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Urban logistics play a pivotal role in smart city development, aiming to improve the efficiency and sustainability of goods delivery in urban environments. As cities face growing challenges related to congestion, traffic management, and environmental impact, there is an increasing need for advanced technologies to optimize urban delivery systems. This paper proposes an innovative framework that integrates artificial intelligence (AI), autonomous vehicles (AVs), and Internet of Things (IoT) technologies to address these challenges. The framework leverages real-time data from IoT-enabled infrastructure to optimize route planning, enhance traffic signal control, and enable predictive demand management for delivery services. By incorporating AI-driven analytics, the proposed approach aims to improve traffic flow, reduce congestion, and minimize the carbon footprint of urban logistics, contributing to the development of more sustainable and efficient smart cities. This work highlights the potential for combining these technologies to transform urban logistics, offering a novel approach to enhancing delivery operations in densely populated areas. Full article

Contemporary urban areas are often characterized by various forms of enclaves, isolated from their surrounding geographical context. Urban green infrastructures provide an opportunity to open these enclaves, establishing physical and functional connections with the broader city, while also contributing to climate change mitigation and adaptation. This study examines the district of San Bartolomeo in the Italian city of Cagliari as an example of an urban enclave, and employs a participatory planning process to design a project that transforms it into a hub open to the wider city community. The result is a neighborhood shaped by its community, where social, economic, and environmental needs are balanced, fostering constant interaction between residents and the city as a whole. Full article

The concept of a smart campus is rapidly gaining traction worldwide, driven by the growth of artificial intelligence (AI) and the Internet of Things (IoT), along with the digital transformation of higher education institutions. While numerous initiatives have been undertaken to enhance the capability of smart campus systems to keep pace with AI advancements, there have been few attempts to develop a cohesive conceptual framework for the smart campus, and to date, there has been limited empirical research conducted to validate the framework. This study bridges this gap by providing the first in-depth assessment of a holistic smart campus conceptual framework. The paper uses a Delphi study approach to validate and consolidate a framework for assessing the robustness of the smart campus assessment framework for application in university settings. The framework consists of four domains, 16 categories, and 48 indicators, comprising a total of 68 items that were validated by experts across the globe. Two rounds of structured questionnaires were conducted to achieve consensus on the framework. The first round involved 34 experts from diverse geographic and professional backgrounds in the smart campus field. The second round included 21 of the earlier participants, which was sufficient to determine consensus. In total, seven of the forty-eight indicators were agreed upon after Round 1, increasing to forty-three after Round 2. The results indicate strong agreement among the experts, affirming the framework’s robustness. This study offers an expert-based, interpretive assessment of the development of the smart campus concept, with a particular focus on validating the smart campus framework. Full article

Entrepreneurial endeavors are essential for stimulating economic growth and rendering them is a primary concern for policymakers. In recent years, smart city ecosystems have garnered attention for enhancing urban living and tackling contemporary difficulties. The contribution of smart cities in promoting entrepreneurship and improving well-being has received little attention. This study aims at examining the potential of smart city as an ecosystem to promote entrepreneurship and enhance well-being and quality of life (QoL). This study uses a Fuzzy evaluation model and the Analytic Hierarchy Process (AHP) to evaluate essential determinants of smart cities and their significance. Data from sources such as the Smart City Index, Ease of Doing Business Ranking, Global Innovation Index, Sustainable Development Report, and Technological Readiness Ranking are utilized with normalization, guaranteeing a dependable evaluation. The findings underscore the significance of open data efforts and transparent governance in recruiting innovative enterprises and promoting entrepreneurship. The study highlights the necessity of cooperative urban planning and public participation in decision-making. Moreover, the authors propose a new definition of smart cities from citizens’ well-being perspective. This research enhances the comprehension of smart cities’ influence on entrepreneurial endeavors, pinpointing problems and prospects for future investigations focused on improving well-being through smart city advancement. Full article

Energy management in smart cities has gained particular significance in the context of climate change and the evolving geopolitical landscape. It has become a key element of sustainable urban development. In this context, energy management plays a central role in facilitating the growth of smart and sustainable cities. The aim of this article is to analyse existing scientific research related to energy in smart cities, identify technological trends, and highlight prospective directions for future studies in this field. The research involves a literature review based on the analysis of articles from the Scopus and Web of Science databases to identify and evaluate studies concerning energy in smart cities. The findings suggest that future research should focus on the development of smart energy grids, energy storage, the integration of renewable energy sources, as well as innovative technologies (e.g., Internet of Things, 5G/6G, artificial intelligence, blockchain, digital twins). This article emphasises the significance of technologies that can enhance energy efficiency in cities, contributing to their sustainable development. The recommended practical and policy directions highlight the development of smart grids as a cornerstone for adaptive energy management and the integration of renewable energy sources, underpinned by regulations encouraging collaboration between operators and consumers. Municipal policies should prioritise the adoption of advanced technologies, such as the IoT, AI, blockchain, digital twins, and energy storage systems, to improve forecasting and resource efficiency. Investments in zero-emission buildings, renewable-powered public transport, and green infrastructure are essential for enhancing energy efficiency and reducing emissions. Furthermore, community engagement and awareness campaigns should form an integral part of promoting sustainable energy practices aligned with broader development objectives. Full article

The growing complexity of urban environments and high-rise facilities presents new challenges for disaster preparedness and response, particularly when managing multiple hazards. Traditional systems that focus on single hazards are insufficient for complex facilities that are prone to cascading disasters. This study develops an open disaster information platform that integrates Building Information Modeling (BIM), Geographic Information Systems (GIS), and real-time monitoring tools to enhance situational awareness and multi-hazard response coordination. The platform combines data from the internet of things’ sensors, structural models, and environmental systems to provide responders and facility managers with real-time access to critical information. Simulation tests and real-world deployments have confirmed the platform’s ability to optimize evacuation routes, improve response times, and minimize risks during emergencies. Integration with GIS further supports risk mapping and post-disaster recovery efforts. This study proposes a scalable disaster management framework that promotes real-time data sharing and collaboration across stakeholders. Aligned with the trend toward smart, resilient cities, the platform provides practical solutions for improving disaster preparedness and response in high-rise and complex urban environments. Full article

This comprehensive state-of-the-art literature review investigates the status of the electric vehicle (EV) market share and the key factors that affect EV adoption with a focus on the shared vision of vehicle electrification and the smart city movement. Investigating the current scenarios of EVs, this study observes a rapid increase in the number of EVs and charging stations in different parts of the world. It reports that people’s socio-economic features (e.g., age, gender, income, education, vehicle ownership, home ownership, and political affiliation) significantly influence EV adoption. Moreover, factors such as high driving range, fuel economy, safety technology, financial incentives, availability of free charging stations, and the capacity of EVs to contribute to decarbonization emerge as key motivators for EV purchases. The literature also indicates that EVs are predominantly used for short-distance travel and users commonly charge their vehicles at home. Most users prefer fast chargers and maintain a high state of charge (SOC) to avoid unforeseen situations. Despite the emergent trend, there is a disparity in charging infrastructure supply compared to the growing demand. Thus, there is a pressing need for more public charging stations to meet the surging charging demand. The integration of smart charging stations equipped with advanced technologies to optimize charging patterns based on energy demand, grid capacity, and people’s demand can help policymakers leverage the smart city movement. This paper makes valuable contributions to the literature by presenting a conceptual framework articulating the factors of EV adoption, outlying their role in achieving smart cities, suggesting policy recommendations to integrate EVs into smart cities, and proposing suggestions for future research directions. Full article

Urban vehicular emissions, a major contributor to environmental degradation, demand accurate methodologies that reflect real-world driving conditions. This study presents a telemetric data-driven framework for assessing emissions of Carbon Monoxide (CO), Hydrocarbons (HCs), and Nitrogen Oxides (NOx) in real-world scenarios. By utilizing Vehicle Specific Power (VSP) calculations, Gaussian Mixture Models (GMMs), and Ensemble Isolation Forests (EIFs), the framework identifies high-risk driving behaviors and maps high-emission zones. Achieving a Silhouette Score of 0.72 for clustering and a precision of 0.88 in anomaly detection, the study provides actionable insights for policymakers to mitigate urban emissions. Spatial–temporal analysis highlights critical high-emission areas, offering strategies for urban planners to reduce environmental impacts. The findings underscore the potential of interventions such as speed regulation and driving behavior modifications in lowering emissions. Future extensions of this work will include hybrid and electric vehicles, alongside the integration of granular environmental factors like weather conditions, to enhance the framework’s accuracy and applicability. By addressing the complexities of real-world emissions, this study contributes to bridging significant knowledge gaps and advancing sustainable urban mobility solutions. Full article

Accurate wind speed and power forecasting are key to optimizing renewable wind station management, which is essential for smart and zero-energy cities. This paper presents a novel integrated wind speed–power forecasting system (WSPFS) that operates across various time horizons, demonstrated through a case study in a high-wind area within the Middle East. The WSPFS leverages 12 AI algorithms both individual and ensemble models to forecast wind speed (WSF) and wind power (WPF) at intervals of 10 min to 36 h. A multi-horizon prediction approach is proposed, using WSF model outputs as inputs for WPF modeling. Predictive accuracy was evaluated using mean absolute percentage error (MAPE) and mean square error (MSE). Additionally, WSPFS advances the smart wind energy deep decarbonization (SWEDD) framework by calculating the carbon city index (CCI) to define the carbon-city transformation curve (CCTC). Findings from this study have broad implications, from enabling zero-energy urban projects and mega-developments like NEOM and the Suez Canal to advancing global energy trading and supply management. Full article

This paper analyses the situated practice of developing a smart city in Limerick, Ireland. It maps out, at a city scale, how the development of the smart city was planned, organised, and governed, as well as ongoing challenges. It addresses two of the principal gaps in the smart city literature, namely, the scarcity of in-depth case studies based on extensive fieldwork and the shortage of studies on smart city development on brownfield sites. Source material was gathered through desk research and interviews with key stakeholders. Limerick adopted an articulated strategic approach to smart city development. The local government’s dedicated smart city unit played a vital role in planning, managing, and implementing smart city operations. The local government did not centralise the smart city development process. Over time, there has been a gradual shift towards the development of Quadruple Helix collaborations and a balance between top-down and bottom-up approaches. The paper also identifies the challenges that might restrain Limerick’s smart city ambitions. These include financial, budgetary, technological, and human resources challenges. It also identifies the challenge of digital exclusion and the need for greater citizen involvement in smart city development. Full article

This study introduces a multi-objective deep reinforcement learning (DRL)-based adaptive transit signal priority control framework designed to enhance safety and efficiency in mixed-autonomy traffic environments. The framework utilizes real-time data from connected and automated vehicles (CAVs) to define states, actions, and rewards, with traffic conflicts serving as the safety reward and vehicle waiting times as the efficiency reward. Transit signal priority strategies are incorporated, assigning weights based on vehicle type and passenger capacity to balance these competing objectives. Simulation modeling, based on a real-world intersection in Changsha, China, evaluated the framework’s performance across multiple CAV penetration rates and weighting configurations. The results revealed that a 5:5 weight ratio for safety and efficiency achieved the best trade-off, minimizing delays and conflicts for all vehicle types. At a 100% CAV penetration rate, delays and conflicts were most balanced, with buses showing an average waiting time of 4.93 s and 0.4 conflicts per vehicle, and CAVs achieving 1.97 s and 0.49 conflicts per vehicle, respectively. In mixed traffic conditions, the framework performed best at a 75% CAV penetration rate, where buses, cars, and CAVs exhibited optimal efficiency and safety. Comparative analysis with fixed-time signal control and other DRL-based methods highlights the framework’s adaptability and robustness, supporting its application in managing mixed traffic and enabling intelligent transportation systems for future smart cities. Full article

Customer satisfaction is not just a significant factor but a cornerstone for smart cities and their organizations that offer services to people. It enhances the organization’s reputation and profitability and drastically raises the chances of returning customers. Unfortunately, customer support service through online chat is often not rated by customers to help improve the service. This study employs artificial intelligence and data augmentation to predict customer satisfaction ratings from conversations by analyzing the responses of customers and service providers. For the study, the authors obtained actual conversations between customers and real agents from the call center database of Jeddah Municipality that were rated by customers on a scale of 1–5. They trained and tested five prediction models with approaches based on logistic regression, random forest, and ensemble-based deep learning, and fine-tuned two pre-trained recent models: ArabicT5 and SaudiBERT. Then, they repeated training and testing models after applying a data augmentation technique using the generative artificial intelligence, GPT-4, to improve the unbalance in customer conversation data. The study found that the ensemble-based deep learning approach best predicts the five-, three-, and two-class classifications. Moreover, data augmentation improved accuracy using the ensemble-based deep learning model with a 1.69% increase and the logistic regression model with a 3.84% increase. This study contributes to the advancement of Arabic opinion mining, as it is the first to report the performance of determining customer satisfaction levels using Arabic conversation data. The implications of this study are significant, as the findings can be applied to improve customer service in various organizations. Full article

In recent years, the rapid development of autonomous vehicles (AVs) has brought new challenges in terms of data security, privacy, and communication integrity. Our research investigates the potential of blockchain technology to improve the security of AVs by securing vehicle communication systems. By integrating blockchain with AI-based predictive algorithms, this approach aims to secure vehicle peer-to-peer communication, reduce traffic congestion, and improve safety for drivers and pedestrians. Blockchain’s decentralized ledger ensures the integrity of data exchange between vehicles and smart city infrastructure and mitigates the risks of cyberattacks such as data manipulation and identity forgery. This paper also examines recent advances in vehicular ad hoc networks (VANETs) and vehicular social networks (VSNs), and it demonstrates how the immutability and cryptographic security of the blockchain can strengthen AV systems. The proposed architecture not only protects user privacy but also decentralizes access to critical data needed for AI-driven decisions, ultimately promoting a safer and more reliable environment for autonomous vehicles. Full article

This research created urban forest management using GIS as the primary instrument to act as a combined technique that allows the locals to participate in the survey. To maintain a sustainable urban green, urban tree management is necessary to reduce complexity and conflict. The initiative used a nature-based solution for tree care depending on species combined with a people-centered smart city approach to better assess tree health in historic urban areas. A total of 4607 records were obtained from the field survey event utilizing a mobile application as a tool. The tree’s basic name, spatial character, position, and potential risk were all gathered during the field survey. As GIS converted the tree’s general or local name into its scientific name, it was able to view and evaluate the data. The findings indicate that trees are most in danger from animals and insects, accounting for 56.39% (2748) of the total risk. Most of them are in areas with poor soil suitability. Through optimized hot-spot analysis mapping, the study recommended that tree care be prioritized. Maps of tree blooming and fruiting indicate the possibility of enhancing the advantages of urban trees in the research region in accordance with their phenological patterns. Full article

Unmanned aerial vehicles (UAVs) have seen widespread adoption across diverse sectors, including agriculture, logistics, surveillance, and disaster management, due to their capabilities for real-time data acquisition and autonomous operations. The integration of UAVs with Internet of Things (IoT) systems further amplifies their functionality, enabling sophisticated applications such as smart city management and environmental monitoring. In this context, blockchain technology plays a pivotal role by providing a decentralized, tamper-resistant ledger that facilitates secure data exchange between UAVs and connected devices. Its transparent and immutable characteristics mitigate the risk of a single point of failure, thereby enhancing data integrity and bolstering trust within UAV–IoT communication networks. However, the interconnected nature of these systems introduces significant security challenges, including unauthorized access, data breaches, and a variety of network-based attacks. These issues are further compounded by the limited computational capabilities of IoT devices and the inherent vulnerabilities of wireless communication channels. Recently, a lightweight mutual authentication scheme using blockchain was presented; however, our analysis identified several critical security flaws in these existing protocols, such as drone impersonation and session key disclosure. To address these vulnerabilities, we propose a secure and lightweight authentication scheme for multi-server UAV–IoT environments. The proposed protocol effectively mitigates emerging security threats while maintaining low computational and communication overhead. We validate the security of our scheme using formal methods, including the Real-Or-Random (RoR) model and BAN logic. Comparative performance evaluations demonstrate that our protocol enhances security while also achieving efficiency, making it well-suited for resource-constrained IoT applications. Full article