gürel, Seyda Serdarasan First page: 3437 Abstract: As cities expand and the global push for zero pollution intensifies, sustainable last-mile delivery (LMD) systems are essential to minimizing environmental and health impacts. This study addresses the need for more sustainable LMD by examining the integration of wind conditions into drone-assisted deliveries, focusing on their effects on air and noise pollution in urban areas. We extend the flying sidekick traveling salesman problem (FSTSP) by incorporating meteorological factors, specifically wind, to assess drone delivery efficiency in varying conditions. Our results show that while drones significantly reduce greenhouse gas emissions compared to traditional delivery vehicles, their contribution to noise pollution remains a concern. This research highlights the environmental advantages of using drones, particularly in reducing CO2 emissions, while also emphasizing the need for further investigation into mitigating their noise impact. By evaluating the trade-offs between air and noise pollution, this study provides insights into developing more sustainable, health-conscious delivery models that contribute to smart city initiatives. The findings inform policy, urban planning, and logistics strategies aimed at achieving zero pollution goals and improving urban livability. Citation: Smart Cities PubDate: 2024-11-10 DOI: 10.3390/smartcities7060134 Issue No:Vol. 7, No. 6 (2024)
Authors:Lapyote Prasittisopin First page: 3458 Abstract: This paper presents a comprehensive review of the transformative impacts of 3D printing technology on smart cities. As cities face rapid urbanization, resource shortages, and environmental degradation, innovative solutions such as additive manufacturing (AM) offer potential pathways for sustainable urban development. By synthesizing 66 publications from 2015 to 2024, the study examines how 3D printing improves urban infrastructure, enhances sustainability, and fosters community engagement in city planning. Key benefits of 3D printing include reducing construction time and material waste, lowering costs, and enabling the creation of scalable, affordable housing solutions. The paper also addresses emerging areas such as the integration of 3D printing with digital twins (DTs), machine learning (ML), and AI to optimize urban infrastructure and predictive maintenance. It highlights the use of smart materials and soft robotics for structural health monitoring (SHM) and repairs. Despite the promising advancements, challenges remain in terms of cost, scalability, and the need for interdisciplinary collaboration among engineers, designers, urban planners, and policymakers. The findings suggest a roadmap for future research and practical applications of 3D printing in smart cities, contributing to the ongoing discourse on sustainable and technologically advanced urban development. Citation: Smart Cities PubDate: 2024-11-12 DOI: 10.3390/smartcities7060135 Issue No:Vol. 7, No. 6 (2024)
Authors:Shalau Farhad Hussein, Sajjad Golshannavaz, Zhiyi Li First page: 3489 Abstract: This paper presents a model for transactive energy management within microgrids (MGs) that include smart homes and buildings. The model focuses on peer-to-peer (P2P) transactive energy management among these homes, establishing a collaborative use of a cloud energy storage system (CESS) to reduce daily energy costs for both smart homes and MGs. This research assesses how smart homes and buildings can effectively utilize CESS while implementing P2P transactive energy management. Additionally, it explores the potential of a solar rooftop parking lot facility that offers charging and discharging services for plug-in electric vehicles (PEVs) within the MG. Controllable and non-controllable appliances, along with air conditioning (AC) systems, are managed by a home energy management (HEM) system to optimize energy interactions within daily scheduling. A linear mathematical framework is developed across three scenarios and solved using General Algebraic Modeling System (GAMS 24.1.2) software for optimization. The developed model investigates the operational impacts and optimization opportunities of CESS within smart homes and MGs. It also develops a transactive energy framework in a P2P energy trading market embedded with CESS and analyzes the cost-effectiveness and arbitrage driven by CESS integration. The results of the comparative analysis reveal that integrating CESS within the P2P transactive framework not only opens up further technical opportunities but also significantly reduces MG energy costs from $55.01 to $48.64, achieving an 11.57% improvement. Results are further discussed. Citation: Smart Cities PubDate: 2024-11-18 DOI: 10.3390/smartcities7060136 Issue No:Vol. 7, No. 6 (2024)
Authors:Furkan Luleci, Alican Sevim, Eren Erman Ozguven, F. Necati Catbas First page: 3511 Abstract: This paper presents COWINE (Community Twin Ecosystem), an ecosystem that harnesses Digital Twin (DT) to elevate and transform community resilience strategies. COWINE aims to enhance the disaster resilience of communities by fostering collaborative participation in the use of its DT among the decision-makers, the general public, and other involved stakeholders. COWINE leverages Cities:Skylines as its base simulation engine integrated with real-world data for community DT development. It is capable of capturing the dynamic, intricate, and interconnected structures of communities to provide actionable insights into disaster resilience planning. Through demonstrative, simulation-based case studies on Brevard County, Florida, the paper illustrates COWINE’s collaborative use with the involved parties in managing tornado scenarios. This study demonstrates how COWINE supports the identification of vulnerable areas, the execution of adaptive strategies, and the efficient allocation of resources before, during, and after a disaster. This paper further explores potential research directions using COWINE. The findings show COWINE’s potential to be utilized as a collaborative tool for community disaster resilience management. Citation: Smart Cities PubDate: 2024-11-20 DOI: 10.3390/smartcities7060137 Issue No:Vol. 7, No. 6 (2024)
Authors:Thiti Chanchayanon, Susit Chaiprakaikeow, Apiniti Jotisankasa, Shinya Inazumi First page: 3547 Abstract: This review examines the integration of ground source heat pump (GSHP) systems with energy piles as a sustainable approach to improving energy efficiency in smart cities. Energy piles, which combine structural support with geothermal heat exchange, offer significant advantages over conventional air source heat pumps (ASHPs) by using stable ground temperatures for more efficient heating and cooling. System efficiency can be improved by integrating hybrid systems, cooling towers, and solar thermal systems. While the initial investment for GSHP systems is higher, their integration with energy piles significantly reduces electricity consumption and operating costs, providing a compelling solution for regions with high energy demand and escalating energy prices. Government financial incentives, including subsidies, loans, and tax rebates, can reduce payback periods to less than 10 years, encouraging the adoption of energy piles and GSHP systems. The paper analyzes heat transfer mechanisms in energy piles, particularly the role of groundwater circulation in improving heat dissipation and overall system performance. It also discusses optimized design considerations, performance metrics, and economics, highlighting the critical role of site-specific conditions from thorough site surveys and strategic planning of adaptive management to adjust system operations based on real-time demand in optimizing the benefits of geothermal energy systems. This review serves as a comprehensive guide for engineers and researchers in the effective application of energy piles within urban infrastructure, thereby supporting sustainable urban development and mitigating the urban heat island effect. Citation: Smart Cities PubDate: 2024-11-25 DOI: 10.3390/smartcities7060138 Issue No:Vol. 7, No. 6 (2024)
Authors:Sina Parhoudeh, Pablo Eguía López, Abdollah Kavousi Fard First page: 3587 Abstract: An Energy Hub (EH) is able to manage several types of energy at the same time by aggregating resources, storage devices, and responsive loads. Therefore, it is expected that energy efficiency is high. Hence, the optimal operation for smart EHs in energy (gas, electrical, and thermal) networks is discussed in this study based on their contribution to reactive power, the energy market, and day-ahead reservations. This scheme is presented in a smart bi-level optimization. In the upper level, the equations of linearized optimal power flow are used to minimize energy losses in the presented energy networks. The lower level considers the maximization of profits of smart EHs in the mentioned markets; it is based on the EH operational model of resource, responsive load, and storage devices, as well as the formulation of the reserve and flexible constraints. This paper uses the “Karush–Kuhn–Tucker” method for single-level model extraction. An “unscented transformation technique” is then applied in order to model the uncertainties associated with energy price, renewable energy, load, and energy consumed in mobile storage. The participation of hubs in the mentioned markets to improve their economic status and the technical status of the networks, modeling of the flexibility of the hubs, and using the unscented transformation method to model uncertainties are the innovations of this article. Finally, the extracted numerical results indicate the proposed model’s potential to improve EHs’ economic and flexibility status and the energy network’s performance compared to their load flow studies. As a result, energy loss, voltage, and temperature drop as operation indices are improved by 14.5%, 48.2%, and 46.2% compared to the load flow studies, in the case of 100% EH flexibility and their optimal economic situation extraction. Citation: Smart Cities PubDate: 2024-11-25 DOI: 10.3390/smartcities7060139 Issue No:Vol. 7, No. 6 (2024)
Authors:Josip Lorincz, Zvonimir Klarin First page: 3616 Abstract: The global deployment of fifth-generation (5G) mobile networks, especially in urban cities, is dedicated to accommodating the demand for high data rates and reliable wireless communications. While the latest 5G networks improve service quality, the support for a simultaneous serving of more user devices (UDs) with higher data rates than previous mobile network generations will require a massive installation of different 5G base station (BS) types dominantly in urban cities. Besides contributing to the smart city service improvements, this massive installation of heterogeneous 5G BSs will also contribute to the increase in 5G network energy consumption (EC) and carbon dioxide emissions. Since this increase in installed 5G BSs imposes environmental and economic challenges, this paper analyzes the impact of the continuously rising number of 5G UDs on the energy efficiency (EE) of the radio part of Croatian and Dutch 5G networks as example cases in the period of 2020s. Analyses consider the countries’ rural, suburban, urban, and dense urban UD density areas by utilizing the proposed simulation framework for the EE evaluation of 5G heterogeneous networks (HetNet) valued through standardized mobile networks EE metrics. The study examines four proposed BS installation and operation scenarios for reducing energy costs of 5G networks that differ in optimizing energy consumption via different BS installations, sleep modes, and transmission power scaling techniques. The obtained results indicate that dynamic adaptation of BS deployments and radio resource management during operation according to the increase in the number of UDs and corresponding DVs can enhance 5G HetNet EE. The findings provide valuable insights for mobile network operators looking to optimize 5G network EE in the upcoming decade. Citation: Smart Cities PubDate: 2024-11-28 DOI: 10.3390/smartcities7060140 Issue No:Vol. 7, No. 6 (2024)
Authors:Yuhan Tang, Ao Qu, Xuan Jiang, Baichuan Mo, Shangqing Cao, Joseph Rodriguez, Haris N Koutsopoulos, Cathy Wu, Jinhua Zhao First page: 3658 Abstract: Public transit systems are critical to the quality of urban life, and enhancing their efficiency is essential for building cost-effective and sustainable smart cities. Historically, researchers sought reinforcement learning (RL) applications to mitigate bus bunching issues with holding strategies. Nonetheless, these attempts often led to oversimplifications and misalignment with the goal of reducing the total time passengers spent in the system, resulting in less robust or non-optimal solutions. In this study, we introduce a novel setting where each bus, supervised by an RL agent, can appropriately form aggregated policies from three strategies (holding, skipping station, and turning around to serve the opposite direction). It’s difficult to learn them all together, due to learning complexity, we employ domain knowledge and develop a gradually expanding action space curriculum, enabling agents to learn these strategies incrementally. We incorporate Long Short-Term Memory (LSTM) in our model considering the temporal interrelation among these actions. To address the inherent uncertainties of real-world traffic systems, we impose Domain Randomization (DR) on variables such as passenger demand and bus schedules. We conduct extensive numerical experiments with the integration of synthetic and real-world data to evaluate our model. Our methodology proves effective, enhancing bus schedule reliability and reducing total passenger waiting time by over 15%, thereby improving bus operation efficiency and smoothering operations of buses that align with sustainable goals. This work highlights the potential of robust RL combined with curriculum learning for optimizing public transport in smart cities, offering a scalable solution for real-world multi-agent systems. Citation: Smart Cities PubDate: 2024-11-29 DOI: 10.3390/smartcities7060141 Issue No:Vol. 7, No. 6 (2024)
Authors:Mohammad Aldossary First page: 3678 Abstract: Rapid technology advances have made managing charging loads and optimizing routes for electric vehicle (EV) fleets, especially in cities, increasingly important. IoT sensors in EV charging stations and cars enhance prediction and optimization algorithms with real-time data on charging behaviors, traffic, vehicle locations, and environmental factors. These IoT data enable the GNN-ViGNet hybrid deep learning model to anticipate electric vehicle charging needs. Data from 400,000 IoT sensors at charging stations and vehicles in Texas were analyzed to identify EV charging patterns. These IoT sensors capture crucial parameters, including charging habits, traffic conditions, and other environmental elements. Frequency-Aware Dynamic Range Scaling and advanced preparation methods, such as Categorical Encoding, were employed to improve data quality. The GNN-ViGNet model achieved 98.9% accuracy. The Forecast Accuracy Rate (FAR) and Charging Load Variation Index (CLVI) were introduced alongside Root-Mean-Square Error (RMSE) and Mean Square Error (MSE) to assess the model’s predictive power further. This study presents a prediction model and a hybrid Coati–Northern Goshawk Optimization (Coati–NGO) route optimization method. Routes can be real-time adjusted using IoT data, including traffic, vehicle locations, and battery life. The suggested Coati–NGO approach combines the exploratory capabilities of Coati Optimization (COA) with the benefits of Northern Goshawk Optimization (NGO). It was more efficient than Particle Swarm Optimization (919 km) and the Firefly Algorithm (914 km), reducing the journey distance to 511 km. The hybrid strategy converged more quickly and reached optimal results in 100 rounds. This comprehensive EV fleet management solution enhances charging infrastructure efficiency, reduces operational costs, and improves fleet performance using real-time IoT data, offering a scalable and practical solution for urban EV transportation. Citation: Smart Cities PubDate: 2024-12-02 DOI: 10.3390/smartcities7060142 Issue No:Vol. 7, No. 6 (2024)
Authors:Antreas Kantaros, Florian Ion Tiberiu Petrescu, Konstantinos Brachos, Theodore Ganetsos, Nicolae Petrescu First page: 3705 Abstract: This work explores the transformative impact of 3D printing technology and disaster management within the context of smart cities. By evaluating various 3D printing technologies, such as desktop and large-scale printers, this research highlights their application in rapidly producing customized structures and essential supplies infrastructure components. Methods included the review of existing technologies, practical application in disasters scenarios. and the analysis of community engagement programs that enhance local preparedness and resilience through 3D printing. Case studies illustrate the significant benefits of integrating 3D printing technologies in disaster management. Findings indicate that while 3D printing offers rapid production and efficiency, disabilities such as high initial cost, regulatory issues, and the need for skilled operators must be addressed. This study concludes that with strategic collaboration and investment in the education and regulatory frameworks, 3D printing can significantly enhance urban resilience and sustainability, making it an invaluable tool for future smart cities. This research underscores the potential of 3D printing to significantly aid disaster management practices, fostering more adaptive and efficient urban environments. Citation: Smart Cities PubDate: 2024-12-02 DOI: 10.3390/smartcities7060143 Issue No:Vol. 7, No. 6 (2024)
Authors:Amir Rafati, Hamid Mirshekali, Hamid Reza Shaker, Navid Bayati First page: 3727 Abstract: The rapid growth of electrical energy demands raises the need for the modernization of distribution grids. Medium-voltage (MV) aged cables are infrastructures facing significant challenges that can compromise the security of supply and reduce the reliability of power grids. To address the challenges, there is a growing interest in optimizing cable replacement and management strategies. This comprehensive review focuses on the technical challenges and innovations associated with MV cable replacement, highlighting defect detection, lifetime estimation, reliability assessment, and management strategies. Various methods for detecting and monitoring cable defects and discussing their advantages and limitations are surveyed. Moreover, different models and techniques for estimating the remaining useful life of MV cables are explored, emphasizing the importance of accurate predictions for assessing cable reliability and optimizing replacement schedules. Furthermore, emerging technologies that enhance cable management strategies are also highlighted. This review provides insights and recommendations for future research and development, paving the way for the sustainable evolution of power grids. Citation: Smart Cities PubDate: 2024-12-03 DOI: 10.3390/smartcities7060144 Issue No:Vol. 7, No. 6 (2024)
Authors:Hossein Jokar, Taher Niknam, Moslem Dehghani, Pierluigi Siano, Khmaies Ouahada, Mokhtar Aly First page: 3764 Abstract: This study introduces an advanced Mixed-Integer Linear Programming model tailored for comprehensive electrical and thermal energy management in small-scale smart grids, addressing emergency load shedding and overload situations. The model integrates combined heat and power sources, capable of simultaneous electricity and heat generation, alongside a mobile photovoltaic battery storage system, a wind resource, a thermal storage tank, and demand response programs (DRPs) for both electrical and thermal demands. Power-to-hydrogen systems are also incorporated to efficiently convert electrical energy into heat, enhancing network synergies. Utilizing the robust Gurobi solver, the model aims to minimize operating, fuel, and maintenance costs while mitigating environmental impact. Simulation results under various scenarios demonstrate the model’s superior performance. Compared to conventional evolutionary methods like particle swarm optimization, non-dominated sorting genetic algorithm III, and biogeography-based optimization, the proposed model exhibits remarkable improvements, outperforming them by 11.4%, 5.6%, and 11.6%, respectively. This study emphasizes the advantages of employing DRP and heat tank equations to balance electrical and thermal energy relationships, reduce heat losses, and enable the integration of larger photovoltaic systems to meet thermal constraints, thus broadening the problem’s feasible solution space. Citation: Smart Cities PubDate: 2024-12-03 DOI: 10.3390/smartcities7060145 Issue No:Vol. 7, No. 6 (2024)
Authors:Edisson Villa-Ávila, Paul Arévalo, Danny Ochoa-Correa, Michael Villa-Ávila, Emilia Sempértegui-Moscoso, Francisco Jurado First page: 3798 Abstract: As the world increasingly embraces renewable energy as a sustainable power source, accurately assessing of solar energy potential becomes paramount. Photovoltaic (PV) systems, especially those integrated into urban rooftops, offer a promising solution to address the challenges posed by aging energy grids and rising fossil fuel prices. However, optimizing the placement of PV panels on rooftops remains a complex task due to factors like building shape, location, and the surrounding environment. This study introduces the Roof-Solar-Max methodology, which aims to maximize the placement of PV panels on urban rooftops while avoiding shading and panel overlap. Leveraging geographic information systems technology and 3D models, this methodology provides precise estimates of PV generation potential. Key contributions of this research include a roof categorization model, identification of PV-ready rooftops, optimal spatial distribution of PV panels, and innovative evaluation technology. Practical implementation in a real urban setting demonstrates the methodology’s utility for decision making in the planning and development of solar energy systems in urban areas. The main findings highlight substantial potential for PV energy generation in the studied urban area, with capacities reaching up to 444.44 kW. Furthermore, implementing PV systems on residential rooftops has proven to be an effective strategy for reducing CO2 emissions and addressing climate change, contributing to a cleaner and more sustainable energy mix in urban environments. Citation: Smart Cities PubDate: 2024-12-04 DOI: 10.3390/smartcities7060146 Issue No:Vol. 7, No. 6 (2024)
Authors:Nuwani Kangana, Nayomi Kankanamge, Chathura De Silva, Ashantha Goonetilleke, Rifat Mahamood, Daneesha Ranasinghe First page: 3823 Abstract: Urbanization presents significant challenges to disaster management as cities grow and develop, hence increasing their vulnerability to disasters. Disaster resilience is crucial for protecting lives and infrastructure, ensuring economic stability, promoting equality and cohesion, and ensuring the long-term viability of metropolitan regions in these rapidly growing cities. This paper investigates contemporary approaches to creating smart and resilient urban environments through disaster management that emphasize community-based solutions in prioritizing advanced technologies. The key findings of the research include three factors to be accomplished in utilizing technology in community-based disaster management, trust in the crowd, digital divide, and cultural sensitivity. Moreover, the review highlights the significance of the use of smart technologies in improving urban resilience, including but not limited to real-time data-sharing platforms and ML algorithms. Furthermore, it emphasizes the challenges regarding reliability and accuracy in crowdsourced information, stressing the importance of user awareness. Citation: Smart Cities PubDate: 2024-12-05 DOI: 10.3390/smartcities7060147 Issue No:Vol. 7, No. 6 (2024)
Authors:Rudolf Francesco Paternost, Riccardo Mandrioli, Vincenzo Cirimele, Mattia Ricco, Gabriele Grandi First page: 3853 Abstract: Catenary-powered networks are expected to play a pivotal role in urban energy transition, due to the larger deployment of electric public transport, in-motion-charging (IMC) vehicles, and catenary-backed electric vehicle chargers. However, there are technical challenges that must be overcome to ensure the successful utilization of existing networks without compromising vehicle performance or compliance with network standards. This paper aims to validate the use of battery energy storage systems (BESS) built from second-life batteries as a means of retrofitting catenary-powered traction networks. The objective is to increase the network robustness without creating a negative impact on its overall operational efficiency. Consequently, more electrification projects can be implemented using the same network infrastructure without substantial modifications. Furthermore, a power management scheme is presented which allows the voltage and current range allowed in the catenary network and the BESS maximum charging rate to be controlled from user-defined values. The proposed control scheme is adept at customizing the BESS size for the specific application under consideration. Validation is performed on a case study of the trolleybus system in Bologna, Italy. Citation: Smart Cities PubDate: 2024-12-07 DOI: 10.3390/smartcities7060148 Issue No:Vol. 7, No. 6 (2024)
Authors:Aliaa A. Okasha, Diaa-Eldin A. Mansour, Ahmed B. Zaky, Junya Suehiro, Tamer F. Megahed First page: 3871 Abstract: Intentional controlled islanding (ICI) is a crucial strategy to avert power system collapse and blackouts caused by severe disturbances. This paper introduces an innovative IoT-based ICI strategy that identifies the optimal location for system segmentation during emergencies. Initially, the algorithm transmits essential data from phasor measurement units (PMUs) to the IoT cloud. Subsequently, it calculates the coherency index among all pairs of generators. Leveraging IoT technology increases system accessibility, enabling the real-time detection of changes in network topology post-disturbance and allowing the coherency index to adapt accordingly. A novel algorithm is then employed to group coherent generators based on relative coherency index values, eliminating the need to transfer data points elsewhere. The “where to island” subproblem is formulated as a mixed integer linear programming (MILP) model that aims to boost system transient stability by minimizing power flow interruptions in disconnected lines. The model incorporates constraints on generators’ coherency, island connectivity, and node exclusivity. The subsequent layer determines the optimal generation/load actions for each island to prevent system collapse post-separation. Signals from the IoT cloud are relayed to the circuit breakers at the terminals of the optimal cut-set to establish stable isolated islands. Additionally, controllable loads and generation controllers receive signals from the cloud to execute load and/or generation adjustments. The proposed system’s performance is assessed on the IEEE 39-bus system through time-domain simulations on DIgSILENT PowerFactory connected to the ThingSpeak cloud platform. The simulation results demonstrate the effectiveness of the proposed ICI strategy in boosting power system stability. Citation: Smart Cities PubDate: 2024-12-10 DOI: 10.3390/smartcities7060149 Issue No:Vol. 7, No. 6 (2024)
Authors:Mfonobong Uko, Sunday C. Ekpo, Sunday Enahoro, Fanuel Elias First page: 3895 Abstract: The convergence of 5G terrestrial networks with satellite systems offers a revolutionary approach to achieving global, seamless connectivity, particularly for Internet of Things (IoT) applications in urban and rural settings. This paper investigates the implications of this 5G–satellite integrated network architecture, specifically through the application of the two-ray propagation model and the free-space path loss (FSPL) model. By simulating signal characteristics over varying distances, altitudes, and environmental parameters, we explore how factors such as transmitter height, satellite altitude, and frequency impact received power, path loss, channel capacity, and outage probability. The key findings indicate that received power decreases significantly with increasing distance, with notable oscillations in the two-ray model due to interference from ground reflections, particularly evident within the first 100 km. For example, at 50 km, a 300 km satellite altitude yields approximately −115 dBm in received power, while at 1000 km altitude, this power drops to around −136 dBm. Higher frequencies (e.g., 32 GHz) exhibit greater path loss than lower frequencies (e.g., 24 GHz), with a 5 dB difference observed at 1000 km, reinforcing the need for frequency considerations in long-range communication design. In terms of channel capacity, increasing bandwidth enhances achievable data rates but declines with distance due to diminishing received power. At 100 km, a 50 MHz bandwidth supports up to 4500 Mbps, while at 3000 km, capacity drops to around 300 Mbps. The outage probability analysis shows that higher signal-to-noise ratio (SNR) thresholds substantially increase the likelihood of communication failures, especially at distances exceeding 2000 km. For instance, at 3000 km, the outage probability for a 15 dB SNR threshold reaches approximately 25%, compared to less than 5% for a 5 dB threshold. These results underscore the critical trade-offs in designing 5G–satellite IoT networks, balancing bandwidth, frequency, SNR thresholds, and satellite altitudes for optimal performance across diverse IoT applications. The analysis provides valuable insights for enhancing connectivity and reliability in 5G–satellite integrated networks, especially in remote and underserved regions. Citation: Smart Cities PubDate: 2024-12-11 DOI: 10.3390/smartcities7060150 Issue No:Vol. 7, No. 6 (2024)
Authors:Andres Udal, Raivo Sell, Krister Kalda, Dago Antov First page: 3914 Abstract: An important development task for the suburbs of smart cities is the transition from rigid and economically inefficient public transport to the flexible order-based service with autonomous vehicles. The article proposes a compact model with a minimal input data set to estimate the effective daily travel time (EDTT) of an average resident of a suburban area considering the availability of the first-mile autonomous vehicles (AVs). Our example case is the Järveküla residential area beyond the Tallinn city border. In the model, the transport times of the whole day are estimated on the basis of the forenoon outbound trips. The one-dimensional distance-based spatial model with 5 residential origin zones and 6 destination districts in the city is applied. A crucial simplification is the 3-parameter sub-model of the distribution of distances on the basis of the real mobility statistics. Effective travel times, optionally completed with psycho-physiological stress factors and psychologically perceived financial costs, are calculated for all distances and transportation modes using the characteristic speeds of each mode of transport. A sub-model of switching from 5 traditional transport modes to two AV-assisted modes is defined by an aggregated AV acceptance parameter ‘a’ based on resident surveys. The main output of the model is the EDTT, dependent on the value of the parameter a. Thanks to the compact and easily adjustable set of input data, the main values of the presented model are its generalizability, predictive ability, and transferability to other similar suburban use cases. Citation: Smart Cities PubDate: 2024-12-11 DOI: 10.3390/smartcities7060151 Issue No:Vol. 7, No. 6 (2024)
Authors:Maryam Fayyaz, Gaetano Fusco, Chiara Colombaroni, Esther González-González, Soledad Nogués First page: 3936 Abstract: Encouraging older and newer mobility alternatives to standard privately owned cars, such as cycling and autonomous vehicles, is necessary to reduce pollution, enhance safety, increase transportation efficiency, and create a more sustainable urban environment. Implementing mobility plans that identify the use of different transport modes in their confidence intervals can lead to the development of smarter and more efficient cities, where all citizens can benefit from safe and environmentally friendly streets. This research aims to provide insights into designing urban streets that seamlessly integrate autonomous vehicles and cyclists, promoting sustainable mobility while ensuring urban transport efficiency. With this aim, the research identifies and prioritizes the factors that are relevant to street design as well as the appropriate strategies to address them. Our methodology combines Multi-Criteria Decision-Making (MCDM) with Game theory to identify and realize the most convenient conditions for this integration. Initially, the basic factors were identified using the value-interval fuzzy Delphi method. Following this, the factors were weighted with the interval-fuzzy Analytic Network Process (ANP), and the cause-and-effect variables were evaluated using the interval-fuzzy Decision-Making Trial and Evaluation Laboratory ANP (DANP). Finally, Game theory was employed to determine the optimal model for addressing these challenges. The results indicate that safety emerged as the most significant factor and two optimal strategies were identified; the integration of green infrastructure and smart technology. Citation: Smart Cities PubDate: 2024-12-12 DOI: 10.3390/smartcities7060152 Issue No:Vol. 7, No. 6 (2024)
Authors:Matej Cenky, Jozef Bendik, Peter Janiga, Illia Lazarenko First page: 3962 Abstract: This paper aims to effectively estimate urban-scale rooftop photovoltaic potential using strictly open-source software and publicly available GIS data. This approach is often neglected; however, its importance is significant regarding technology transfer and general commercial or academic ease of use. A complete methodology is introduced, including the building shadow analysis. Although many papers are published in similar areas, very few reveal the specific steps and functions in the software used, or the computational core of some part of the estimation is a “black box” of a commercial service. Detailed irradiation parameters can be obtained using the proposed methodologies, and the maximum photovoltaic (PV) power output in the area can be estimated. The great advantage of this model is its scalability and the easy way of modifying every computational parameter. The results and limitations of the proposed methodology are discussed, and further development is suggested. The presented model is based on a sample location in Bratislava, Slovakia, with an area of circa 2.5 km2. Citation: Smart Cities PubDate: 2024-12-12 DOI: 10.3390/smartcities7060153 Issue No:Vol. 7, No. 6 (2024)
Authors:Juan David Parra Rodriguez, Kwasi Boakye-Boateng, Ratinder Kaur, Allyson Zhou, Rongxing Lu, Ali A. Ghorbani First page: 3983 Abstract: OT (operational technology) protocols such as DNP3/TCP, commonly used in the electrical utility sector, have become a focal point for security researchers. We assess the applicability of attacks previously published from theoretical and practical points of view. From the theoretical point of view, previous work strongly focuses on transcribing protocol details (e.g., list fields at the link, transport, and application layer) without providing the rationale behind protocol features or how the features are used. This has led to confusion about the impact of many theoretical DNP3 attacks. After a detailed analysis around which protocol features are used and how, a review of the configuration capabilities for several IEDs (Intelligent Electrical Devices), and some testing with real devices, we conclude that similar results to several complex theoretical attacks can be achieved with considerably less effort. From a more practical point of view, there is existing work on DNP3 man-in-the-middle attacks; however, research still needs to discuss how to overcome a primary hardening effect: IEDs can be configured to allow for communication with specific IP addresses (allow list). For purely scientific purposes, we implemented a DNP3 man-in-the-middle attack capable of overcoming the IP allow-list restriction. We tested the attack using real IEDs and network equipment ruggedized for electrical environments. Even though the man-in-the-middle attack can be successful in a lab environment, we also explain the defense-in-depth mechanisms provided by industry in real life that mitigate the attack. These mechanisms are based on standard specifications, capabilities of the OT hardware, and regulations applicable to some electrical utilities. Citation: Smart Cities PubDate: 2024-12-14 DOI: 10.3390/smartcities7060154 Issue No:Vol. 7, No. 6 (2024)
Authors:Froylán Correa, Miguel Bartorila, Mónica Ribeiro-Palacios, Gerardo I. Pérez-Soto, Juvenal Rodríguez-Reséndiz First page: 4002 Abstract: Active Mobility (AM) currently presents an opportunity to change the paradigm of the competitive and dispersed city created by motorized mobility, revaluing the role of walking and cycling in generating more sustainable urban ecosystems. This article addresses the challenges and opportunities for AM to contribute to the regeneration of urban systems and the capacity for anticipation. This article analyzes AM using the Ecosystemic Urbanism (EU) as an analysis framework within its four axes: social cohesion, complexity, efficiency, and compactness and functionality. Through this analysis, the points of incidence of AM were identified within each of these axes. The study highlights the potential of AM to act as a transformative driver in urban development, integrating an ecological framework where urban systems are interconnected and mutually reinforced. This perspective reveals walking and cycling as a catalyst for reshaping urban interactions. In light of this, future cities must adopt a human urban scale through compactness that fosters complexity and diverse and engaging urban interactions. In addition, the enjoyability achieved through AM brings significant ecosystem benefits by promoting awareness of others, nature, and the interconnectedness between the individual and the city. This represents a new paradigm shift in which the automobile does not play the central role, allowing more sustainable ways of living together. Citation: Smart Cities PubDate: 2024-12-16 DOI: 10.3390/smartcities7060155 Issue No:Vol. 7, No. 6 (2024)
Authors:Wiem Fekih Hassen, Luis Schoppik, Sascha Schiegg, Armin Gerl First page: 4025 Abstract: The applicability of Hybrid Energy Storage Systems (HESSs) has been shown in multiple application fields, such as Charging Stations (CSs), grid services, and microgrids. HESSs consist of an integration of two or more single Energy Storage Systems (ESSs) to combine the benefits of each ESS and improve the overall system performance. In this work, we propose a novel power management controller called the Hybrid Controller for the efficient HESS’s charging and discharging, considering the State of Charge (SoC) of the HESS and the dynamic supply and load. The Hybrid Controller optimises the use of the HESS, i.e., minimises the amount of energy drawn from and discharged to the grid, thus utilising and prioritising the provided Photovoltaic (PV) power. The performance of our proposal was assessed via simulation using various evaluation metrics, i.e., Autarky, charge/discharge cycle, and Self-Consumption (SC), where we defined 24 scenarios in different locations in Germany. Citation: Smart Cities PubDate: 2024-12-23 DOI: 10.3390/smartcities7060156 Issue No:Vol. 7, No. 6 (2024)
Authors:Chetan Aggarwal, Sudhakar Molleti, Mehdi Ghobadi First page: 2781 Abstract: The building sector is crucial in keeping the environment healthy, mainly because of its energy and material usage. Roofs are one of the most important components to consider, as they not only shield the building from the elements but also have a big impact on the environment. The paper provides a state-of-the-art review of the life cycle assessment (LCA) application in the roofing industry. The review examines three main focus areas: (1) LCA of different roofing materials, (2) LCA of roofing systems, and (3) whole-building LCA. Key takeaways from the literature review demonstrate that there is significant variability in LCA methods and impact categories assessed across roofing studies. Only a few studies have explored the complete urban scale in LCA assessments of roofing components. Future research can include utilizing the potential of LCA at urban scales, which can offer a full understanding of the environmental impacts associated with roofing materials in urban settings. Citation: Smart Cities PubDate: 2024-09-29 DOI: 10.3390/smartcities7050108 Issue No:Vol. 7, No. 5 (2024)
Authors:Seyed Salar Sefati, Razvan Craciunescu, Bahman Arasteh, Simona Halunga, Octavian Fratu, Irina Tal First page: 2802 Abstract: Smart cities increasingly rely on the Internet of Things (IoT) to enhance infrastructure and public services. However, many existing IoT frameworks face challenges related to security, privacy, scalability, efficiency, and low latency. This paper introduces the Blockchain and Federated Learning for IoT (BFLIoT) framework as a solution to these issues. In the proposed method, the framework first collects real-time data, such as traffic flow and environmental conditions, then normalizes, encrypts, and securely stores it on a blockchain to ensure tamper-proof data management. In the second phase, the Data Authorization Center (DAC) uses advanced cryptographic techniques to manage secure data access and control through key generation. Additionally, edge computing devices process data locally, reducing the load on central servers, while federated learning enables distributed model training, ensuring data privacy. This approach provides a scalable, secure, efficient, and low-latency solution for IoT applications in smart cities. A comprehensive security proof demonstrates BFLIoT’s resilience against advanced cyber threats, while performance simulations validate its effectiveness, showing significant improvements in throughput, reliability, energy efficiency, and reduced delay for smart city applications. Citation: Smart Cities PubDate: 2024-10-01 DOI: 10.3390/smartcities7050109 Issue No:Vol. 7, No. 5 (2024)
Authors:Marco Rinaldi, Stefano Primatesta, Martin Bugaj, Ján Rostáš, Giorgio Guglieri First page: 2842 Abstract: In an efficient aerial package delivery scenario carried out by multiple Unmanned Aerial Vehicles (UAVs), a task allocation problem has to be formulated and solved in order to select the most suitable assignment for each delivery task. This paper presents the development methodology of an evolutionary-based optimization framework designed to tackle a specific formulation of a Drone Delivery Problem (DDP) with charging hubs. The proposed evolutionary-based optimization framework is based on a double-chromosome task encoding logic. The goal of the algorithm is to find optimal (and feasible) UAV task assignments such that (i) the tasks’ due dates are met, (ii) an energy consumption model is minimized, (iii) re-charge tasks are allocated to ensure service persistency, (iv) risk-aware flyable paths are included in the paradigm. Hard and soft constraints are defined such that the optimizer can also tackle very demanding instances of the DDP, such as tens of package delivery tasks with random temporal deadlines. Simulation results show how the algorithm’s development methodology influences the capability of the UAVs to be assigned to different tasks with different temporal constraints. Monte Carlo simulations corroborate the results for two different realistic scenarios in the city of Turin, Italy. Citation: Smart Cities PubDate: 2024-10-06 DOI: 10.3390/smartcities7050110 Issue No:Vol. 7, No. 5 (2024)
Authors:Hoi-Kin Cheng, Kun-Pang Kou, Ka-Io Wong First page: 2861 Abstract: Public transportation has been identified as a viable solution to mitigate traffic congestion. Transit signal priority (TSP) control, which is widely used at signalized intersections, has been recognized as a practical strategy to improve the efficiency and reliability of bus operations. However, traditional TSP control may fall short of efficiency and is facing several challenges of negative externalities for non-transit users and the need to handle conflicting priority requests. Recent studies have proposed the use of reinforcement learning (RL) methods to identify efficient traffic signal control (TSC). Some of these studies on RL-based TSC have incorporated the concept of max-pressure (MP), which is a maximal weight-matching algorithm to minimize queue sizes. Nevertheless, the existing RL-based TSC methods focus on private vehicles and cannot adequately distinguish between buses and private vehicles. In prior research, RL-based control has been implemented within the context of bus rapid transit (BRT) systems. This study proposes a novel RL-based TSC strategy that leverages the MP concept and extends it to incorporate TSP control. This is the first implementation of RL-based TSP control within the mixed-traffic road network. A significant innovation of this research is the introduction of the priority factor (PF), which is designed to prioritize bus movements at signalized intersections. The proposed RL-based TSP with PF control seeks to balance the competing objectives of enhancing bus operations while mitigating adverse impacts on non-transit users. To evaluate the performance of the proposed TSP method with the PF mechanism, simulations were conducted on an arterial and a grid network under dynamic traffic conditions. The simulation results demonstrated that the proposed TSP with PF not only reduces bus travel times and resolves conflicts between priority requests but also does not make a significant negative impact on passenger car operations. Furthermore, the PF can be dynamically assigned according to the number of passengers on each bus, suggesting the potential for the proposed approach to be applied in various traffic management scenarios. Citation: Smart Cities PubDate: 2024-10-06 DOI: 10.3390/smartcities7050111 Issue No:Vol. 7, No. 5 (2024)
Authors:Konstantinos Markantonakis, Ghada Arfaoui, Sarah Abu Ghazalah, Carlton Shepherd, Raja Naeem Akram, Damien Sauveron First page: 2887 Abstract: The Consumer-Oriented Trusted Service Manager (CO-TSM) model has been recognised as a significant advancement in managing applications on Near Field Communication (NFC)-enabled mobile devices and multi-application smart cards. Traditional Trusted Service Manager (TSM) models, while useful, often result in market fragmentation and limit widespread adoption due to their centralised control mechanisms. The CO-TSM model addresses these issues by decentralising management and offering greater flexibility and scalability, making it more adaptable to the evolving needs of embedded systems, particularly in the context of the Internet of Things (IoT) and Radio Frequency Identification (RFID) technologies. This paper provides a comprehensive analysis of the CO-TSM model, highlighting its application in various technological domains such as smart cards, HCE-based NFC mobile phones, TEE-enabled smart home IoT devices, and RFID-based smart supply chains. By evaluating the CO-TSM model’s architecture, implementation challenges, and practical deployment scenarios, this paper demonstrates how CO-TSM can overcome the limitations of traditional TSM approaches. The case studies presented offer practical insights into the model’s adaptability and effectiveness in real-world scenarios. Through this examination, the paper aims to underscore the CO-TSM model’s role in enhancing scalability, flexibility, and user autonomy in secure embedded device management, while also identifying areas for future research and development. Citation: Smart Cities PubDate: 2024-10-08 DOI: 10.3390/smartcities7050112 Issue No:Vol. 7, No. 5 (2024)
Authors:Yuxin Cong, Shinya Inazumi First page: 2910 Abstract: This paper examines how smart cities can address land subsidence and liquefaction in the context of rapid urbanization in Japan. Since the 1960s, liquefaction has been an important topic in geotechnical engineering, and extensive efforts have been made to evaluate soil resistance to liquefaction. Currently, there is a lack of machine learning applications in smart cities that specifically target geological hazards. This study aims to develop a high-performance prediction model for estimating the depth of the bearing layer, thereby improving the accuracy of geotechnical investigations. The model was developed using actual survey data from 433 points in Setagaya-ku, Tokyo, by applying two machine learning techniques: artificial neural networks (ANNs) and bagging. The results indicate that machine learning offers significant advantages in predicting the depth of the bearing layer. Furthermore, the prediction performance of ensemble learning improved by about 20% compared to ANNs. Both interdisciplinary approaches contribute to risk prediction and mitigation, thereby promoting sustainable urban development and underscoring the potential of future smart cities. Citation: Smart Cities PubDate: 2024-10-08 DOI: 10.3390/smartcities7050113 Issue No:Vol. 7, No. 5 (2024)
Authors:Margherita Pazzini, Leonardo Cameli, Valeria Vignali, Andrea Simone, Claudio Lantieri First page: 2925 Abstract: This study analyses five months of continuous monitoring of different lighting warning systems at a pedestrian crosswalk through video surveillance cameras during nighttime. Three different light signalling systems were installed near a pedestrian crossing to improve the visibility and safety of vulnerable road users: in-curb LED strips, orange flashing beacons, and asymmetric enhanced LED lighting. Seven different lighting configurations of the three systems were studied and compared with standard street lighting. The speed of vehicles for each pedestrian–driver interaction was also evaluated. This was then compared to the speed that vehicles should maintain in order to stop in time and allow pedestrians to cross the road safely. In all of the conditions studied, speeds were lower than those maintained in the five-month presence of standard street lighting (42.96 km/h). The results show that in conditions with dedicated flashing LED lighting, in-curb LED strips, and orange flashing beacons, most drivers (72%) drove at a speed that allowed the vehicle to stop safely compared to standard street lighting (10%). In addition, with this lighting configuration, the majority of vehicles (85%) stopped at pedestrian crossings, while in standard street lighting conditions only 26% of the users stopped to give way to pedestrians. Citation: Smart Cities PubDate: 2024-10-10 DOI: 10.3390/smartcities7050114 Issue No:Vol. 7, No. 5 (2024)
Authors:Yangluxi Li, Huishu Chen, Peijun Yu First page: 2940 Abstract: In the context of increasingly deteriorating global ecological conditions and rising carbon emissions from buildings, campus architecture, as the primary environment for youth learning and living, plays a crucial role in low-carbon energy-efficient design, and green environments. This paper takes the case of Yezhai Middle School in Qianshan, Anhui Province, to explore wind environment optimization and facade energy-saving strategies for mountainous campus buildings under existing building stock renovation. In the context of smart city development, integrating advanced technologies and sustainable practices into public infrastructure has become a key objective. Through wind environment simulations and facade energy retrofitting, this study reveals nonlinear increases in wind speed with building height and significant effects of ground roughness on wind speed variations. Adopting EPS panels and insulation layers in facade energy retrofitting reduces energy consumption for winter heating and summer cooling. The renovated facade effectively prevents cold air intrusion and reduces external heat gain, achieving approximately 24% energy savings. This research provides a scientific basis and practical experience for low-carbon energy retrofitting of other campus and public buildings, advancing the construction industry towards green and low-carbon development goals within the framework of smart city initiatives. Citation: Smart Cities PubDate: 2024-10-11 DOI: 10.3390/smartcities7050115 Issue No:Vol. 7, No. 5 (2024)
Authors:Giancarlo Nota, Gennaro Petraglia First page: 2966 Abstract: Today, historic villages represent a widespread and relevant reality of the Italian administrative structure. To preserve their value for future generations, smart city applications can contribute to implement effective monitoring and decision-making processes devoted to safeguarding their fragile ecosystem. Starting from a situational awareness model, this study proposes a method for designing human-in-the-loop cyber-physical systems that allow the design of monitoring and decision-making applications for historic villages. Both the model and the design method can be used as a reference for the realization of human-in-the-loop cyber-physical systems that consist of human beings, smart objects, edge devices, and cloud components in edge-cloud architectures. The output of the research, consisting of the graphical models for the definition of monitoring architectures and the method for the design of human-in-the-loop cyber-physical systems, was validated in the context of the village of Sant’Agata dei Goti through the implementation of a human-in-the-loop cyber-physical system for monitoring sites aiming at their management, conservation, protection, and fruition. Citation: Smart Cities PubDate: 2024-10-14 DOI: 10.3390/smartcities7050116 Issue No:Vol. 7, No. 5 (2024)
Authors:Roman Schotten, Daniel Bachmann First page: 2995 Abstract: Critical infrastructure (CI) networks face diverse natural hazards, such as flooding. CI network modeling methods are used to evaluate these hazards, enabling the analysis of cascading effects, flood risk, and potential flood risk-reducing measures. However, there is a lack of linkage between analytical methods and potential multisectoral, structural, and nonstructural measures. This deficiency impedes the development of CI network (CIN) models as robust tools for active flood risk management. CI operators have significant expertise in managing and implementing flooding-related measures within their sectors. The objective of this study is to bridge the gap between the application of CIN modeling and the consideration of flood measures in three steps. The first step is conducting a literature review and CI stakeholder interviews in Central Europe on flood measures. The second step is the culmination of the findings in a comprehensive catalog detailing flood measures tailored to five CI sectors, with a generalized category spanning each phase of the disaster risk management cycle. The third step is the validation of the catalog’s utility in a proof-of-concept study along the Vicht River in Western Germany with a model-based flood risk analysis of five flood measures. The application of the flood measure catalog improves the options available for active and residual flood risk management. Additionally, the CI flood risk modeling approach presented here allows for consideration of disruption duration and recovery capability, thus linking the concept of risk and resilience. Citation: Smart Cities PubDate: 2024-10-16 DOI: 10.3390/smartcities7050117 Issue No:Vol. 7, No. 5 (2024)
Authors:Vincenzo Inzillo, David Garompolo, Carlo Giglio First page: 3022 Abstract: The advent of Sixth Generation (6G) wireless technologies introduces challenges and opportunities for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), necessitating a reevaluation of traditional routing protocols. This paper introduces the Multi-Metric Scoring Dynamic Source Routing (MMS-DSR), a novel enhancement of the Dynamic Source Routing (DSR) protocol, designed to meet the demands of 6G-enabled MANETs and the dynamic environments of VANETs. MMS-DSR integrates advanced technologies and methodologies to enhance routing performance in dynamic scenarios. Key among these is the use of a CNN-LSTM-based beamforming algorithm, which optimizes beamforming vectors dynamically, exploiting spatial-temporal variations characteristic of 6G channels. This enables MMS-DSR to adapt beam directions in real time based on evolving network conditions, improving link reliability and throughput. Furthermore, MMS-DSR incorporates a multi-metric scoring mechanism that evaluates routes based on multiple QoS parameters, including latency, bandwidth, and reliability, enhanced by the capabilities of Massive MIMO and the IEEE 802.11ax standard. This ensures route selection is context-aware and adaptive to changing dynamics, making it effective in urban settings where vehicular and mobile nodes coexist. Additionally, the protocol uses machine learning techniques to predict future route performance, enabling proactive adjustments in routing decisions. The integration of dynamic beamforming and machine learning allows MMS-DSR to effectively handle the high mobility and variability of 6G networks, offering a robust solution for future wireless communications, particularly in smart cities. Citation: Smart Cities PubDate: 2024-10-17 DOI: 10.3390/smartcities7050118 Issue No:Vol. 7, No. 5 (2024)
Open Access journal
