Smart Cities

The present research explores an innovative approach to objectively assessing urban streets attributes using 3D point clouds and Geographic Information Systems (GIS). Urban streets are vital components of cities, playing a significant role in the lives of their residents. Usually, the evaluation of some of their physical attributes has been subjective, but this study leverages 3D point clouds and digital terrain models (DTM) to provide a more objective perspective. This article undertakes a micro-urban analysis of basic physical characteristics (slope, width, and human scale) of a representative street in the historic centre of Valencia (Spain), utilizing 3D laser-scanned point clouds and GIS tools. Applying the proposed methodology, thematic maps were generated, facilitating the objective identification of areas with physical attributes more conducive to suitable pedestrian dynamics. This approach provides a comprehensive understanding of urban street attributes, emphasizing the importance of addressing their assessment through advanced digital technologies. Moreover, this versatile methodology has diverse applications, contributing to social sustainability by enhancing the quality of urban streets and open spaces. Full article

Cellular Vehicle-to-Everything (C-V2X) communications is a technology that enables intelligent vehicles to exchange information and thus coordinate with other vehicles, road users, and infrastructure. However, despite advancements in cellular technology for V2X applications, significant challenges remain regarding the ability of the system to meet stringent Quality-of-Service (QoS) requirements when deployed at scale. Thus, smaller-scale V2X use case deployments may embody a necessary stepping stone to address these challenges. This work assesses network architectures for an Intelligent Perception System (IPS) blind road junction or blind corner scenarios. Measurements were collected using a private 5G NR network with Sub-6GHz and mmWave connectivity, evaluating the feasibility and trade-offs of IPS network configurations. The results demonstrate the feasibility of the IPS as a V2X application, with implementation considerations based on deployment and maintenance costs. If computation resources are co-located with the sensors, sufficient performance is achieved. However, if the computational burden is instead placed upon the intelligent vehicle, it is questionable as to whether an IPS is achievable or not. Much depends on image quality, latency, and system performance requirements. Full article

Cities are facing challenges in adaptation to, and mitigation of climate change. Urban Green Spaces (UGS) have a pivotal role in this transformative process and are almost always coupled with digital tools. The deployment of digital solutions, encompassing Information and Communication Technology (ICT) and the Internet of Things (IoT), seeks to increase awareness of UGS benefits across a wider range of users. This study is part of a Horizon 2020 project that aims to measure the social impact of Visionary Solutions (VS), i.e., combined Nature Based Solutions (NBSs) and Digital Solutions (DSs), in UGSs located in seven European cities. The project proposes a novel application of the Social Return on Investment (SROI) methodology to forecast the impact of VS implementation in the case of an Italian demonstration. The three main objectives are: (i) establishing a causal chain for transformation through the Theory of Change (ToC) tool; (ii) quantifying the expected change by developing two monetary alternatives; and (iii) comparing these alternatives to assess which is more influential in stakeholders’ decision-making. The authors reviewed a range of financial proxies of social outcomes from other SROI case studies. The result of the Italian demonstration is that, for each euro invested in project solutions, two euros of social return are generated. The analysis reveals these monetized intangible outcomes. Full article

The speed at which vehicles navigate through roundabouts is information that needs to be considered in the intersection design process, simulation model development, and policy implementation. The last published data on speed profiles by distance was the Federal Highway Administration (FHWA) Roundabouts: an Informational Guide report, published in 2000, which pre-dates the ability to collect large volumes of connected vehicle (CV) data. The objective of this paper is to use a large sample of CV data to provide empirical analysis on vehicle speeds at roundabouts and to determine if previous guidelines are still applicable. Over 15 million speed records sampled at 56 roundabouts in Carmel, Indiana, from February to May 2023 during weekdays are categorized by turn type (i.e., right, through, or left) and by roundabout section (i.e., approach, circulation, or departure). Speed profiles and distributions for each category are analyzed by four different time-of-day (TOD) periods. The speed distribution analysis by roundabout section shows that 85% of vehicles travel under 34, 22, and 35 miles per hour (mph) on the approach, circulation, and departure zones, respectively. The analysis by turn type indicates that vehicles making left turns consistently maintain speeds below 20 mph when navigating inside roundabouts. In contrast, vehicles proceeding straight through or turning right accelerate soon after entering. Regardless of turn-type or TOD periods, most vehicles depart the roundabouts at similar speeds around 35 mph. A comparison between sampled and theoretical speed profiles reveals that while a state-of-the-practice model accurately estimates vehicle accelerations and decelerations near roundabouts, it does not account for reduced speeds before circulation begins and, in some cases, underestimates values on the circulation and departure sections. The results presented in this paper can be used to update current knowledge on vehicle speeds at roundabouts. Furthermore, local and state transportation agencies can use the presented technique to periodically update travel speed knowledge for their roundabouts where CV data or detection technology to derive traveling speeds is available. Full article

Public acceptance of conditionally automated vehicles is a crucial step in the realization of smart cities. Prior research in Europe has shown that the factors of hedonic motivation, social influence, and performance expectancy, in decreasing order of importance, influence acceptance. Moreover, a generally positive acceptance of the technology was reported. However, there is a lack of information regarding the public acceptance of conditionally automated vehicles in the United States. In this study, we carried out a web-based experiment where participants were provided information regarding the technology and then completed a questionnaire on their perceptions. The collected data was analyzed using PLS-SEM to examine the factors that may lead to public acceptance of the technology in the United States. Our findings showed that social influence, performance expectancy, effort expectancy, hedonic motivation, and facilitating conditions determine conditionally automated vehicle acceptance. Additionally, certain factors were found to influence the perception of how useful the technology is, the effort required to use it, and the facilitating conditions for its use. By integrating the insights gained from this study, stakeholders can better facilitate the adoption of autonomous vehicle technology, contributing to safer, more efficient, and user-friendly transportation systems in the future that help realize the vision of the smart city. Full article

Sustainable urban mobility is an imperative concern in contemporary cities, and shared micromobility systems, such as docked bike-sharing, dockless bike-sharing, and dockless e-scooter-sharing, are recognized as essential contributors to sustainable behaviors in cities, both complementing and enhancing public transport options. Most of the literature on this subject predominantly focuses on individual assessments of these systems, overlooking the comparative analysis necessary for a comprehensive understanding. This study aims to bridge this gap by conducting a spatiotemporal analysis of two different shared micromobility modes of transportation, docked bike-sharing systems and dockless e-scooter-sharing systems operating in the municipality of Lisbon. The analysis is further segmented into arrivals and departures on weekdays and weekends. Additionally, this study explores the impact of sociodemographic factors, the population’s commuting modes, and points of interest (POIs) on the demand for both docked bike-sharing and dockless e-scooter-sharing. Multiscale Geographically Weighted Regression (MGWR) models are employed to estimate the influence of these factors on system usage in different parishes in Lisbon. Comparative analysis reveals that the temporal distribution of trips is similar for both docked bike-sharing and dockless e-scooter-sharing systems on weekdays and weekends. However, differences in spatial distribution between the two systems were observed. The MGWR results indicate that the number of individuals commuting by bike in each parish has a positive effect on docked bike-sharing, while it exerts a negative influence on dockless e-scooter-sharing. Also, the number of commercial points of interest (POIs) for weekday arrivals positively affects the usage of both systems. This study contributes to a deeper understanding of shared micromobility patterns in urban environments and can aid cities in developing effective strategies that not only promote and increase the utilization of these shared micromobility systems but also contribute to sustainable urban mobility. Full article

Efficient energy management is crucial for optimizing greenhouse (GH) operations and promoting sustainability. This paper presents a novel multi-objective optimization approach tailored for GH energy management, aiming to minimize grid energy consumption while maximizing battery state of charge (SOC) within a specified time frame. The optimization problem integrates decision variables such as network power, battery power, and battery energy, subject to constraints based on battery capacity and initial energy, along with minimum and maximum energy from the battery storage system. Through the comparison of a smart energy management system (EMS) with traditional optimization algorithms, the study evaluates its efficiency. Key hyperparameters essential for the optimization problem, including plateau time, prediction time, and optimization time, are determined using the ellipse optimization method. Treating the GH as a microgrid, the analysis encompasses energy management indicators and loads. A simulation conducted via Simulink in MATLAB software (R2021b) demonstrates a significant enhancement, with the smart EMS achieving a more than 50% reduction in the objective function compared to conventional EMS. Moreover, the EMS exhibits robust performance across variations in the load power and irradiation profile. Under partial shading conditions, the EMS maintains adaptability, with a maximum objective function increase of 0.35553%. Aligning the output power of photovoltaic (PV) systems with real-world conditions further validates the EMS’s effectiveness in practical scenarios. The findings underscore the efficiency of the smart EMS in optimizing energy consumption within GH environments, offering promising avenues for sustainable energy management practices. This research contributes to advancing energy optimization strategies in agricultural settings, thereby fostering resource efficiency and environmental stewardship. Full article

The digital twin (DT) concept has been developed for a single function in previous studies. This study aims to empower DTs with a layered integration of multifunctional models in the built environment. It develops a framework of DT modules in three hierarchical tiers: region, asset, and system; defines a new concept of the degree of digital twinning (DODT) to the real world by the number of models enabled by a common DT platform; and enables spatiotemporal analysis in multiple scales to couple nonstructural with structural building components and connect the built environment to planning constructions. While the asset and system DTs focus on the lifecycle management of buildings and infrastructure systems, the region DT addresses diverse modeling approaches for a comprehensive management of the built environment as demonstrated on a university campus. The DODT allows the value-driven digital replication of a physical twin at different levels. For the campus case study, the DODT is eight, for building and infrastructure planning, condition assessment of building envelopes, construction management for efficiency and quality, damage/cost scenario studies under earthquake events, energy harvesting efficiency, environmental planning for flood zone susceptibility, master planning for green space development, and security protocol development. Full article

Infrastructure, service delivery, governance, and digital transformation stand as indispensable cornerstones, playing pivotal roles in the establishment of intelligent and sustainable urban centers. While the extant literature has underscored the significance of each of these elements, their interconnected and symbiotic relationship demands a more profound exploration. Grounded in a systematic review of the existing literature and relevant case studies, this paper explored the intricate interplay between digital transformation, infrastructure development, service delivery, and governance in contemporary society, all in the pursuit of cultivating smart sustainable cities. It contends that by collaboratively working together, these four pillars possess the transformative potential to turn cities into smart and sustainable cities. Digital transformation emerges as the catalyst, propelling innovation and efficiency, while infrastructure forms the bedrock for the seamless delivery of services. Effective governance, in turn, ensures alignment with the evolving needs of citizens. In essence, this study underscores the transformative power of combined action, asserting that the interdependent elements within can transform cities beyond merely having smart or sustainable status to become smart sustainable cities. This paradigm shift harmonizes technological advancements with the foundational goals of sustainable development, steering towards a holistic and inclusive urban future. Full article

The focus of this study is to integrate the DEX (Decision EXpert) decision-modeling method in architectural and urban design (A & UD) competitions. This study aims to assess the effectiveness of integrating the DEX (Decision EXpert) decision-modeling method into the evaluation process of A & UD competitions to enhance decision-making transparency, objectivity, and efficiency. By using symbolic values in decision models, the approach offers a more user-friendly alternative to the conventional jury decision-making process. The practical application of the DEX method is demonstrated in the Rhinoceros 3D environment to show its effectiveness in evaluating A & UD competition project solutions related to the development of the smart city. The results indicate that the DEX method, with its hierarchical and symbolic values, significantly improves the simplicity of the evaluation process in A & UD competitions, aligning it with the objectives of the smart cities. This method provides an efficient, accessible, and viable alternative to other multi-criteria decision-making approaches. This study importantly contributes to the field of architectural decision making by merging qualitative multi-criteria decision models into the CAD environment, thus supporting more informed, objective, and transparent decision-making processes in the planning and development of smart cities. Full article

Wireless communication infrastructure is a key enabling technology for smart cities. This paper investigates a novel technique to enhance the performance of 5G base stations by addressing the compensation of nonlinear distortions caused by radiofrequency power amplifiers. For this purpose, a sequential digital predistortion approach that uses twin nonlinear two-box structure along with reduced sampling rates in the feedback path is proposed to implement a linearization system. Such a system is shown to have a correction bandwidth that exceeds the bandwidth of the feedback path. This is achieved by synthesizing the predistortion function in two successive characterization iterations. Both characterizations use the same hardware, which has a reduced sampling rate in the feedback path. Hence, the proposed predistorter scheme does not require any additional hardware compared to standard schemes. Moreover, coarse delay alignment is performed while identifying the memory polynomial function in order to further reduce the computational complexity of the proposed system. Experimental results using an inverse Class-F power amplifier demonstrate the ability of the proposed predistorter to achieve a correction bandwidth of 100 MHz with a feedback sampling rate as low as 25 MSa/s. Full article

There are several standards for representing and compressing video information. These standards are adapted to the vision of the human eye. Autonomous cars see and perceive objects in a different way than humans and, therefore, the common standards are not suitable for them. In this paper, we will present a way of adjusting the common standards to be appropriate for the vision of autonomous cars. The focus of this paper will be on the H.264 format, but a similar order can be adapted to other standards as well. Full article

A surging demand for sustainable energy and the urgency to lower greenhouse gas emissions is driving industrial systems towards more eco-friendly and cost-effective models. Biogas from agricultural and municipal organic waste is gaining momentum as a renewable energy source. Concurrently, the European Hydrogen Strategy focuses on green hydrogen for decarbonising the industrial and transportation sectors. This paper presents a multi-objective network design model for urban–industrial symbiosis, incorporating anaerobic digestion, cogeneration, photovoltaic, and hydrogen production technologies. Additionally, a Bayesian best-worst method is used to evaluate the weights of the sustainability aspects by decision-makers, integrating these into the mathematical model. The model optimises industrial plant locations considering economic, environmental, and social parameters, including the net present value, energy consumption, and carbon footprint. The model’s functionalities are demonstrated through a real-world case study based in Emilia Romagna, Italy. It is subject to sensitivity analysis to evaluate how changes in the inputs affect the outcomes and highlights feasible trade-offs through the exploration of the $ϵ$-constraint. The findings demonstrate that the model substantially boosts energy and hydrogen production. It is not only economically viable but also reduces the carbon footprint associated with fossil fuels and landfilling. Additionally, it contributes to job creation. This research has significant implications, with potential future studies intended to focus on system resilience, plant location optimisation, and sustainability assessment. Full article

The continuous growth of urban populations and the complexities of their current management in Africa have driven local governments to explore new technologies to optimize their urban and territorial performance. These governments and related stakeholders’ resort to the term “smart city” to orient the current urban planning policies and practices to be more efficient and adequate. Nevertheless, the issue that remains is how to contextualize this global term that has not yet been fully adopted by African cities that have claimed to be “Smart”. This contextualization becomes more complex in this critical context, where the city has not yet reached an ideal performance. Therefore, to reach this prospective African smart city, a critical review of how it would be both human-centered and techno-centered is imperative. This paper would review accordingly the above argument and set key performance indicator-based methodology on how to evaluate the smartness of a city in the African context. Full article

The management of decentralized energy resources and smart grids needs novel data-driven low-latency applications and services to improve resilience and responsiveness and ensure closer to real-time control. However, the large-scale integration of Internet of Things (IoT) devices has led to the generation of significant amounts of data at the edge of the grid, posing challenges for the traditional cloud-based smart-grid architectures to meet the stringent latency and response time requirements of emerging applications. In this paper, we delve into the energy grid and computational distribution architectures, including edge–fog–cloud models, computational orchestration, and smart-grid frameworks to support the design and offloading of grid applications across the computational continuum. Key factors influencing the offloading process, such as network performance, data and Artificial Intelligence (AI) processes, computational requirements, application-specific factors, and energy efficiency, are analyzed considering the smart-grid operational requirements. We conduct a comprehensive overview of the current research landscape to support decision-making regarding offloading strategies from cloud to fog or edge. The focus is on metaheuristics for identifying near-optimal solutions and reinforcement learning for adaptively optimizing the process. A macro perspective on determining when and what to offload in the smart grid is provided for the next-generation AI applications, offering an overview of the features and trade-offs for selecting between federated learning and edge AI solutions. Finally, the work contributes to a comprehensive understanding of edge offloading in smart grids, providing a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to support cost–benefit analysis in decision-making regarding offloading strategies. Full article

Flooding in urban areas is expected to become even more common due to climatic changes, putting pressure on cities to implement effective response measures. Practical mechanisms for assessing flood risk have become highly desired, but existing solutions have been devoted to evaluating only specific cities and consider only limited risk perspectives, constraining their general applicability. This article presents an innovative approach for assessing the flood risk of delimited urban areas by exploiting geospatial information from publicly available databases, providing a method that is applicable to any city in the world and requiring minimum configurations. A set of mathematical equations is defined for numerically assessing risk levels based on elevation, slope, and proximity to rivers, while the existence of emergency-related urban infrastructure is considered as a risk reduction factor. Then, computed risk levels are used to classify areas, allowing easy visualisation of flood risk for a city. This smart city approach not only serves as a valuable tool for assessing the expected flood risk based on different parameters but also facilitates the implementation of cutting-edge strategies to effectively mitigate critical situations, ultimately enhancing urban resilience to flood-related disaster. Full article

The data sharing strategy involves understanding the challenges and problems that can be solved through the collaboration of different entities sharing their data. The implementation of a data space in Mallorca is based on understanding the available data and identifying the problems that can be solved using them. The use of data through data spaces will contribute to the transformation of destinations into smart tourism destinations. Smart tourism destinations are considered as smart cities in which the tourism industry offers a new layer of complexity in which technologies, digitalization, and intelligence are powered by data. This study analyzes four scenarios in which geo-dashboards are developed: flood exposure of tourist accommodation, land-cover changes, human pressure, and tourist uses in urban areas. The results of applying the geo-dashboards to these different scenarios provide tourists and destination managers with valuable information for decision-making, highlighting the utility of this type of tool, and laying the foundations for a future tourism data space in Mallorca. Full article

There is increasing use of digital technologies in urban planning, including in the generation of designs and the participative side of planning. We examine this digital planning by reporting on the application of an experimental online participatory platform in the regeneration of a London housing estate, enabling reflection on participation processes and outcomes. Drawing on lessons learned, the paper synthesises a conceptual representation of online participation and a relational framework for understanding the participatory platform and its context. We subsequently develop a ‘matrix of participative space’, building on Arnstein’s ‘ladder of participation’, to present a two-dimensional framework of online participation, identifying cases of ‘participative deficit’ and ‘democratic deficit’. We conclude with implications for future digital participation in urban planning and design. Full article

In recent years, research in the smart city sector has experienced exponential growth, establishing itself as a fundamental and multifaceted field of study. Education is one of the sectors of interest in smart cities. Concurrently, the extensive academic literature on smart cities makes identifying the main areas of interest related to education, leading institutions and authors, potential interconnections between different disciplines, and existing gaps more complicated. This article maps the knowledge domain of education in smart cities through a bibliometric analysis to identify current trends, research networks, and topics of greatest interest. A total of 88 articles, published between 2000 and 2023, were examined using an interdisciplinary approach. The leading countries are mainly located in Europe and North America and include China. Bibliometrics provides an intellectual configuration of knowledge on education in smart cities; a co-word analysis identifies conceptual sub-domains in specific themes. In general, education within smart cities represents a universal challenge that requires a structured and interdisciplinary approach at all levels. Finally, this paper offers some suggestions for future research, adopting a more comprehensive view of the areas of investigation through a holistic analysis of stakeholders. Full article