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Smart Cities
Number of Followers: 3  Open Access journalISSN (Online) 2624-6511 Published by MDPI  [258 journals]
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- Smart Cities, Vol. 7, Pages 1502-1550: The Role of Smart Homes in
Providing Care for Older Adults: A Systematic Literature Review from 2010
to 2023
Authors: Arian Vrančić, Hana Zadravec, Tihomir Orehovački
First page: 1502
Abstract: This study undertakes a systematic literature review, framed by eight research questions, and an exploration into the state-of-the-art concerning smart home innovations for care of older adults, ethical, security, and privacy considerations in smart home deployment, integration of technology, user interaction and experience, and smart home design and accessibility. The review evaluates the role of smart home technologies (SHTs) in enhancing the lives of older adults, focusing on their cost-effectiveness, ease of use, and overall utility. The inquiry aims to outline both the advantages these technologies offer in supporting care for older adults and the obstacles that impede their widespread adoption. Throughout the investigation, 58 studies were analyzed, selected for their relevance to the discourse on smart home applications in care for older adults. This selection came from a search of literature published between 2010 and 2023, ensuring an up-to-date understanding of the field. The findings highlight the potential of SHTs to improve various aspects of daily living for older adults, including safety, health monitoring, and social interaction. However, the research also identifies several challenges, including the high costs associated with these technologies, their complex nature, and ethical concerns surrounding privacy and autonomy. To address these challenges, the study presents recommendations to increase the accessibility and user-friendliness of SHTs for older adults. Among these, educational initiatives for older adults are emphasized as a strategy to improve technology acceptance, along with suggestions for design optimizations in wearable devices to enhance comfort and adaptability. The implications of this study are significant, offering insights for researchers, practitioners, developers, and policymakers engaged in creating and implementing smart home solutions for care of older adults. By offering an understanding of both the opportunities and barriers associated with SHTs, this research supports future efforts to create more inclusive, practical, and supportive environments for aging populations.
Citation: Smart Cities
PubDate: 2024-06-26
DOI: 10.3390/smartcities7040062
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1551-1575: Enhancing Service Quality of
On-Demand Transportation Systems Using a Hybrid Approach with Customized
Heuristics
Authors: Sonia Nasri, Hend Bouziri, Wassila Aggoune Mtalaa
First page: 1551
Abstract: As customers’ expectations continue to rise, advanced on-demand transport services face the challenge of meeting new requirements. This study addresses a specific transportation issue belonging to dial-a-ride problems, including constraints aimed at fulfilling customer needs. In order to provide more efficient on-demand transportation solutions, we propose a new hybrid evolutionary computation method. This method combines customized heuristics including two exchanged mutation operators, a crossover, and a tabu search. These optimization techniques have been empirically proven to support advanced designs and reduce operational costs, while significantly enhancing service quality. A comparative analysis with an evolutionary local search method from the literature has demonstrated the effectiveness of our approach across small-to-large-scale problems. The main results show that service providers can optimize their scheduling operations, reduce travel costs, and ensure a high level of service quality from the customer’s perspective.
Citation: Smart Cities
PubDate: 2024-06-26
DOI: 10.3390/smartcities7040063
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1576-1625: Unlocking Artificial Intelligence
Adoption in Local Governments: Best Practice Lessons from Real-World
Implementations
Authors: Tan Yigitcanlar, Anne David, Wenda Li, Clinton Fookes, Simon Elias Bibri, Xinyue Ye
First page: 1576
Abstract: In an era marked by rapid technological progress, the pivotal role of Artificial Intelligence (AI) is increasingly evident across various sectors, including local governments. These governmental bodies are progressively leveraging AI technologies to enhance service delivery to their communities, ranging from simple task automation to more complex engineering endeavours. As more local governments adopt AI, it is imperative to understand the functions, implications, and consequences of these advanced technologies. Despite the growing importance of this domain, a significant gap persists within the scholarly discourse. This study aims to bridge this void by exploring the applications of AI technologies within the context of local government service provision. Through this inquiry, it seeks to generate best practice lessons for local government and smart city initiatives. By conducting a comprehensive review of grey literature, we analysed 262 real-world AI implementations across 170 local governments worldwide. The findings underscore several key points: (a) there has been a consistent upward trajectory in the adoption of AI by local governments over the last decade; (b) local governments from China, the US, and the UK are at the forefront of AI adoption; (c) among local government AI technologies, natural language processing and robotic process automation emerge as the most prevalent ones; (d) local governments primarily deploy AI across 28 distinct services; and (e) information management, back-office work, and transportation and traffic management are leading domains in terms of AI adoption. This study enriches the existing body of knowledge by providing an overview of current AI applications within the sphere of local governance. It offers valuable insights for local government and smart city policymakers and decision-makers considering the adoption, expansion, or refinement of AI technologies in urban service provision. Additionally, it highlights the importance of using these insights to guide the successful integration and optimisation of AI in future local government and smart city projects, ensuring they meet the evolving needs of communities.
Citation: Smart Cities
PubDate: 2024-06-28
DOI: 10.3390/smartcities7040064
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1626-1669: Business Models Used in Smart
Cities—Theoretical Approach with Examples of Smart Cities
Authors: Radosław Wolniak, Bożena Gajdzik, Michaline Grebski, Roman Danel, Wiesław Wes Grebski
First page: 1626
Abstract: This paper examines business model implementations in three leading European smart cities: London, Amsterdam, and Berlin. Through a systematic literature review and comparative analysis, the study identifies and analyzes various business models employed in these urban contexts. The findings reveal a diverse array of models, including public–private partnerships, build–operate–transfer arrangements, performance-based contracts, community-centric models, innovation hubs, revenue-sharing models, outcome-based financing, and asset monetization strategies. Each city leverages a unique combination of these models to address its specific urban challenges and priorities. The study highlights the role of PPPs in large-scale infrastructure projects, BOT arrangements in transportation solutions, and performance-based contracts in driving efficiency and accountability. It also explores the benefits of community-centric models, innovation hubs, revenue-sharing models, outcome-based financing, and asset monetization strategies in enhancing the sustainability, efficiency, and livability of smart cities. The paper offers valuable insights for policymakers, urban planners, and researchers seeking to advance smart city development worldwide.
Citation: Smart Cities
PubDate: 2024-07-01
DOI: 10.3390/smartcities7040065
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1670-1705: Personalization of the Car-Sharing
Fleet Selected for Commuting to Work or for Educational Purposes—An
Opportunity to Increase the Attractiveness of Systems in Smart Cities
Authors: Katarzyna Turoń
First page: 1670
Abstract: Car-sharing services, which provide short-term vehicle rentals in urban centers, are rapidly expanding globally but also face numerous challenges. A significant challenge is the effective management of fleet selection to meet user expectations. Addressing this challenge, as well as methodological and literature gaps, the objective of this article is to present an original methodology that supports the evaluation of the suitability of vehicle fleets used in car-sharing systems and to identify the vehicle features preferred by users necessary for specific types of travel. The proposed methodology, which incorporates elements of transportation system modeling and concurrent analysis, was tested using a real-world case study involving a car-sharing service operator. The research focused on the commuting needs of car-sharing users for work or educational purposes. The study was conducted for a German car-sharing operator in Berlin. The research was carried out from 1 January to 30 June 2022. The findings indicate that the best vehicles for the respondents are large cars representing classes D or E, equipped with a combustion engine with a power of 63 to 149 kW, at least parking sensors, navigation, hands-free, lane assistant, heated seats, and high safety standards as indicated by Euro NCAP ratings, offered at the lowest possible rental price. The results align with market trends in Germany, which focus on the sale of at least medium-sized vehicles. This suggests a limitation of small cars in car-sharing systems, which were ideologically supposed to be a key fleet in those kinds of services. The developed methodology supports both system operators in verifying whether their fleet meets user needs and urban policymakers in effectively managing policies towards car-sharing services, including fleet composition, pricing regulations, and vehicle equipment standards. This work represents a significant step towards enhancing the efficiency of car-sharing services in the context of smart cities, where personalization and optimizing transport are crucial for sustainable development.
Citation: Smart Cities
PubDate: 2024-07-02
DOI: 10.3390/smartcities7040066
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1706-1722: Data-Driven Reliability Prediction
for District Heating Networks
Authors: Lasse Kappel Mortensen, Hamid Reza Shaker
First page: 1706
Abstract: As district heating networks age, current asset management practices, such as those relying on static life expectancies and age- and rule-based approaches, need to be replaced by data-driven asset management. As an alternative to physics-of-failure models that are typically preferred in the literature, this paper explores the application of more accessible traditional and novel machine learning-enabled reliability models for analyzing the reliability of district heating pipes and demonstrates how common data deficiencies can be accommodated by modifying the models’ likelihood expressions. The tested models comprised the Herz, Weibull, and the Neural Weibull Proportional Hazard models. An assessment of these models on data from an actual district heating network in Funen, Denmark showed that the relative youth of the network complicated the validation of the models’ distributional assumptions. However, a comparative evaluation of the models showed that there is a significant benefit in employing data-driven reliability modeling as they enable pipes to be differentiated based on the their working conditions and intrinsic features. Therefore, it is concluded that data-driven reliability models outperform current asset management practices such as age-based vulnerability ranking.
Citation: Smart Cities
PubDate: 2024-07-02
DOI: 10.3390/smartcities7040067
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1723-1775: Human-Centric Collaboration and
Industry 5.0 Framework in Smart Cities and Communities: Fostering
Sustainable Development Goals 3, 4, 9, and 11 in Society 5.0
Authors: Amr Adel, Noor HS Alani
First page: 1723
Abstract: The necessity for substantial societal transformations to meet the Sustainable Development Goals (SDGs) has become more urgent, especially in the wake of the COVID-19 pandemic. This paper examines the critical role of disruptive technologies, specifically Industry 5.0 and Society 5.0, in driving sustainable development. Our research investigation focuses on their impact on product development, healthcare innovation, pandemic response, and the development of nature-inclusive business models and smart cities. We analyze how these technologies influence SDGs 3 (Good Health and Well-Being), 4 (Quality Education), 9 (Industry, Innovation, and Infrastructure), and 11 (Sustainable Cities and Communities). By integrating these concepts into smart cities, we propose a coordinated framework to enhance the achievement of these goals. Additionally, we provide a SWOT analysis to evaluate this approach. This study aims to guide industrialists, policymakers, and researchers in leveraging technological advancements to meet the SDGs.
Citation: Smart Cities
PubDate: 2024-07-05
DOI: 10.3390/smartcities7040068
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1776-1801: Enhancing Property Valuation in
Post-War Recovery: Integrating War-Related Attributes into Real Estate
Valuation Practices
Authors: Mounir Azzam, Valerie Graw, Eva Meidler, Andreas Rienow
First page: 1776
Abstract: In post-war environments, property valuation encounters obstacles stemming from widespread destruction, population displacement, and complex legal frameworks. This study addresses post-war property valuation by integrating war-related considerations into the ISO 19152 Land Administration Domain Model, resulting in a valuation information model for Syria’s post-war landscape, serving as a reference for property valuation in conflict-affected areas. Additionally, property valuation is enhanced through visualization modeling, aiding the comprehension of war-related attributes amidst and following conflict. We utilize data from a field survey of 243 Condominium Units in the Harasta district, Rural Damascus Governorate. These data were collected through quantitative interviews with real estate companies and residents to uncover facts about property prices and war-related conditions. Our quantitative data are analyzed using inferential statistics of mean housing prices to assess the impact of war-related variables on property values during both wartime and post-war periods. The analysis reveals significant fluctuations in prices during wartime, with severely damaged properties experiencing notable declines (about −75%), followed by moderately damaged properties (about −60%). In the post-war phase, rehabilitated properties demonstrate price improvements (1.8% to 22.5%), while others continue to depreciate (−55% to −65%). These insights inform post-war property valuation standards, facilitating sustainable investment during the post-war recovery phase.
Citation: Smart Cities
PubDate: 2024-07-05
DOI: 10.3390/smartcities7040069
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1802-1835: Energy Management System for a
Residential Positive Energy District Based on Fuzzy Logic Approach
(RESTORATIVE)
Authors: Tony Castillo-Calzadilla, Jesús Oroya-Villalta, Cruz E. Borges
First page: 1802
Abstract: There is a clear European Strategy to transition by 2050 from a fossil fuel-based economy to a completely new system based on renewable energy resources, with electricity as the main energy carrier. Positive Energy Districts (PEDs) are urban areas that produce at least as much energy as their yearly consumption. To meet this objective, they must incorporate distributed generation based on renewable systems within their boundaries. This article considers the fluctuations in electricity prices and local renewable availability and develops a PED model with a centralised energy storage system focused on electricity self-sufficiency and self-consumption. We present a fuzzy logic-based energy management system which optimises the state of charge of the energy storage solution considering local electricity production and loads along with the contracted electric tariff. The methodology is tested in a PED comprising 360 households in Bilbao (a city in the north of Spain), setting various scenarios, including changes in the size of the electric storage, long-term climate change effects, and extreme changes in the price of energy carriers. The study revealed that the assessed PED could reach up to 75.6% self-sufficiency and 76.8% self-consumption, with climate change expected to improve these values. On economic aspects, the return on investment of the proposal ranges from 6 up to 12 years depending on the configuration choice. Also, the case that boosts the economic viability is tight to non-business as usual (BaU), whichever event spiked up the prices or climate change conditions shortens the economic variables. The average bill is around 12.89 EUR/month per house for scenario BaU; meanwhile, a catastrophic event increases the bill by as much as 76.7%. On the other hand, climate crisis events impact energy generation, strengthening this and, as a consequence, slightly reducing the bill by up to 11.47 EUR/month.
Citation: Smart Cities
PubDate: 2024-07-16
DOI: 10.3390/smartcities7040070
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1836-1856: AI-Driven Prediction and Mapping of
Soil Liquefaction Risks for Enhancing Earthquake Resilience in Smart
Cities
Authors: Arisa Katsuumi, Yuxin Cong, Shinya Inazumi
First page: 1836
Abstract: In response to increasing urbanization and the need for infrastructure resilient to natural hazards, this study introduces an AI-driven predictive model designed to assess the risk of soil liquefaction. Utilizing advanced ensemble machine learning techniques, the model integrates geotechnical and geographical data to accurately predict the potential for soil liquefaction in urban areas, with a specific focus on Yokohama, Japan. This methodology leverages comprehensive datasets from geological surveys and seismic activity to enhance urban planning and infrastructure development in smart cities. The primary outputs include detailed soil liquefaction risk maps that are essential for effective urban risk management. These maps support urban planners and engineers in making informed decisions, prioritizing safety, and promoting sustainability. The model employs a robust combination of artificial neural networks and gradient boosting decision trees to analyze and predict data points, assessing soil susceptibility to liquefaction during seismic events. Notably, the model achieves high accuracy in predicting soil classifications and N-values, which are critical for evaluating soil liquefaction risk. Validation against an extensive dataset from geotechnical surveys confirms the model’s practical effectiveness. Moreover, the results highlight the transformative potential of AI in enhancing geotechnical risk assessments and improving the resilience of urban areas against natural hazards.
Citation: Smart Cities
PubDate: 2024-07-17
DOI: 10.3390/smartcities7040071
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1857-1877: Data Governance to Counter Hybrid
Threats against Critical Infrastructures
Authors: Gabriel Pestana, Souzanna Sofou
First page: 1857
Abstract: Hybrid threats exploit vulnerabilities in digital infrastructures, posing significant challenges to democratic countries and the resilience of critical infrastructures (CIs). This study explores integrating data governance with business process management in response actions to hybrid attacks, particularly those targeting CI vulnerabilities. This research analyzes hybrid threats as a multidimensional and time-dependent problem. Using the Business Process Model and Notation, this investigation explores data governance to counter CI-related hybrid threats. It illustrates the informational workflow and context awareness necessary for informed decision making in a cross-border hybrid threat scenario. An airport example demonstrates the proposed approach’s efficacy in ensuring stakeholder coordination for potential CI attacks requiring cross-border decision making. This study emphasizes the importance of the information security lifecycle in protecting digital assets and sensitive information through detection, prevention, response, and knowledge management. It advocates proactive strategies like implementing security policies, intrusion detection software tools, and IT services. Integrating Infosec with the methodology of confidentiality, integrity, and availability, especially in the response phase, is essential for a proactive Infosec approach, ensuring a swift stakeholder response and effective incident mitigation. Effective data governance protects sensitive information and provides reliable digital data in CIs like airports. Implementing robust frameworks enhances resilience against hybrid threats, establishes trusted information exchange, and promotes stakeholder collaboration for an emergency response. Integrating data governance with Infosec strengthens security measures, enabling proactive monitoring, mitigating threats, and safeguarding CIs from cyber-attacks and other malicious activities.
Citation: Smart Cities
PubDate: 2024-07-22
DOI: 10.3390/smartcities7040072
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1878-1887: Modeling Strategic Interventions to
Increase Attendance at Youth Community Centers
Authors: Alejandro Moro-Araujo, Luis Alonso Pastor, Kent Larson
First page: 1878
Abstract: Community centers play a crucial role in urban environments, providing physical and educational services to their surrounding communities, particularly for students. Among the many benefits for students are enhanced academic outcomes, improvement of behavioral problems, and increased school attendance. Such centers are also particularly vital for low-income and racial minority students as they are pivotal in giving them outside-of-school learning opportunities. However, determinants influencing attendance at community centers remain largely unexplored. The novelty of our research comes from using census data, Boston Centers for Youth and Families (BCYF) attendance data, and specific center attributes, to develop human mobility gravitational models that have been used, for the first time, to predict attendance across the BCYF network. Using those models, we simulated the potential effects on general and student attendance by changing center attributes, such as facilities and operating hours. We also researched the impact of changing the walking accessibility to those centers on their respective attendance patterns. After the analysis, we found that the most cost-effective policy to increase BCYF attendance is changing each center’s educational and recreational offerings far beyond any accessibility interventions. Our results provide insights into potential policy changes that could optimize the attendance and reach of BCYF Community Centers to under-served populations.
Citation: Smart Cities
PubDate: 2024-07-22
DOI: 10.3390/smartcities7040073
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1888-1906: Multi-Type Structural Damage Image
Segmentation via Dual-Stage Optimization-Based Few-Shot Learning
Authors: Jiwei Zhong, Yunlei Fan, Xungang Zhao, Qiang Zhou, Yang Xu
First page: 1888
Abstract: The timely and accurate recognition of multi-type structural surface damage (e.g., cracks, spalling, corrosion, etc.) is vital for ensuring the structural safety and service performance of civil infrastructure and for accomplishing the intelligent maintenance of smart cities. Deep learning and computer vision have made profound impacts on automatic structural damage recognition using nondestructive test techniques, especially non-contact vision-based algorithms. However, the recognition accuracy highly depends on the training data volume and damage completeness in the conventional supervised learning pipeline, which significantly limits the model performance under actual application scenarios; the model performance and stability for multi-type structural damage categories are still challenging. To address the above issues, this study proposes a dual-stage optimization-based few-shot learning segmentation method using only a few images with supervised information for multi-type structural damage recognition. A dual-stage optimization paradigm is established encompassing an internal network optimization based on meta-task and an external meta-learning machine optimization based on meta-batch. The underlying image features pertinent to various structural damage types are learned as prior knowledge to expedite adaptability across diverse damage categories via only a few samples. Furthermore, a mathematical framework of optimization-based few-shot learning is formulated to intuitively express the perception mechanism. Comparative experiments are conducted to verify the effectiveness and necessity of the proposed method on a small-scale multi-type structural damage image set. The results show that the proposed method could achieve higher segmentation accuracies for various types of structural damage than directly training the original image segmentation network. In addition, the generalization ability for the unseen structural damage category is also validated. The proposed method provides an effective solution to achieve image-based structural damage recognition with high accuracy and robustness for bridges and buildings, which assists the unmanned intelligent inspection of civil infrastructure using drones and robotics in smart cities.
Citation: Smart Cities
PubDate: 2024-07-22
DOI: 10.3390/smartcities7040074
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1907-1935: Energy Management in Residential
Microgrid Based on Non-Intrusive Load Monitoring and Internet of Things
Authors: Rawda Ramadan, Qi Huang, Amr S. Zalhaf, Olusola Bamisile, Jian Li, Diaa-Eldin A. Mansour, Xiangning Lin, Doaa M. Yehia
First page: 1907
Abstract: Recently, various strategies for energy management have been proposed to improve energy efficiency in smart grids. One key aspect of this is the use of microgrids. To effectively manage energy in a residential microgrid, advanced computational tools are required to maintain the balance between supply and demand. The concept of load disaggregation through non-intrusive load monitoring (NILM) is emerging as a cost-effective solution to optimize energy utilization in these systems without the need for extensive sensor infrastructure. This paper presents an energy management system based on NILM and the Internet of Things (IoT) for a residential microgrid, including a photovoltaic (PV) plant and battery storage device. The goal is to develop an efficient load management system to increase the microgrid’s independence from the traditional electrical grid. The microgrid model is developed in the electromagnetic transient program PSCAD/EMTDC to analyze and optimize energy performance. Load disaggregation is obtained by combining artificial neural networks (ANNs) and particle swarm optimization (PSO) to identify appliances for demand-side management. An ANN is applied in NILM as a load identification task, and PSO is used to optimize the ANN algorithm. This combination enhances the NILM technique’s accuracy, which is verified using the mean absolute error method to assess the difference between the predicted and measured power consumption of appliances. The NILM output is then transferred to consumers through the ThingSpeak IoT platform, enabling them to monitor and control their appliances to save energy and costs.
Citation: Smart Cities
PubDate: 2024-07-23
DOI: 10.3390/smartcities7040075
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1936-1949: Smart Non-Intrusive Appliance
Load-Monitoring System Based on Phase Diagram Analysis
Authors: Denis Stanescu, Florin Enache, Florin Popescu
First page: 1936
Abstract: Much of today’s power grid was designed and built using technologies and organizational principles developed decades ago. The lack of energy resources and classic power networks are the main causes of the development of the smart grid to efficiently use energy resources, with stable and safe operation. In such a network, one of the fundamental priorities is provided by non-intrusive appliance load monitoring (NIALM) in order to analyze, recognize and determine the electricity consumption of each consumer. In this paper, we propose a new smart system approach for the characterization of the appliance load signature based on a data-driven method, namely the phase diagram. Our aim is to use the non-intrusive load monitoring of appliances in order to recognize different types of consumers that can exist within a smart building.
Citation: Smart Cities
PubDate: 2024-07-23
DOI: 10.3390/smartcities7040076
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1950-1970: Analyzing the Requirements for
Smart Pedestrian Applications: Findings from Nicosia, Cyprus
Authors: George N. Papageorgiou, Demetris Demetriou, Elena Tsappi, Athanasios Maimaris
First page: 1950
Abstract: This paper elicits and analyzes the main requirements for Smart Pedestrian applications designed to enhance the pedestrian experience in urban environments by offering optimized walking routes, improved accessibility, and support for social inclusion and connectivity. Utilizing a mixed-methods approach, the research combines qualitative insights with quantitative data analysis based on surveys conducted in two strategically selected urban areas of Nicosia, Cyprus. Through the survey, the requirements and potential use of Smart Pedestrian apps are investigated while accounting for the quality of service of the urban infrastructure in a medium-sized city context. Additionally, the study contrasts the current smartphone applications, as they predominantly facilitate vehicular transportation, with the potential use of ICT/ITS to support pedestrians for sustainable mobility. The findings reveal a significant demand for a Pedestrian Smartphone app, driven by its ability to provide relevant information on optimum pedestrian routes, as well as act as a citizen’s voice for spotting infrastructure problems and improving the pedestrian network. Further, it is also revealed that limitations in the pedestrian infrastructure substantially restrict walking preferences, emphasizing the need for urgent city-level urban planning solutions to support active mobility. Additionally, the research carried out underscores the importance of a sustainable business model to support the successful deployment of Smart Pedestrian apps. Ultimately, the results of the study suggest prioritizing a smart technology leverage with a crowdsourcing social network business model to promote pedestrian mobility, thereby reducing vehicular dependence, enhancing public health, and improving the quality of life. Such an approach would act as catalyst for policymakers to concentrate on sustainability by investing in digital technology for integrated pedestrian networks, fostering the emergence of genuine smart cities.
Citation: Smart Cities
PubDate: 2024-07-24
DOI: 10.3390/smartcities7040077
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1971-1991: Integrating Smart City Principles
in the Numerical Simulation Analysis on Passive Energy Saving of Small and
Medium Gymnasiums
Authors: Feng Qian, Hongliang Sun, Li Yang
First page: 1971
Abstract: With the increasing energy consumption in buildings, the proportion of energy consumption in public buildings continues to grow. As an essential component of public buildings, sports buildings are receiving more attention regarding energy-saving technologies. This paper aims to study the passive energy-saving design methods of small-and medium-sized sports halls in hot summer and cold winter regions, exploring how to reduce building energy consumption by improving the spatial design and thermal performance of the enclosure structures of sports halls. Taking the Wuhu County Sports Center as an example, this study uses computer simulation software to analyze the building’s wind environment and the thermal performance of its external walls and roof. The results show that the large volume of the sports hall significantly impacts the distribution of wind speed and pressure around it, and this impact decreases with height. The thermal simulation of the enclosure structures demonstrates that adding insulation layers to the interior and exterior of the walls and roof of the sports hall is an effective way to reduce energy consumption in both winter and summer. Additionally, wind environment simulations of different roof shapes reveal that flat roofs have the most significant blocking effect on wind and are prone to inducing strong vortices on the leeward side; concave arch roofs have the least blocking effect on airflow, and arch and wave-shaped roofs maintain lower vortex intensity on the leeward side. Hopefully, this study can provide significant references for the energy-saving design of future small- and medium-sized sports buildings.
Citation: Smart Cities
PubDate: 2024-07-25
DOI: 10.3390/smartcities7040078
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 1992-2014: Enhancing Cycling Safety in Smart
Cities: A Data-Driven Embedded Risk Alert System
Authors: José Miguel Ferreira, Daniel G. Costa
First page: 1992
Abstract: The safety of cyclists on city streets is a significant concern, particularly with the rising number of accidents in densely populated areas. Urban environments present numerous challenges, such as complex road networks and heavy traffic, which increase the risk of cycling-related incidents. Such concern has been recurrent, even within smart city scenarios that have been focused on only expanding the cycling infrastructure. This article introduces an innovative low-cost embedded system designed to improve cycling safety in urban areas, taking geospatial data as input. By assessing the proximity to emergency services and utilizing GPS coordinates, the system can determine the indirect current risk level for cyclists, providing real-time alerts when crossing high-risk zones. Built on a Raspberry Pi Zero board, this solution is both cost-effective and efficient, making it easily reproducible in various urban settings. Preliminary results in Porto, Portugal, showcase the system’s practical application and effectiveness in enhancing cycling safety and supporting sustainable urban mobility.
Citation: Smart Cities
PubDate: 2024-07-26
DOI: 10.3390/smartcities7040079
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 2015-2041: Transitioning to a Low-Carbon
Lifestyle' An Exploration of Millennials’ Low-Carbon
Behavior—A Case Study in China
Authors: Yan Wu, Pim Martens, Thomas Krafft
First page: 2015
Abstract: The Sustainable Development Goals (SDGs) have set the agenda for 2030, calling for collective global efforts to deal with climate change while seeking a balance between economic development and environmental protection. Although many countries are exploring emission reduction paths, mainly from government and corporate perspectives, addressing climate change is also an individual responsibility and requires public participation in collective action. The millennial generation constitutes the current workforce and will be the leaders in climate action for the next 30 years. Therefore, our study focuses on the Chinese millennial generation, conducting in-depth semi-structured interviews with 50 participants in qualitative research to explore their low-carbon lifestyles, the barriers, and enablers in switching to a wider range of low-carbon lifestyles. There are three main results: (1) Based on our study samples, there is an indication that Chinese millennials have a positive attitude towards transitioning to a low-carbon lifestyle. Women demonstrate a stronger willingness to adopt low-carbon behaviors in their daily household activities compared to men. However, their involvement in governance in the context of transitioning to a low-carbon society is limited, with most women assuming execution roles in climate action rather than decision-making positions. (2) Millennial’s low-carbon life transition is accompanied by technological innovation and progress. However, this progress brings some new forms of resource waste, and reasonable policy-making is essential. (3) Personal economic interests and the satisfaction of their consumption needs will drive millennials to reduce carbon emissions in their daily lives, but it requires the guidance of reasonable policy-making and synergies among various stakeholders. This research will help policymakers better understand the current status and potential issues related to people’s low-carbon actions, enabling the formulation of more rational guiding policies. It can also help other stakeholders learn about millennials’ demands and take more effective collective action toward carbon reduction.
Citation: Smart Cities
PubDate: 2024-07-26
DOI: 10.3390/smartcities7040080
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 2042-2064: Cyber Insurance for Energy Economic
Risks
Authors: Alexis Pengfei Zhao, Faith Xue Fei, Mohannad Alhazmi
First page: 2042
Abstract: The proliferation of information and communication technologies (ICTs) within smart cities has not only enhanced the capabilities and efficiencies of urban energy systems but has also introduced significant cyber threats that can compromise these systems. To mitigate the financial risks associated with cyber intrusions in smart city infrastructures, this study introduces a two-stage hierarchical planning model for ICT-integrated multi-energy systems, emphasizing the economic role of cyber insurance. By adopting cyber insurance, smart city operators can mitigate the financial impact of unforeseen cyber incidents, transferring these economic risks to the insurance provider. The proposed two-stage optimization model strategically balances the economic implications of urban energy system operations with cyber insurance coverage. This approach allows city managers to make economically informed decisions about insurance procurement in the first stage and implement cost-effective defense strategies against potential cyberattacks in the second stage. Utilizing a distributionally robust approach, the study captures the emergent and uncertain nature of cyberattacks through a moment-based ambiguity set and resolves the reformulated linear problem using a dynamic cutting plane method. This work offers a distinct perspective on managing the economic risks of cyber incidents in smart cities and provides a valuable framework for decision making regarding cyber insurance procurement, ultimately aiming to enhance the financial stability of smart city energy operations.
Citation: Smart Cities
PubDate: 2024-07-27
DOI: 10.3390/smartcities7040081
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 2065-2093: Advancing Electric Load
Forecasting: Leveraging Federated Learning for Distributed,
Non-Stationary, and Discontinuous Time Series
Authors: Lucas Richter, Steve Lenk, Peter Bretschneider
First page: 2065
Abstract: In line with several European directives, residents are strongly encouraged to invest in renewable power plants and flexible consumption systems, enabling them to share energy within their Renewable Energy Community at lower procurement costs. This, along with the ability for residents to switch between such communities on a daily basis, leads to dynamic portfolios, resulting in non-stationary and discontinuous electrical load time series. Given poor predictability as well as insufficient examination of such characteristics, and the critical importance of electrical load forecasting in energy management systems, we propose a novel forecasting framework using Federated Learning to leverage information from multiple distributed communities, enabling the learning of domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we evaluate their effectiveness by applying different tests for white noise in the forecast error signal. The findings suggest that our proposed framework is capable of effectively forecast non-stationary as well as discontinuous time series, extract domain-invariant features, and is applicable to new, unseen data through the integration of knowledge from multiple sources.
Citation: Smart Cities
PubDate: 2024-07-28
DOI: 10.3390/smartcities7040082
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 2094-2109: Mapping the Implementation
Practices of the 15-Minute City
Authors: Zaheer Allam, Amir Reza Khavarian-Garmsir, Ulysse Lassaube, Didier Chabaud, Carlos Moreno
First page: 2094
Abstract: This paper delves into the rapidly progressing 15-Minute City concept, an innovative urban planning model that envisions a city where residents can access essential services and amenities within a 15-min walk or bike ride from their homes. Endorsed by UN-Habitat as a critical strategy for sustainable urban regeneration, this concept has gained considerable worldwide recognition since its introduction in 2016. The 15-Minute City framework aims to enhance accessibility, sustainability, and social cohesion by emphasizing mixed-use development, compact urban design, and efficient transportation systems. Nevertheless, the swift expansion of this concept has surpassed the production of academic literature on the topic, leading to a knowledge gap that calls for alternative research methodologies. To address this gap, our paper adopts a mixed-method approach, systematically analyzing the scholarly literature, gray literature, media articles, and policy documents to offer a holistic understanding of the 15-Minute City concept, its real-world application, and the primary principles embraced by policymakers. By investigating the various manifestations of the 15-Minute City model and its potential advantages, challenges, and implications for urban planning and policy, this paper contributes to the ongoing conversation on sustainable urban development and planning. Through this study, we aim to inform policymakers, urban planners, and researchers about the current state of the 15-Minute City movement and its possible future trajectory.
Citation: Smart Cities
PubDate: 2024-08-01
DOI: 10.3390/smartcities7040083
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 2110-2130: Advancing Urban Resilience Amid
Rapid Urbanization: An Integrated Interdisciplinary Approach for
Tomorrow’s Climate-Adaptive Smart Cities—A Case Study of
Wuhan, China
Authors: Mehdi Makvandi, Wenjing Li, Yu Li, Hao Wu, Zeinab Khodabakhshi, Xinhui Xu, Philip F. Yuan
First page: 2110
Abstract: This research addresses the urgent challenges posed by rapid urbanization and climate change through an integrated interdisciplinary approach combining advanced technologies with rigorous scientific exploration. The comprehensive analysis focused on Wuhan, China, spanning decades of meteorological and land-use data to trace extreme urbanization trajectories and reveal intricate temporal and spatial patterns. Employing the innovative 360° radial Fibonacci geometric growth framework, the study facilitated a meticulous dissection of urban morphology at granular scales, establishing a model that combined fixed and mobile observational techniques to uncover climatic shifts and spatial transformations. Geographic information systems and computational fluid dynamics were pivotal tools used to explore the intricate interplay between urban structures and their environments. These analyses elucidated the nuanced impact of diverse morphosectors on local conditions. Furthermore, genetic algorithms were harnessed to distill meaningful relationships from the extensive data collected, optimizing spatial arrangements to enhance urban resilience and sustainability. This pioneering interdisciplinary approach not only illuminates the complex dynamics of urban ecosystems but also offers transformative insights for designing smarter, more adaptable cities. The findings underscore the critical role of green spaces in mitigating urban heat island effects. This highlights the imperative for sustainable urban planning to address the multifaceted challenges of the 21st century, promoting long-term environmental sustainability and urban health, particularly in the context of tomorrow’s climate-adaptive smart cities.
Citation: Smart Cities
PubDate: 2024-08-01
DOI: 10.3390/smartcities7040084
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 2131-2181: Network Security Challenges and
Countermeasures for Software-Defined Smart Grids: A Survey
Authors: Dennis Agnew, Sharon Boamah, Arturo Bretas, Janise McNair
First page: 2131
Abstract: The rise of grid modernization has been prompted by the escalating demand for power, the deteriorating state of infrastructure, and the growing concern regarding the reliability of electric utilities. The smart grid encompasses recent advancements in electronics, technology, telecommunications, and computer capabilities. Smart grid telecommunication frameworks provide bidirectional communication to facilitate grid operations. Software-defined networking (SDN) is a proposed approach for monitoring and regulating telecommunication networks, which allows for enhanced visibility, control, and security in smart grid systems. Nevertheless, the integration of telecommunications infrastructure exposes smart grid networks to potential cyberattacks. Unauthorized individuals may exploit unauthorized access to intercept communications, introduce fabricated data into system measurements, overwhelm communication channels with false data packets, or attack centralized controllers to disable network control. An ongoing, thorough examination of cyber attacks and protection strategies for smart grid networks is essential due to the ever-changing nature of these threats. Previous surveys on smart grid security lack modern methodologies and, to the best of our knowledge, most, if not all, focus on only one sort of attack or protection. This survey examines the most recent security techniques, simultaneous multi-pronged cyber attacks, and defense utilities in order to address the challenges of future SDN smart grid research. The objective is to identify future research requirements, describe the existing security challenges, and highlight emerging threats and their potential impact on the deployment of software-defined smart grid (SD-SG).
Citation: Smart Cities
PubDate: 2024-08-02
DOI: 10.3390/smartcities7040085
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 2182-2207: Ontology-Based Deep Learning Model
for Object Detection and Image Classification in Smart City Concepts
Authors: Adekanmi Adeyinka Adegun, Jean Vincent Fonou-Dombeu, Serestina Viriri, John Odindi
First page: 2182
Abstract: Object detection in remotely sensed (RS) satellite imagery has gained significance in smart city concepts, which include urban planning, disaster management, and environmental monitoring. Deep learning techniques have shown promising outcomes in object detection and scene classification from RS satellite images, surpassing traditional methods that are reliant on hand-crafted features. However, these techniques lack the ability to provide in-depth comprehension of RS images and enhanced interpretation for analyzing intricate urban objects with functional structures and environmental contexts. To address this limitation, this study proposes a framework that integrates a deep learning-based object detection algorithm with ontology models for effective knowledge representation and analysis. The framework can automatically and accurately detect objects and classify scenes in remotely sensed satellite images and also perform semantic description and analysis of the classified scenes. The framework combines a knowledge-guided ontology reasoning module into a YOLOv8 objects detection model. This study demonstrates that the proposed framework can detect objects in varying environmental contexts captured using a remote sensing satellite device and incorporate efficient knowledge representation and inferences with a less-complex ontology model.
Citation: Smart Cities
PubDate: 2024-08-02
DOI: 10.3390/smartcities7040086
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 2208-2231: Expert Evaluation of the
Significance of Criteria for Electric Vehicle Deployment: A Case Study of
Lithuania
Authors: Henrikas Sivilevičius, Vidas Žuraulis, Justas Bražiūnas
First page: 2208
Abstract: This study presents the hierarchical structure of 50 sub-criteria divided into 7 main criteria for the assessment of electric vehicle (EV) deployment. Two options, Average Rank Transformations and Analytic Hierarchy Process methods, were applied in determining the local weights of the sub-criteria. The sufficient compatibility of expert opinions was accomplished using the averages of the ranks of the main criteria and sub-criteria as the result of solving the problem. The averages of the local weights were calculated employing three Multiple Criteria Decision-Making methods that increased the reliability of the research results. Based on this, the global weights and priorities of the sub-criteria were evaluated. The experts suppose that EV deployment at the national level is mainly affected by the higher cost of manufacturing and purchasing EVs, the application of financial incentives for purchasing EVs, the lack of exhausted gasses, the installation of fast charging points, and the absence of infrastructure in the five largest cities nationwide. The obtained results demonstrate that out of 50 sub-criteria, the cumulative global weight of the 10 most important sub-criteria (mainly based in economics) amounts to more than 35%, whereas that of the 22 most important sub-criteria have a weight above the average (0.2), reaching approximately 65%. The findings can be put into practice by state decision makers of EV deployment.
Citation: Smart Cities
PubDate: 2024-08-03
DOI: 10.3390/smartcities7040087
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 2232-2257: Smart City Community
Watch—Camera-Based Community Watch for Traffic and Illegal Dumping
Authors: Nupur Pathak, Gangotri Biswal, Megha Goushal, Vraj Mistry, Palak Shah, Fenglian Li, Jerry Gao
First page: 2232
Abstract: The United States is the second-largest waste generator in the world, generating 4.9 pounds (2.2 kg) of Municipal Solid Waste (MSW) per person each day. The excessive amount of waste generated poses serious health and environmental risks, especially because of the prevalence of illegal dumping practices, including improper waste disposal in unauthorized areas. To clean up illegal dumping, the government spends approximately USD 600 per ton, which amounts to USD 178 billion per year. Municipalities face a critical challenge to detect and prevent illegal dumping activities. Current techniques to detect illegal dumping have limited accuracy in detection and do not support an integrated solution of detecting dumping, identifying the vehicle, and a decision algorithm notifying the municipalities in real-time. To tackle this issue, an innovative solution has been developed, utilizing a You Only Look Once (YOLO) detector YOLOv5 for detecting humans, vehicles, license plates, and trash. The solution incorporates DeepSORT for effective identification of illegal dumping by analyzing the distance between a human and the trash’s bounding box. It achieved an accuracy of 97% in dumping detection after training on real-time examples and the COCO dataset covering both daytime and nighttime scenarios. This combination of YOLOv5, DeepSORT, and the decision module demonstrates robust capabilities in detecting dumping. The objective of this web-based application is to minimize the adverse effects on the environment and public health. By leveraging advanced object detection and tracking techniques, along with a user-friendly web application, it aims to promote a cleaner, healthier environment for everyone by reducing improper waste disposal.
Citation: Smart Cities
PubDate: 2024-08-07
DOI: 10.3390/smartcities7040088
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 2258-2282: Exploring the Influence of Thai
Government Policy Perceptions on Electric Vehicle Adoption: A Measurement
Model and Empirical Analysis
Authors: Dissakoon Chonsalasin, Thanapong Champahom, Sajjakaj Jomnonkwao, Ampol Karoonsoontawong, Norarat Runkawee, Vatanavongs Ratanavaraha
First page: 2258
Abstract: This study explores the influence of Thai government policy perceptions on the adoption of electric vehicles (EVs). Transitioning to EVs is vital for reducing greenhouse gas emissions and combating climate change, aligning with global sustainability goals. This study addresses gaps in understanding how multidimensional perceptions of government policies influence EV adoption intentions in emerging markets, particularly in Thailand. A questionnaire was distributed to 3770 respondents across Thailand between January and March 2024. The survey assessed multiple dimensions of government policy, including commitment and efficiency, welfare, communication, policy effectiveness, and tax benefits. Using statistical techniques such as Exploratory Factor Analysis (EFA), second-order confirmatory factor analysis (CFA), and structural equation modeling (SEM), this study validated the constructs of government support perception and examined their influence on EV adoption intentions. The findings highlight that tangible government policies, particularly those improving EV infrastructure and providing clear regulatory support, alongside effective communication about these policies, significantly influence public willingness to adopt EVs. The results also emphasize the critical role of perceived government commitment and fiscal incentives in shaping consumer decisions. Based on these insights, this study recommends prioritizing the expansion of EV infrastructure, enhancing the visibility of government commitment, and improving direct financial incentives to accelerate EV adoption. These findings contribute to the growing body of knowledge on EV adoption in emerging markets and offer practical implications for policymakers seeking to promote sustainable transportation solutions.
Citation: Smart Cities
PubDate: 2024-08-09
DOI: 10.3390/smartcities7040089
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 2283-2317: Decentralized Incident Reporting:
Mobilizing Urban Communities with Blockchain
Authors: El-hacen Diallo, Rouwaida Abdallah, Mohammad Dib, Omar Dib
First page: 2283
Abstract: This paper introduces an innovative response to the pressing challenge of rapid and effective incident detection and management in urban settings. The proposed solution is a decentralized incident reporting system (IRS) harnessing blockchain technology and decentralized data storage systems. By empowering residents to report incidents, the proposed IRS enables seamless real-time monitoring and intervention by relevant departments. Built on a blockchain foundation, the proposed solution ensures immutability, transparency, security, and auditability, enhancing data resilience and comprehensive applicability. The proposed system leverages the InterPlanetary File System (IPFS) for the storage of incident proofs to manage the blockchain size effectively. Through the proposed IRS, transparency is upheld, enabling complete auditability of incident details and required interventions by citizens, societal bodies, and governmental bodies. Moreover, an incentive model is introduced to encourage active participation in incident reporting, thereby enhancing the system’s overall effectiveness and long-term sustainability. The proposed IRS integrates mobile technology to facilitate user engagement and data submission, essential for urban emergency management. Empirical validation using the Quorum–Raft blockchain demonstrates the feasibility of the proposed approach in terms of system throughput, incident reporting delay, blockchain size, and deployment cost. Specifically, the system maintains a latency of under 15 s even at high transaction rates, can handle up to 200 incidents per second, and is cost-effective, with deployment estimates for 16 organizations over five years being under 1.99 million USD. The method involves extensive testing with simulated incidents and user interactions to ensure robustness and scalability, showcasing the system’s potential for effective emergency management in urban environments.
Citation: Smart Cities
PubDate: 2024-08-14
DOI: 10.3390/smartcities7040090
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 2318-2338: Pipeline Leak Detection System for
a Smart City: Leveraging Acoustic Emission Sensing and Sequential Deep
Learning
Authors: Niamat Ullah, Muhammad Farooq Siddique, Saif Ullah, Zahoor Ahmad, Jong-Myon Kim
First page: 2318
Abstract: This study explores a novel approach utilizing acoustic emission (AE) signaling technology for pipeline leakage detection and analysis. Pipeline leaks are a significant concern in the liquids and gases industries, prompting the development of innovative detection methods. Unlike conventional methods, which often require contact and visual inspection with the pipeline surface, the proposed time-series-based deep learning approach offers real-time detection with higher safety and efficiency. In this study, we propose an automatic detection system of pipeline leakage for efficient transportation of liquid (water) and gas across the city, considering the smart city approach. We propose an AE-based framework combined with time-series deep learning algorithms to detect pipeline leaks through time-series features. The time-series AE signal detection module is designed to capture subtle changes in the AE signal state caused by leaks. Sequential deep learning models, including long short-term memory (LSTM), bi-directional LSTM (Bi-LSTM), and gated recurrent units (GRUs), are used to classify the AE response into normal and leakage detection from minor seepage, moderate leakage, and major ruptures in the pipeline. Three AE sensors are installed at different configurations on a pipeline, and data are acquired at 1 MHz sample/sec, which is decimated to 4K sample/second for efficiently utilizing the memory constraints of a remote system. The performance of these models is evaluated using metrics, namely accuracy, precision, recall, F1 score, and convergence, demonstrating classification accuracies of up to 99.78%. An accuracy comparison shows that BiLSTM performed better mostly with all hyperparameter settings. This research contributes to the advancement of pipeline leakage detection technology, offering improved accuracy and reliability in identifying and addressing pipeline integrity issues.
Citation: Smart Cities
PubDate: 2024-08-20
DOI: 10.3390/smartcities7040091
Issue No: Vol. 7, No. 4 (2024)
- Smart Cities, Vol. 7, Pages 973-990: Evaluating the Feasibility of
Intelligent Blind Road Junction V2I Deployments
Authors: Joseph Clancy, Dara Molloy, Sean Hassett, James Leahy, Enda Ward, Patrick Denny, Edward Jones, Martin Glavin, Brian Deegan
First page: 973
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-04-24
DOI: 10.3390/smartcities7030041
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 991-1006: 3D Point Cloud and GIS Approach to
Assess Street Physical Attributes
Authors: Patricio R. Orozco Carpio, María José Viñals, María Concepción López-González
First page: 991
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-04-25
DOI: 10.3390/smartcities7030042
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1007-1043: Off-Grid Electrification Using
Renewable Energy in the Philippines: A Comprehensive Review
Authors: Arizeo C. Salac, Jairus Dameanne C. Somera, Michael T. Castro, Maricor F. Divinagracia-Luzadas, Louis Angelo M. Danao, Joey D. Ocon
First page: 1007
Abstract: Universal access to electricity is beneficial for the socio-economic development of a country and the development of smart communities. Unfortunately, the electrification of remote off-grid areas, especially in developing countries, is rather slow due to geographic and economic barriers. In the Philippines, specifically, many electrified off-grid areas are underserved, with access to electricity being limited to only a few hours a day. This is mainly due to the high dependence on diesel power plants (DPPs) for electrifying these areas. To address these problems, hybrid renewable energy systems (HRESs) have been considered good electrification alternatives and have been extensively studied for their techno-economic and financial feasibility for Philippine off-grid islands. In this work, articles published from 2012 to 2023 focusing on off-grid Philippine rural electrification were reviewed and classified based on their topic. The taxonomical analysis of collected studies shows that there is a saturation of works focusing on the technical and economic aspects of off-grid electrification. Meanwhile, studies focusing on environmental and socio-political factors affecting HRES off-grid electrification are lagging. A bibliographic analysis of the reviewed articles also showed that there is still a lack of a holistic approach in studying off-grid electrification in the Philippines. There are only a few works that extend beyond the typical techno-economic study. Research works focusing on environmental and socio-political factors are also mainly isolated and do not cross over with technical papers. The gap between topic clusters should be addressed in future works on off-grid electrification.
Citation: Smart Cities
PubDate: 2024-04-26
DOI: 10.3390/smartcities7030043
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1044-1059: Smart Cities for All' Bridging
Digital Divides for Socially Sustainable and Inclusive Cities
Authors: Johan Colding, Caroline Nilsson, Stefan Sjöberg
First page: 1044
Abstract: This paper aims to emphasize the need for enhancing inclusivity and accessibility within smart-city societies. It represents the first attempt to apply Amartya Sen’s capability approach by exploring the implications of digital divides for promoting inclusive and climate-friendly cities that prioritize well-being, equity, and societal participation. Sen’s framework recognizes individual variations in converting resources into valuable ‘functionings’, and herein emphasizes the importance of aligning personal, social, and environmental conversion factors for individuals to fully navigate, participate in, and enjoy the benefits provided by smart cities. Adopting the capability approach and employing a cross-disciplinary analysis of the scientific literature, the primary objective is to broaden understanding of how to improve inclusivity and accessibility within smart-city societies, with a specific focus on marginalized community members facing first- and second-level digital divides. This paper underscores the importance of adopting a systemic perspective on climate-smart city navigation and stresses the importance of establishing a unified governing body responsible for monitoring, evaluating, and enhancing smart-city functionality. The paper concludes by summarizing some policy recommendations to boost social inclusion and address climate change in smart cities, such as creating capability-enhancing institutions, safeguarding redundancy in public-choice options, empowering citizens, and leveraging academic knowledge in smart-city policy formulation.
Citation: Smart Cities
PubDate: 2024-05-03
DOI: 10.3390/smartcities7030044
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1060-1088: Combined Optimisation of Traffic
Light Control Parameters and Autonomous Vehicle Routes
Authors: Mariano Gallo
First page: 1060
Abstract: In the near future, fully autonomous vehicles may revolutionise mobility and contribute to the development of the smart city concept. In this work, we assume that vehicles are not only fully autonomous but also centrally controlled by a single operator, who can also define the traffic light control parameters at intersections. With the aim of optimising the system to achieve a global optimum, the operator can define both the routes of the fleet of vehicles and the traffic light control parameters. This paper proposes a model for the joint optimisation of traffic light control parameters and autonomous vehicle routes to achieve the system optimum. The model, which is solved using a gradient algorithm, is tested on networks of different sizes. The results obtained show the validity of the proposed approach and the advantages of centralised management of vehicles and intersection control parameters.
Citation: Smart Cities
PubDate: 2024-05-03
DOI: 10.3390/smartcities7030045
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1089-1108: Integration of Smart City
Technologies with Advanced Predictive Analytics for Geotechnical
Investigations
Authors: Yuxin Cong, Shinya Inazumi
First page: 1089
Abstract: This paper addresses challenges and solutions in urban development and infrastructure resilience, particularly in the context of Japan’s rapidly urbanizing landscape. It explores the integration of smart city concepts to combat land subsidence and liquefaction, phenomena highlighted by the 2011 Great East Japan Earthquake. Additionally, it examines the current situation and lack of geoinformation and communication technology in the concept of smart cities in Japan. Consequently, this study employs advanced technologies, including smart sensing and predictive analytics through kriging and ensemble learning, with the objective of enhancing the precision of geotechnical investigations and urban planning. By analyzing data in Setagaya, Tokyo, it develops predictive models to accurately determine the depth of bearing layers that are critical to urban infrastructure. The results demonstrate the superiority of ensemble learning in predicting the depth of bearing layers. Two methods have been developed to predict undetected geographic data and prepare ground reality and digital smart maps for the construction industry to build smart cities. This study is useful for real-time analysis of existing data, for the government to make new urban plans, for construction companies to conduct risk assessments before doing their jobs, and for individuals to obtain real-time geographic data and hazard warnings through mobile phones and other means in the future. To the best of our knowledge, this is the first instance of predictive analysis of geographic information being conducted through geographic information, big data technology, machine learning, integrated learning, and artificial intelligence.
Citation: Smart Cities
PubDate: 2024-05-06
DOI: 10.3390/smartcities7030046
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1109-1125: Smart Delivery Assignment through
Machine Learning and the Hungarian Algorithm
Authors: Juan Pablo Vásconez, Elias Schotborgh, Ingrid Nicole Vásconez, Viviana Moya, Andrea Pilco, Oswaldo Menéndez, Robert Guamán-Rivera, Leonardo Guevara
First page: 1109
Abstract: Intelligent transportation and advanced mobility techniques focus on helping operators to efficiently manage navigation tasks in smart cities, enhancing cost efficiency, increasing security, and reducing costs. Although this field has seen significant advances in developing large-scale monitoring of smart cities, several challenges persist concerning the practical assignment of delivery personnel to customer orders. To address this issue, we propose an architecture to optimize the task assignment problem for delivery personnel. We propose the use of different cost functions obtained with deterministic and machine learning techniques. In particular, we compared the performance of linear and polynomial regression methods to construct different cost functions represented by matrices with orders and delivery people information. Then, we applied the Hungarian optimization algorithm to solve the assignment problem, which optimally assigns delivery personnel and orders. The results demonstrate that when used to estimate distance information, linear regression can reduce estimation errors by up to 568.52 km (1.51%) for our dataset compared to other methods. In contrast, polynomial regression proves effective in constructing a superior cost function based on time information, reducing estimation errors by up to 17,143.41 min (11.59%) compared to alternative methods. The proposed approach aims to enhance delivery personnel allocation within the delivery sector, thereby optimizing the efficiency of this process.
Citation: Smart Cities
PubDate: 2024-05-12
DOI: 10.3390/smartcities7030047
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1126-1148: Enabling Alarm-Based Fault
Prediction for Smart Meters in District Heating Systems: A Danish Case
Study
Authors: Henrik Alexander Nissen Søndergaard, Hamid Reza Shaker, Bo Nørregaard Jørgensen
First page: 1126
Abstract: District heating companies utilize smart meters that generate alarms that indicate faults in their sensors and installations. If these alarms are not tended to, the data cannot be trusted, and the applications that utilize them will not perform properly. Currently, smart meter data are mostly used for billing, and the district heating company is obligated to ensure the data quality. Here, retrospective correction of data is possible using the alarms; however, identification of sensor problems earlier can help improve the data quality. This paper is undertaken in collaboration with a district heating company in which not all of these alarms are tended to. This is due to various barriers and misconceptions. A shift in perspective must happen, both to utilize the current alarms more efficiently and to permit the incorporation of predictive capabilities of alarms to enable smart solutions in the future and improve data quality now. This paper proposes a prediction framework for one of the alarms in the customer installation. The framework can predict sensor faults to a high degree with a precision of 88% and a true positive rate of 79% over a prediction horizon of 24 h. The framework uses a modified definition of an alarm and was tested using a selection of machine learning methods with the optimization of hyperparameters and an investigation into prediction horizons. To the best of our knowledge, this is the first instance of such a methodology.
Citation: Smart Cities
PubDate: 2024-05-14
DOI: 10.3390/smartcities7030048
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1149-1168: Redesigning Municipal Waste
Collection for Aging and Shrinking Communities
Authors: Andante Hadi Pandyaswargo, Chaoxia Shan, Akihisa Ogawa, Ryota Tsubouchi, Hiroshi Onoda
First page: 1149
Abstract: Due to aging and depopulation, cities in Japan struggle to maintain their municipal waste collection services. These challenges were exacerbated by the pandemic. To overcome these challenges, a prototype of collective and contactless waste collection technology has been developed. However, its acceptance by society is unknown. In this study, we surveyed Japanese people’s preferences regarding household waste disposal. The results showed that older adults (older than 60) are willing to walk longer (more than 2 min) to carry their waste to the disposal site than younger adults. They are also less concerned about the risk of disease infection from touching other people’s garbage than younger respondents (at a 0.24 count ratio). Other significant findings are that people who live alone prefer the temporary disposal site to be placed more than one minute away from their house (at a 0.19 count ratio). People living alone also produce less plastic and packaging waste than larger households. With more Japanese older adults living alone because of the scarcity of older-adult care facilities, we proposed two waste collection strategies that can allow for the implementation of more collective and automatized contactless waste pickup technology. Each design poses different challenges, such as the need for residents’ cooperation and a higher energy supply. However, they also open new opportunities, such as encouraging active aging and using renewable energy.
Citation: Smart Cities
PubDate: 2024-05-16
DOI: 10.3390/smartcities7030049
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1169-1198: Camera-Based Crime Behavior
Detection and Classification
Authors: Jerry Gao, Jingwen Shi, Priyanka Balla, Akshata Sheshgiri, Bocheng Zhang, Hailong Yu, Yunyun Yang
First page: 1169
Abstract: Increasing numbers of public and private locations now have surveillance cameras installed to make those areas more secure. Even though many organizations still hire someone to monitor the cameras, the person hired is more likely to miss some unexpected events in the video feeds because of human error. Several researchers have worked on surveillance data and have presented a number of approaches for automatically detecting aberrant events. To keep track of all the video data that accumulate, a supervisor is often required. To analyze the video data automatically, we recommend using neural networks to identify the crimes happening in the real world. Through our approach, it will be easier for police agencies to discover and assess criminal activity more quickly using our method, which will reduce the burden on their staff. In this paper, we aim to provide anomaly detection using surveillance videos as input specifically for the crimes of arson, burglary, stealing, and vandalism. It will provide an efficient and adaptable crime-detection system if integrated across the smart city infrastructure. In our project, we trained multiple accurate deep learning models for object detection and crime classification for arson, burglary and vandalism. For arson, the videos were trained using YOLOv5. Similarly for burglary and vandalism, we trained using YOLOv7 and YOLOv6, respectively. When the models were compared, YOLOv7 performed better with the highest mAP of 87. In this, we could not compare the model’s performance based on crime type because all the datasets for each crime type varied. So, for arson YOLOv5 performed well with 80% mAP and for vandalism, YOLOv6 performed well with 86% mAP. This paper designed an automatic identification of crime types based on camera or surveillance video in the absence of a monitoring person, and alerts registered users about crimes such as arson, burglary, and vandalism through an SMS service. To detect the object of the crime in the video, we trained five different machine learning models: Improved YOLOv5 for arson, Faster RCNN and YOLOv7 for burglary, and SSD MobileNet and YOLOv6 for vandalism. Other than improved models,we innovated by building ensemble models of all three crime types. The main aim of the project is to provide security to the society without human involvement and make affordable surveillance cameras to detect and classify crimes. In addition, we implemented the Web system design using the built package in Python, which is Gradio. This helps the registered user of the Twilio communication tool to receive alert messages when any suspicious activity happens around their communities.
Citation: Smart Cities
PubDate: 2024-05-19
DOI: 10.3390/smartcities7030050
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1199-1220: Exploring Sustainable Urban
Transportation: Insights from Shared Mobility Services and Their
Environmental Impact
Authors: Ada Garus, Andromachi Mourtzouchou, Jaime Suarez, Georgios Fontaras, Biagio Ciuffo
First page: 1199
Abstract: The transportation landscape is witnessing profound changes due to technological advancements, necessitating proactive policy responses to harness innovation and avert urban mobility disruption. The sharing economy has already transformed ridesharing, bicycle-sharing, and electric scooters, with shared autonomous vehicles (SAVs) poised to reshape car ownership. This study pursues two objectives: firstly, to establish a market segmentation for shared ride services and secondly, to evaluate the environmental impact of ridesharing in different contexts. To mitigate potential biases linked to stated preference data, we analysed the navette service, utilized by a research institute in Europe, closely resembling future SAVs. The market segmentation relied on hierarchical cluster analysis using employee survey responses, while the environmental analysis was grounded in the 2019 navette service data. Our analysis revealed four unique employee clusters: Cluster 1, emphasizing active transportation and environmental awareness; Cluster 2, showing openness towards SAVs given reliable alternatives are available; Cluster 3, the largest segment, highlighting a demand for policy support and superior service quality; and Cluster 4, which places a premium on time, suggesting a potential need for strategies to make the service more efficient and, consequently, discourage private car use. These findings highlight a general willingness to adopt shared transport modes, signalling a promising transition to shared vehicle ownership with significant environmental benefits achievable through service design and policy measures.
Citation: Smart Cities
PubDate: 2024-05-20
DOI: 10.3390/smartcities7030051
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1221-1260: Measuring and Assessing the Level
of Living Conditions and Quality of Life in Smart Sustainable Cities in
Poland—Framework for Evaluation Based on MCDM Methods
Authors: Jarosław Brodny, Magdalena Tutak, Peter Bindzár
First page: 1221
Abstract: The increasing degree of urbanization of the world community is creating several multidimensional challenges for modern cities in terms of the need to provide adequate living and working conditions for their residents. An opportunity to ensure optimal conditions and quality of life are smart sustainable cities, which integrate various resources for their sustainable development using modern and smart technological solutions. This paper addresses these issues by presenting the results of a study of the level and quality of living conditions in the 29 largest cities in Poland, an EU member state. This study used 35 indicators characterizing the six main areas of activity of the cities to assess the living conditions and quality of life in these cities. To achieve this purpose, an original research methodology was developed, in which the EDAS and WASPAS methods and the Laplace criterion were applied. The application of a multi-criteria approach to the issue under study made it possible to determine the levels of quality of life and living conditions in the studied cities for each dimension, as well as the final index of this assessment (Smart Sustainable Cities Assessment Scores). On this basis, a ranking of these cities was made. In addition, relationships between living conditions and quality of life and the levels of wealth and population of the cities were also assessed. The results showed a wide variation in the levels of living conditions and quality of life in the cities studied, as well as their independence from geographic location. Cities with higher GDP levels that were investing in innovation and knowledge-based development fared much better.
Citation: Smart Cities
PubDate: 2024-05-22
DOI: 10.3390/smartcities7030052
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1261-1288: Radiometric Infrared Thermography
of Solar Photovoltaic Systems: An Explainable Predictive Maintenance
Approach for Remote Aerial Diagnostic Monitoring
Authors: Usamah Rashid Qureshi, Aiman Rashid, Nicola Altini, Vitoantonio Bevilacqua, Massimo La Scala
First page: 1261
Abstract: Solar photovoltaic (SPV) arrays are crucial components of clean and sustainable energy infrastructure. However, SPV panels are susceptible to thermal degradation defects that can impact their performance, thereby necessitating timely and accurate fault detection to maintain optimal energy generation. The considered case study focuses on an intelligent fault detection and diagnosis (IFDD) system for the analysis of radiometric infrared thermography (IRT) of SPV arrays in a predictive maintenance setting, enabling remote inspection and diagnostic monitoring of the SPV power plant sites. The proposed IFDD system employs a custom-developed deep learning approach which relies on convolutional neural networks for effective multiclass classification of defect types. The diagnosis of SPV panels is a challenging task for issues such as IRT data scarcity, defect-patterns’ complexity, and low thermal image acquisition quality due to noise and calibration issues. Hence, this research carefully prepares a customized high-quality but severely imbalanced six-class thermographic radiometric dataset of SPV panels. With respect to previous approaches, numerical temperature values in floating-point are used to train and validate the predictive models. The trained models display high accuracy for efficient thermal anomaly diagnosis. Finally, to create a trust in the IFDD system, the process underlying the classification model is investigated with perceptive explainability, for portraying the most discriminant image features, and mathematical-structure-based interpretability, to achieve multiclass feature clustering.
Citation: Smart Cities
PubDate: 2024-05-28
DOI: 10.3390/smartcities7030053
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1289-1303: Towards Municipal Data Utilities:
Experiences Regarding the Development of a Municipal Data Utility for
Intra- and Intermunicipal Actors within the German City of Mainz
Authors: Philipp Lämmel, Jonas Merbeth, Tim Cleffmann, Lukas Koch
First page: 1289
Abstract: This paper describes the requirements analysis phase towards the establishment and implementation of a municipal data utility (KDW = Kommunales Datenwerk, German) to facilitate data sharing between intra- and intermunicipal stakeholders. Against the backdrop of increasing digitisation and the growing importance of data-driven decision making in municipal governance, this paper aims to address the pressing need for efficient data management solutions within and across municipalities. Based on a structured self-developed methodology, the authors use a qualitative research approach: the paper examines the experiences and challenges encountered during the requirements phase, the design phase, and the development phase of the KDW. The findings indicate that the establishment of a robust KDW requires (1) extensive stakeholder engagement, (2) clear governance structures, and (3) a robust technical infrastructure. In addition, the study highlights the critical importance of establishing a sound legal framework that addresses data ownership, privacy, security and regulatory compliance. Addressing legal and regulatory barriers to data sharing is paramount to the successful implementation and operation of the KDW. The paper concludes by highlighting the potential benefits of KDWs and outlining future work. The overall methodology, approach, and outcome are validated within the city of Mainz, and the lessons learned are accommodated in the insights presented in the rest of the paper.
Citation: Smart Cities
PubDate: 2024-05-28
DOI: 10.3390/smartcities7030054
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1304-1329: Effectiveness of the Fuzzy Logic
Control to Manage the Microclimate Inside a Smart Insulated Greenhouse
Authors: Jamel Riahi, Hamza Nasri, Abdelkader Mami, Silvano Vergura
First page: 1304
Abstract: Agricultural greenhouses incorporate intricate systems to regulate the internal climate. Among the crucial climatic variables, indoor temperature and humidity take precedence in establishing an optimal environment for plant production and growth. The present research emphasizes the efficacy of employing intelligent control systems in the automation of the indoor climate for smart insulated greenhouses (SIGs), utilizing a fuzzy logic controller (FLC). This paper proposes the use of an FLC to reduce the energy consumption of a greenhouse. In the first step, a thermodynamic model is presented and experimentally validated based on thermal heat exchanges between the indoor and outdoor climatic variables. The outcomes show the effectiveness of the proposed model in controlling indoor air temperature and relative humidity with a low error percentage. Secondly, several fuzzy logic control models have been developed to regulate the indoor temperature and humidity for cold and hot periods. The results show the good performance of the proposed FLC model as highlighted by the statistical analysis. In fact, the root mean squared error (RMSE) is very small and equal to 0.69% for temperature and 0.23% for humidity, whereas the efficiency factor (EF) of the fuzzy logic control is equal to 99.35% for temperature control and 99.86% for humidity control.
Citation: Smart Cities
PubDate: 2024-06-06
DOI: 10.3390/smartcities7030055
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1330-1345: Characterizing Smart Cities Based
on Artificial Intelligence
Authors: Laaziza Hammoumi, Mehdi Maanan, Hassan Rhinane
First page: 1330
Abstract: Cities worldwide are attempting to be labelled as smart, but truly classifying as such remains a great challenge. This study aims to use artificial intelligence (AI) to classify the performance of smart cities and identify the factors linked to their smartness. Based on residents’ perceptions of urban structures and technological applications, this study included 200 cities globally. For 147 cities, we gathered the perceptions of 120 residents per city through a survey of 39 questions covering two main pillars: ‘Structures’, referring to the existing infrastructure of the city, and the ‘Technology’ pillar that describes the technological provisions and services available to the inhabitants. These pillars were evaluated across five key areas: health and safety, mobility, activities, opportunities, and governance. For the remaining 53 cities, scores were derived by analyzing pertinent data collected from various online resources. Multiple machine learning algorithms, including Random Forest, Artificial Neural Network, Support Vector Machine, and Gradient Boost, were tested and compared in order to select the best one. The results showed that Random Forest and the Artificial Neural Network are the best trained models that achieved the highest levels of accuracy. This study provides a robust framework for using machine learning to identify and assess smart cities, offering valuable insights for future research and urban planning.
Citation: Smart Cities
PubDate: 2024-06-07
DOI: 10.3390/smartcities7030056
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1346-1389: Artificial Intelligence in Smart
Cities—Applications, Barriers, and Future Directions: A Review
Authors: Radosław Wolniak, Kinga Stecuła
First page: 1346
Abstract: As urbanization continues to pose new challenges for cities around the world, the concept of smart cities is a promising solution, with artificial intelligence (AI) playing a central role in this transformation. This paper presents a literature review of AI solutions applied in smart cities, focusing on its six main areas: smart mobility, smart environment, smart governance, smart living, smart economy, and smart people. The analysis covers publications from 2021 to 2024 available on Scopus. This paper examines the application of AI in each area and identifies barriers, advances, and future directions. The authors set the following goals of the analysis: (1) to identify solutions and applications using artificial intelligence in smart cities; (2) to identify the barriers to implementation of artificial intelligence in smart cities; and (3) to explore directions of the usage of artificial intelligence in smart cities.
Citation: Smart Cities
PubDate: 2024-06-10
DOI: 10.3390/smartcities7030057
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1390-1413: Optimization of Geothermal Heat
Pump Systems for Sustainable Urban Development in Southeast Asia
Authors: Thiti Chanchayanon, Susit Chaiprakaikeow, Apiniti Jotisankasa, Shinya Inazumi
First page: 1390
Abstract: This study examines the optimization of ground source heat pump (GSHP) systems and energy piles for sustainable urban development, focusing on Southeast Asia. GSHPs, which utilize geothermal energy for indoor HVAC needs, offer a sustainable alternative to traditional systems by utilizing consistent subsurface temperatures for heating and cooling. The study highlights the importance of understanding thermal movement within the soil, especially in soft marine clays prevalent in Southeast Asia, to improve GSHP system efficiency. Using a one-dimensional finite difference model, the study examines the effects of soil thermal conductivity and density on system performance. The results show that GSHP systems, especially when integrated with energy piles, significantly reduce electricity consumption and greenhouse gas emissions, underscoring their potential to mitigate the urban heat island effect in densely populated areas. Despite challenges posed by the region’s hot and humid climate, which could affect long-term effectiveness, the study highlights the need for further study, including field experiments and advanced modeling techniques, to optimize GSHP configurations and fully exploit geothermal energy in urban environments. The study’s insights into soil thermal dynamics and system design optimization contribute to advancing sustainable urban infrastructure development.
Citation: Smart Cities
PubDate: 2024-06-12
DOI: 10.3390/smartcities7030058
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1414-1440: Turning Features Detection from
Aerial Images: Model Development and Application on Florida’s Public
Roadways
Authors: Antwi, Kimollo, Takyi, Ozguven, Sando, Moses, Dulebenets
First page: 1414
Abstract: Advancements in computer vision are rapidly revolutionizing the way traffic agencies gather roadway geometry data, leading to significant savings in both time and money. Utilizing aerial and satellite imagery for data collection proves to be more cost-effective, more accurate, and safer compared to traditional field observations, considering factors such as equipment cost, crew safety, and data collection efficiency. Consequently, there is a pressing need to develop more efficient methodologies for promptly, safely, and economically acquiring roadway geometry data. While image processing has previously been regarded as a time-consuming and error-prone approach for capturing these data, recent developments in computing power and image recognition techniques have opened up new avenues for accurately detecting and mapping various roadway features from a wide range of imagery data sources. This research introduces a novel approach combining image processing with a YOLO-based methodology to detect turning lane pavement markings from high-resolution aerial images, specifically focusing on Florida’s public roadways. Upon comparison with ground truth data from Leon County, Florida, the developed model achieved an average accuracy of 87% at a 25% confidence threshold for detected features. Implementation of the model in Leon County identified approximately 3026 left turn, 1210 right turn, and 200 center lane features automatically. This methodology holds paramount significance for transportation agencies in facilitating tasks such as identifying deteriorated markings, comparing turning lane positions with other roadway features like crosswalks, and analyzing intersection-related accidents. The extracted roadway geometry data can also be seamlessly integrated with crash and traffic data, providing crucial insights for policymakers and road users.
Citation: Smart Cities
PubDate: 2024-06-13
DOI: 10.3390/smartcities7030059
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1441-1461: Methodology for Identifying Optimal
Pedestrian Paths in an Urban Environment: A Case Study of a School
Environment in A Coruña, Spain
Authors: David Fernández-Arango, Francisco-Alberto Varela-García, Alberto M. Esmorís
First page: 1441
Abstract: Improving urban mobility, especially pedestrian mobility, is a current challenge in virtually every city worldwide. To calculate the least-cost paths and safer, more efficient routes, it is necessary to understand the geometry of streets and their various elements accurately. In this study, we propose a semi-automatic methodology to assess the capacity of urban spaces to enable adequate pedestrian mobility. We employ various data sources, but primarily point clouds obtained through a mobile laser scanner (MLS), which provide a wealth of highly detailed information about the geometry of street elements. Our method allows us to characterize preferred pedestrian-traffic zones by segmenting crosswalks, delineating sidewalks, and identifying obstacles and impediments to walking in urban routes. Subsequently, we generate different displacement cost surfaces and identify the least-cost origin–destination paths. All these factors enable a detailed pedestrian mobility analysis, yielding results on a raster with a ground sampling distance (GSD) of 10 cm/pix. The method is validated through its application in a case study analyzing pedestrian mobility around an educational center in a purely urban area of A Coruña (Galicia, Spain). The segmentation model successfully identified all pedestrian crossings in the study area without false positives. Additionally, obstacle segmentation effectively identified urban elements and parked vehicles, providing crucial information to generate precise friction surfaces reflecting real environmental conditions. Furthermore, the generation of cumulative displacement cost surfaces allowed for identifying optimal routes for pedestrian movement, considering the presence of obstacles and the availability of traversable spaces. These surfaces provided a detailed representation of pedestrian mobility, highlighting significant variations in travel times, especially in areas with high obstacle density, where differences of up to 15% were observed. These results underscore the importance of considering obstacles’ existence and location when planning pedestrian routes, which can significantly influence travel times and route selection. We consider the capability to generate accurate cumulative cost surfaces to be a significant advantage, as it enables urban planners and local authorities to make informed decisions regarding the improvement of pedestrian infrastructure.
Citation: Smart Cities
PubDate: 2024-06-14
DOI: 10.3390/smartcities7030060
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 1462-1500: A Review of IoT-Based Smart City
Development and Management
Authors: Mostafa Zaman, Nathan Puryear, Sherif Abdelwahed, Nasibeh Zohrabi
First page: 1462
Abstract: Smart city initiatives aim to enhance urban domains such as healthcare, transportation, energy, education, environment, and logistics by leveraging advanced information and communication technologies, particularly the Internet of Things (IoT). While IoT integration offers significant benefits, it also introduces unique challenges. This paper provides a comprehensive review of IoT-based management in smart cities. It includes a discussion of a generalized architecture for IoT in smart cities, evaluates various metrics to assess the success of smart city projects, explores standards pertinent to these initiatives, and delves into the challenges encountered in implementing smart cities. Furthermore, the paper examines real-world applications of IoT in urban management, highlighting their advantages, practical impacts, and associated challenges. The research methodology involves addressing six key questions to explore IoT architecture, impacts on efficiency and sustainability, insights from global examples, critical standards, success metrics, and major deployment challenges. These findings offer valuable guidance for practitioners and policymakers in developing effective and sustainable smart city initiatives. The study significantly contributes to academia by enhancing knowledge, offering practical insights, and highlighting the importance of interdisciplinary research for urban innovation and sustainability, guiding future initiatives towards more effective smart city solutions.
Citation: Smart Cities
PubDate: 2024-06-20
DOI: 10.3390/smartcities7030061
Issue No: Vol. 7, No. 3 (2024)
- Smart Cities, Vol. 7, Pages 712-734: Contextualizing the Smart City in
Africa: Balancing Human-Centered and Techno-Centric Perspectives for Smart
Urban Performance
Authors: Nessrine Moumen, Hassan Radoine, Kh Md Nahiduzzaman, Hassane Jarar Oulidi
First page: 712
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-02-27
DOI: 10.3390/smartcities7020029
Issue No: Vol. 7, No. 2 (2024)
- Smart Cities, Vol. 7, Pages 735-757: Enhancing Waste-to-Energy and
Hydrogen Production through Urban–Industrial Symbiosis: A
Multi-Objective Optimisation Model Incorporating a Bayesian Best-Worst
Method
Authors: Alessandro Neri, Maria Angela Butturi, Francesco Lolli, Rita Gamberini
First page: 735
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-02-28
DOI: 10.3390/smartcities7020030
Issue No: Vol. 7, No. 2 (2024)
- Smart Cities, Vol. 7, Pages 758-771: Video Compression Prototype for
Autonomous Vehicles
Authors: Yair Wiseman
First page: 758
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-03-08
DOI: 10.3390/smartcities7020031
Issue No: Vol. 7, No. 2 (2024)
- Smart Cities, Vol. 7, Pages 772-785: Reduced Complexity Sequential Digital
Predistortion Technique for 5G Applications
Authors: Moustafa Abdelnaby, Reem Alnajjar, Souheil Bensmida, Oualid Hammi
First page: 772
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-03-18
DOI: 10.3390/smartcities7020032
Issue No: Vol. 7, No. 2 (2024)
- Smart Cities, Vol. 7, Pages 786-805: Integrating Multi-Criteria Decision
Models in Smart Urban Planning: A Case Study of Architectural and Urban
Design Competitions
Authors: Tomaž Berčič, Marko Bohanec, Lucija Ažman Momirski
First page: 786
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-03-18
DOI: 10.3390/smartcities7020033
Issue No: Vol. 7, No. 2 (2024)
- Smart Cities, Vol. 7, Pages 806-835: Exploring the Symbiotic Relationship
between Digital Transformation, Infrastructure, Service Delivery, and
Governance for Smart Sustainable Cities
Authors: Dillip Kumar Das
First page: 806
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-03-25
DOI: 10.3390/smartcities7020034
Issue No: Vol. 7, No. 2 (2024)
- Smart Cities, Vol. 7, Pages 836-858: Multifunctional Models in Digital and
Physical Twinning of the Built Environment—A University Campus Case
Study
Authors: Genda Chen, Ibrahim Alomari, Woubishet Zewdu Taffese, Zhenhua Shi, Mohammad Hossein Afsharmovahed, Tarutal Ghosh Mondal, Son Nguyen
First page: 836
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-03-26
DOI: 10.3390/smartcities7020035
Issue No: Vol. 7, No. 2 (2024)
- Smart Cities, Vol. 7, Pages 859-879: Optimizing Energy Consumption in
Agricultural Greenhouses: A Smart Energy Management Approach
Authors: Fatemeh Jamshidi, Mohammad Ghiasi, Mehran Mehrandezh, Zhanle Wang, Raman Paranjape
First page: 859
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-03-28
DOI: 10.3390/smartcities7020036
Issue No: Vol. 7, No. 2 (2024)
- Smart Cities, Vol. 7, Pages 880-912: A Spatiotemporal Comparative Analysis
of Docked and Dockless Shared Micromobility Services
Authors: Hassam, Alpalhão, Neto
First page: 880
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-04-05
DOI: 10.3390/smartcities7020037
Issue No: Vol. 7, No. 2 (2024)
- Smart Cities, Vol. 7, Pages 913-931: Gauging Public Acceptance of
Conditionally Automated Vehicles in the United States
Authors: Antonios Saravanos, Eleftheria K. Pissadaki, Wayne S. Singh, Donatella Delfino
First page: 913
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-04-12
DOI: 10.3390/smartcities7020038
Issue No: Vol. 7, No. 2 (2024)
- Smart Cities, Vol. 7, Pages 932-945: An Update on Passenger Vehicle Speeds
at Roundabouts
Authors: Enrique D. Saldivar-Carranza, Myles W. Overall, Darcy M. Bullock
First page: 932
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-04-17
DOI: 10.3390/smartcities7020039
Issue No: Vol. 7, No. 2 (2024)
- Smart Cities, Vol. 7, Pages 946-972: Visionary Nature-Based Solutions
Evaluated through Social Return on Investment: The Case Study of an
Italian Urban Green Space
Authors: Elisa-Elena Vasiliu, Sara Torabi Moghadam, Adriano Bisello, Patrizia Lombardi
First page: 946
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-04-20
DOI: 10.3390/smartcities7020040
Issue No: Vol. 7, No. 2 (2024)
- Smart Cities, Vol. 7, Pages 141-162: A Review on Key Innovation Challenges
for Smart City Initiatives
Authors: Rui José, Helena Rodrigues
First page: 141
Abstract: Smart city initiatives are being promoted across the world to address major urban challenges, and they all share a common belief in the transformative power of digital technologies. However, the pace of innovation in smart cities seems to be much slower than the rapid and profoundly disruptive transformations brought about by digital innovation in many other domains. To develop new insights about the main causes behind this relatively modest success, this study provides a Systematic Literature Review (SLR) on the connection between major smart city challenges and the essential properties of digital innovation. The review involved the qualitative analysis of 44 research papers reporting on smart city innovation practices and outcomes. The results characterize five major challenge categories for smart city innovation: Strategic vision; Organizational Capabilities and Agility; Technology Domestication; Ecosystem Development; and Transboundary Innovation. This study also explores the connections between these challenges and concrete digital innovation practices in smart city initiatives. The main conclusion is that current innovation practices in smart cities are not properly aligned with what the research literature commonly describes as core properties of digital innovation and that this might be a major cause behind the limited progress in smart city initiatives.
Citation: Smart Cities
PubDate: 2024-01-02
DOI: 10.3390/smartcities7010006
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 163-178: Using Explainable Artificial
Intelligence (XAI) to Predict the Influence of Weather on the Thermal
Soaring Capabilities of Sailplanes for Smart City Applications
Authors: Maren Schnieder
First page: 163
Abstract: Background: Drones, also known as unmanned aerial vehicles, could potentially be a key part of future smart cities by aiding traffic management, infrastructure inspection and maybe even last mile delivery. This paper contributes to the research on managing a fleet of soaring aircraft by gaining an understanding of the influence of the weather on soaring capabilities. To do so, machine learning algorithms were trained on flight data, which was recorded in the UK over the past ten years at selected gliding clubs (i.e., sailplanes). Methods: A random forest regressor was trained to predict the flight duration and a random forest (RF) classifier was used to predict whether at least one flight on a given day managed to soar in thermals. SHAP (SHapley Additive exPlanations), a form of explainable artificial intelligence (AI), was used to understand the predictions given by the models. Results: The best RF have a mean absolute error of 5.7 min (flight duration) and an accuracy of 81.2% (probability of soaring in a thermal on a given day). The explanations derived from SHAP are in line with the common knowledge about the effect of weather systems to predict soaring potential. However, the key conclusion of this study is the importance of combining human knowledge with machine learning to devise a holistic explanation of a machine learning model and to avoid misinterpretations.
Citation: Smart Cities
PubDate: 2024-01-15
DOI: 10.3390/smartcities7010007
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 179-207: Delay and Energy Efficient Offloading
Strategies for an IoT Integrated Water Distribution System in Smart Cities
Authors: Nibi Kulangara Velayudhan, Aiswarya S, Aryadevi Remanidevi Devidas, Maneesha Vinodini Ramesh
First page: 179
Abstract: In the fast-moving world of information and communications technologies, one significant issue in metropolitan cities is water scarcity and the need for an intelligent water distribution system for sustainable water management. An IoT-based monitoring system can improve water distribution system management and mitigate challenges in the distribution network networks such as leakage, breakage, theft, overflow, dry running of pumps and so on. However, the increase in the number of communication and sensing devices within smart cities has evoked challenges to existing communication networks due to the increase in delay and energy consumption within the network. The work presents different strategies for efficient delay and energy offloading in IoT-integrated water distribution systems in smart cities. Different IoT-enabled communication network topology diagrams are proposed, considering the different water network design parameters, land cover patterns and wireless channels for communication. From these topologies and by considering all the relevant communication parameters, the optimum communication network architecture to continuously monitor a water distribution network in a metropolitan city in India is identified. As a case study, an IoT design and analysis model is studied for a secondary metropolitan city in India. The selected study area is in Kochi, India. Based on the site-specific model and land use and land cover pattern, delay and energy modeling of the IoT-based water distribution system is discussed. Algorithms for node categorisation and edge-to-fog allocation are discussed, and numerical analyses of delay and energy models are included. An approximation of the delay and energy of the network is calculated using these models. On the basis of these study results, and state transition diagrams, the optimum placement of fog nodes linked with edge nodes and a cloud server could be carried out. Also, by considering different scenarios, up to a 40% improvement in energy efficiency can be achieved by incorporating a greater number of states in the state transition diagram. These strategies could be utilized in implementing delay and energy-efficient IoT-enabled communication networks for site-specific applications.
Citation: Smart Cities
PubDate: 2024-01-16
DOI: 10.3390/smartcities7010008
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 208-232: Fit Islands: Designing a
Multifunctional Virtual Urban Community to Promote Healthy Aging for
Chinese Older Adults
Authors: Zixin Shen, Rongbo Hu, Dong Wan, Thomas Bock
First page: 208
Abstract: Within the context of an aging global population, the demographic structure of emerging economies is undergoing a dramatic transformation. Emerging economies have a large population base and rapid economic development, but they are ill-prepared to deal with population aging. Limited resources force many older adults to face health issues such as chronic diseases and loss of physical independence, exacerbating the burden of traditional family and societal elderly care. Uncontrollable events such as the COVID-19 pandemic and regional conflicts have exacerbated the plight of older adults. Improving the quality of life and health of older adults has become a development priority in emerging economies in the face of a rapidly aging population. The development of smart cities has brought with it many available digital technologies, and the consequent development of smart aging offers endless possibilities for improving the quality of life and health of older people, making cities more inclusive of older people. Researchers from developed economies have attempted to address the health issues of older adults through a technology that combines physical exercise and digital technology called Exergame. However, existing projects are not suitable for older adults in emerging economies due to differences in national conditions. The aim of this project is therefore to propose a universal approach to designing a health-promoting Exergame system in the format of a virtual urban community to help emerging economies cope with aging populations, making cities more inclusive. To verify the feasibility of this approach, the authors designed an expandable Exergame called “Fit Islands”, using China as a case study. Based on the initial demonstration, the authors conducted functional tests. The result is that Fit Islands can meet the development objective of motivating Chinese older people to increase their physical activity, providing initial evidence of the feasibility of an Exergame system to promote healthy aging in emerging economies. The application of Fit Islands demonstrates the feasibility of the universal Exergame development method, which can, in principle, provide comprehensive and practical guidance for other countries.
Citation: Smart Cities
PubDate: 2024-01-18
DOI: 10.3390/smartcities7010009
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 233-253: Urban Traffic Congestion Prediction:
A Multi-Step Approach Utilizing Sensor Data and Weather Information
Authors: Nikolaos Tsalikidis, Aristeidis Mystakidis, Paraskevas Koukaras, Marius Ivaškevičius, Lina Morkūnaitė, Dimosthenis Ioannidis, Paris A. Fokaides, Christos Tjortjis, Dimitrios Tzovaras
First page: 233
Abstract: The continuous growth of urban populations has led to the persistent problem of traffic congestion, which imposes adverse effects on quality of life, such as commute times, road safety, and the local air quality. Advancements in Internet of Things (IoT) sensor technology have contributed to a plethora of new data streams regarding traffic conditions. Therefore, the recognition and prediction of traffic congestion patterns utilizing such data have become crucial. To that end, the integration of Machine Learning (ML) algorithms can further enhance Intelligent Transportation Systems (ITS), contributing to the smart management of transportation systems and effectively tackling traffic congestion in cities. This study seeks to assess a wide range of models as potential solutions for an ML-based multi-step forecasting approach intended to improve traffic congestion prediction, particularly in areas with limited historical data. Various interpretable predictive algorithms, suitable for handling the complexity and spatiotemporal characteristics of urban traffic flow, were tested and eventually shortlisted based on their predictive performance. The forecasting approach selects the optimal model in each step to maximize the accuracy. The findings demonstrate that, in a 24 h step prediction, variating Ensemble Tree-Based (ETB) regressors like the Light Gradient Boosting Machine (LGBM) exhibit superior performances compared to traditional Deep Learning (DL) methods. Our work provides a valuable contribution to short-term traffic congestion predictions and can enable more efficient scheduling of daily urban transportation.
Citation: Smart Cities
PubDate: 2024-01-19
DOI: 10.3390/smartcities7010010
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 254-276: Efficient Decoder and Intermediate
Domain for Semantic Segmentation in Adverse Conditions
Authors: Xiaodong Chen, Nan Jiang, Yifeng Li, Guangliang Cheng, Zheng Liang, Zuobin Ying, Qi Zhang, Runsheng Zhao
First page: 254
Abstract: In smart city contexts, traditional methods for semantic segmentation are affected by adverse conditions, such as rain, fog, or darkness. One challenge is the limited availability of semantic segmentation datasets, specifically for autonomous driving in adverse conditions, and the high cost of labeling such datasets. To address this problem, unsupervised domain adaptation (UDA) is commonly employed. In UDA, the source domain contains data from good weather conditions, while the target domain contains data from adverse weather conditions. The Adverse Conditions Dataset with Correspondences (ACDC) provides reference images taken at different times but in the same location, which can serve as an intermediate domain, offering additional semantic information. In this study, we introduce a method that leverages both the intermediate domain and frequency information to improve semantic segmentation in smart city environments. Specifically, we extract the region with the largest difference in standard deviation and entropy values from the reference image as the intermediate domain. Secondly, we introduce the Fourier Exponential Decreasing Sampling (FEDS) algorithm to facilitate more reasonable learning of frequency domain information. Finally, we design an efficient decoder network that outperforms the DAFormer network by reducing network parameters by 28.00%. When compared to the DAFormer work, our proposed approach demonstrates significant performance improvements, increasing by 6.77%, 5.34%, 6.36%, and 5.93% in mean Intersection over Union (mIoU) for Cityscapes to ACDC night, foggy, rainy, and snowy, respectively.
Citation: Smart Cities
PubDate: 2024-01-19
DOI: 10.3390/smartcities7010011
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 277-301: Sensors in Civil Engineering: From
Existing Gaps to Quantum Opportunities
Authors: Boris Kantsepolsky, Itzhak Aviv
First page: 277
Abstract: The vital role of civil engineering is to enable the development of modern cities and establish foundations for smart and sustainable urban environments of the future. Advanced sensing technologies are among the instrumental methods used to enhance the performance of civil engineering infrastructures and address the multifaceted challenges of future cities. Through this study, we discussed the shortcomings of traditional sensors in four primary civil engineering domains: construction, energy, water, and transportation. Then, we investigated and summarized the potential of quantum sensors to contribute to and revolutionize the management of civil engineering infrastructures. For the water sector, advancements are expected in monitoring water quality and pressure in water and sewage infrastructures. In the energy sector, quantum sensors may facilitate renewables integration and improve grid stability and buildings’ energy efficiency. The most promising progress in the construction field is the ability to identify subsurface density and underground structures. In transportation, these sensors create many fresh avenues for real-time traffic management and smart mobility solutions. As one of the first-in-the-field studies offering the adoption of quantum sensors across four primary domains of civil engineering, this research establishes the basis for the discourse about the scope and timeline for deploying quantum sensors to real-world applications towards the quantum transformation of civil engineering.
Citation: Smart Cities
PubDate: 2024-01-22
DOI: 10.3390/smartcities7010012
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 302-324: Benchmarking the Functional,
Technical, and Business Characteristics of Automated Passenger Counting
Products
Authors: Pronello Cristina, Baratti Luca, Anbarasan Deepan
First page: 302
Abstract: Urban transport planning and the integration of various mobility options have become increasingly complex, necessitating a thorough understanding of user mobility patterns and their diverse needs. This paper focuses on benchmarking different Automatic Passenger Counting (APC) technologies, which play a key role in Mobility as a Service (MaaS) systems. APC systems provide valuable data for analysing mobility patterns and informing decisions about resource allocation. Our study presents a comprehensive data collection and benchmark analysis of APC solutions. The literature review emphasises the significance of passenger counting for transport companies and discusses various existing APC technologies, such as pressure sensors, wireless sensors, optical infrared sensors (IR), and video image technology. Real-world applications of APC systems are examined, highlighting experimental results and their potential for improving accuracy. The methodology outlines the data collection process, which involved identifying APC companies, conducting interviews with companies and customers, and administering an ad hoc survey to gather specific information about APC systems. The collected data were used to establish criteria and key performance indicators (KPIs) for the benchmarking analysis. The benchmarking analysis compares APC devices and companies based on ten criteria: technology, accuracy, environment, coverage, interface, interference, robustness (for devices), price, pricing model, and system integration (for companies). KPIs were developed to measure performance and make comparison easier. The results of the benchmarking analysis offer insights into the costs and accuracy of different APC systems, enabling informed decision making regarding system selection and implementation. The findings fill a research gap and provide valuable information for transport companies and policy makers, and we offer a comprehensive analysis of APC systems, highlighting their strengths, weaknesses, and business strategies. The paper concludes by discussing limitations and suggesting future research directions for APC technologies.
Citation: Smart Cities
PubDate: 2024-01-22
DOI: 10.3390/smartcities7010013
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 325-369: The Convergence of Intelligent
Tutoring, Robotics, and IoT in Smart Education for the Transition from
Industry 4.0 to 5.0
Authors: Amr Adel
First page: 325
Abstract: This review paper provides a comprehensive analysis of the automation of smart education in the context of Industry 5.0 from 78 papers, focusing on the integration of advanced technologies and the development of innovative, effective, and ethical educational solutions for the future workforce. As the world transitions into an era characterized by human–machine collaboration and rapidly evolving technologies, there is an urgent need to recognize the pivotal role of smart education in preparing individuals for the opportunities and challenges presented by the new industrial landscape. The paper examines key components of smart education, including intelligent tutoring systems, adaptive learning environments, learning analytics, and the application of the Internet of Things (IoT) in education. It also discusses the role of advanced technologies such as artificial intelligence (AI), machine learning (ML), robotics, and augmented and virtual reality (AR/VR) in shaping personalized and immersive learning experiences. The review highlights the importance of smart education in addressing the growing demand for upskilling and reskilling, fostering a culture of lifelong learning, and promoting adaptability, resilience, and self-improvement among learners. Furthermore, the paper delves into the challenges and ethical considerations associated with the implementation of smart education, addressing issues such as data privacy, the digital divide, teacher and student readiness, and the potential biases in AI-driven systems. Through a presentation of case studies and examples of successful smart education initiatives, the review aims to inspire educators, policymakers, and industry stakeholders to collaborate and innovate in the design and implementation of effective smart education solutions. Conclusively, the paper outlines emerging trends, future directions, and potential research opportunities in the field of smart education, emphasizing the importance of continuous improvement and the integration of new technologies to ensure that education remains relevant and effective in the context of Industry 5.0. By providing a holistic understanding of the key components, challenges, and potential solutions associated with smart education, this review paper seeks to contribute to the ongoing discourse surrounding the automation of smart education and its role in preparing the workforce for the future of work.
Citation: Smart Cities
PubDate: 2024-01-30
DOI: 10.3390/smartcities7010014
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 370-413: On the Adoption of Smart Home
Technology in Switzerland: Results from a Survey Study Focusing on
Prevention and Active Healthy Aging Aspects
Authors: Raphael Iten, Joël Wagner, Angela Zeier Röschmann
First page: 370
Abstract: Smart home (SH) technologies offer advancements in comfort, energy management, health, and safety. There is increasing interest in technology-enabled home services from scholars and professionals, particularly to meet the needs of a growing aging population. Yet, current research focuses on assisted living scenarios developed for elderly individuals with health impairments, and neglects to explore the potential of SHs in prevention. We aim to improve comprehension and guide future research on the value of SH technology for risk prevention with a survey assessing the adoption of SHs by older adults based on novel ad hoc collected data. Our survey is based on the theoretical background derived from the extant body of literature. In addition to established adoption factors and user characteristics, it includes previously unexamined elements such as active and healthy aging parameters, risk and insurance considerations, and social and hedonic dimensions. Descriptive results and regression analyses indicate that a vast majority of individuals acknowledge the preventive benefits of SHs. Additionally, we observe that individuals with higher levels of social activity, technology affinity, and knowledge of SHs tend to report greater interest. Moreover, perceived enjoyment and perceived risk emerge as central elements for SH adoption. Our research indicates that considering lifestyle factors when examining technology adoption and emphasizing the preventive benefits present possibilities for both future studies and practical implementations.
Citation: Smart Cities
PubDate: 2024-01-30
DOI: 10.3390/smartcities7010015
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 414-444: Smart Cities and Urban Energy
Planning: An Advanced Review of Promises and Challenges
Authors: Saeed Esfandi, Safiyeh Tayebi, John Byrne, Job Taminiau, Golkou Giyahchi, Seyed Ali Alavi
First page: 414
Abstract: This review explores the relationship between urban energy planning and smart city evolution, addressing three primary questions: How has research on smart cities and urban energy planning evolved in the past thirty years' What promises and hurdles do smart city initiatives introduce to urban energy planning' And why do some smart city projects surpass energy efficiency and emission reduction targets while others fall short' Based on a bibliometric analysis of 9320 papers published between January 1992 and May 2023, five dimensions were identified by researchers trying to address these three questions: (1) energy use at the building scale, (2) urban design and planning integration, (3) transportation and mobility, (4) grid modernization and smart grids, and (5) policy and regulatory frameworks. A comprehensive review of 193 papers discovered that previous research prioritized technological advancements in the first four dimensions. However, there was a notable gap in adequately addressing the inherent policy and regulatory challenges. This gap often led to smart city endeavors underperforming relative to their intended objectives. Overcoming the gap requires a better understanding of broader issues such as environmental impacts, social justice, resilience, safety and security, and the affordability of such initiatives.
Citation: Smart Cities
PubDate: 2024-01-31
DOI: 10.3390/smartcities7010016
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 445-459: Engaging Young People in the
Development of Innovative Nature-Inspired Technologies for Carbon
Sequestration in Cities: Case Studies from Portugal
Authors: Manuela Moreira da Silva, Lurdes Ferreira, Teresa Sarmento, Catarina Selada
First page: 445
Abstract: Currently, cities are the most vulnerable places on the planet to the effects of global change, both anthropogenic and climate-related, and this is not compatible with harmony and well-being regarding the economy, nature, and future generations. Young people have a unique potential to catalyze the transformative sustainable change that the planet needs now, as they are the first generation to grow up with tangible impacts of climate change. We tested a new strategy to empower young people to foster carbon neutrality in cities by engaging them in ecosystem services quantification and technological innovation to increase CO2 sequestration in two Portuguese cities. The species with best performance for carbon sequestration were M. exelsa in Porto and O. europea in Loulé, and for air pollutant removal and hydrological regulation were P. hispanica in Porto and P. pinea in Loulé. Through the innovative advanced summer program SLI, a nature-based learning experience, young people developed two new concepts of technological solutions to accelerate city decarbonization by designing a hedge for air pollution hotspots and a biodevice to be placed at bus stops using autochthonous shrubs and mosses. Initiatives like SLI contribute to a greater awareness among young people about the drivers that brought us to the current climate emergency, motivating them towards more balanced lifestyles and creating innovative nature-based solutions towards a smart and sustainable city.
Citation: Smart Cities
PubDate: 2024-01-31
DOI: 10.3390/smartcities7010017
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 460-474: Predicting Autonomous Driving
Behavior through Human Factor Considerations in Safety-Critical Events
Authors: Jamal Raiyn, Galia Weidl
First page: 460
Abstract: This paper investigates the ability of autonomous driving systems to predict outcomes by considering human factors like gender, age, and driving experience, particularly in the context of safety-critical events. The primary objective is to equip autonomous vehicles with the capacity to make plausible deductions, handle conflicting data, and adjust their responses in real-time during safety-critical situations. A foundational dataset, which encompasses various driving scenarios such as lane changes, merging, and navigating complex intersections, is employed to enable vehicles to exhibit appropriate behavior and make sound decisions in critical safety events. The deep learning model incorporates personalized cognitive agents for each driver, considering their distinct preferences, characteristics, and requirements. This personalized approach aims to enhance the safety and efficiency of autonomous driving, contributing to the ongoing development of intelligent transportation systems. The efforts made contribute to advancements in safety, efficiency, and overall performance within autonomous driving systems. To describe the causal relationship between external factors like weather conditions and human factors, and safety-critical driver behaviors, various data mining techniques can be applied. One commonly used method is regression analysis. Additionally, correlation analysis is employed to reveal relationships between different factors, helping to identify the strength and direction of their impact on safety-critical driver behavior.
Citation: Smart Cities
PubDate: 2024-02-01
DOI: 10.3390/smartcities7010018
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 475-495: The Cybersecurity Applied by Online
Travel Agencies and Hotels to Protect Users’ Private Data in Smart
Cities
Authors: Lázaro Florido-Benítez
First page: 475
Abstract: The purpose of this paper is to analyse the cybersecurity in online travel agencies (OTAs) and hotel sectors to protect users’ private data in smart cities. Methodologically, this research uses a sample of information about cyberattacks that occurred during the period of 2000–2023 in companies operating as OTAs and in the travel, tourism, and food sectors, which was obtained from research articles. Then, we had to expand the research to include updated information about cyberattacks from digital newspapers, regulatory sources, and state data breach notification sites like CSIS, KonBriefing, EUROCONTROL, and GlobalData. The findings of the current research prove that hotels and OTAs were constantly exposed to cyberattacks in the period analysed, especially by data breaches and malware attacks; in fact, this is the main novelty of this research. In addition, these incidents were severe for both guests and tourism companies because their vulnerabilities and consequences affect the reputation of companies and smart cities where these firms operate, as well as consumer confidence. The results also showed that most of the cyberattacks examined in this manuscript were aimed at stealing information about the companies’ and users’ private data such as email addresses; credit card numbers, security codes, and expiration dates; and encoded magstripe data; among many other types of data. Cyberattacks and cyberthreats never disappear completely in the travel and tourism sectors because these illegal activities are closely related to the hacker’s thirst for power, fame, and wealth.
Citation: Smart Cities
PubDate: 2024-02-04
DOI: 10.3390/smartcities7010019
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 496-517: An Artificial Intelligence and
Industrial Internet of Things-Based Framework for Sustainable Hydropower
Plant Operations
Authors: Fation T. Fera, Christos Spandonidis
First page: 496
Abstract: Hydropower plays a crucial role in supplying electricity to developed nations and is projected to expand its capacity in various developing countries such as Sub-Saharan Africa, Argentina, Colombia, and Turkey. With the increasing demand for sustainable energy and the emphasis on reducing carbon emissions, the significance of hydropower plants is growing. Nevertheless, numerous challenges arise for these plants due to their aging infrastructure, impacting both their efficiency and structural stability. In order to tackle these issues, the present study has formulated a specialized real-time framework for identifying damage, with a particular focus on detecting corrosion in the conductors of generators within hydropower plants. It should be noted that corrosion processes can be highly complex and nonlinear, making it challenging to develop accurate physics-based models that capture all the nuances. Therefore, the proposed framework leverages autoencoder, an unsupervised, data-driven AI technology with the Mahalanobis distance, to capture the intricacies of corrosion and automate its detection. Rigorous testing shows that it can identify slight variations indicating conductor corrosion with over 80% sensitivity and a 5% false alarm rate for ‘medium’ to ‘high’ severity damage. By detecting and resolving corrosion early, the system reduces disruptions, streamlines maintenance, and mitigates unscheduled repairs’ negative effects on the environment. This enhances energy generation effectiveness, promotes hydroelectric facilities’ long-term viability, and fosters community prosperity.
Citation: Smart Cities
PubDate: 2024-02-06
DOI: 10.3390/smartcities7010020
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 518-540: Safety and Mobility Evaluation of
Cumulative-Anticipative Car-Following Model for Connected Autonomous
Vehicles
Authors: Hafiz Usman Ahmed, Salman Ahmad, Xinyi Yang, Pan Lu, Ying Huang
First page: 518
Abstract: In the typical landscape of road transportation, about 90% of traffic accidents result from human errors. Vehicle automation enhances road safety by reducing driver fatigue and errors and improves overall mobility efficiency. The advancement of autonomous vehicle technology will significantly impact traffic safety, potentially saving more than 30,000 lives annually in the United States alone. The widespread acceptance of autonomous and connected autonomous vehicles (AVs and CAVs) will be a process spanning multiple decades, requiring their coexistence with traditional vehicles. This study explores the mobility and safety performance of CAVs in mixed-traffic environments using the cumulative-anticipative car-following (CACF) model. This research compares the CACF model with established Wiedemann 99 and cooperative adaptive cruise control (CACC) models using a VISSIM platform. The simulations include single-lane and multi-lane networks, incorporating sensitivity tests for mobility and safety parameters. The study reveals increased throughput, reduced delays, and enhanced travel times with CACF, emphasizing its advantages over CACC. Safety analyses demonstrate CACF’s ability to prevent traffic shockwaves and bottlenecks, emphasizing the significance of communication range and acceleration coefficients. The research recommends early investment in vehicle-to-infrastructure (V2I) communication technology, refining CACC logic, and expanding the study to diverse road scenarios.
Citation: Smart Cities
PubDate: 2024-02-06
DOI: 10.3390/smartcities7010021
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 541-565: Enhancing Smart Cities through
Third-Party Logistics: Predicting Delivery Intensity
Authors: Mariusz Kmiecik, Aleksandra Wierzbicka
First page: 541
Abstract: This article addresses the key and current issues of smart cities in the context of last-mile supply management. Specifically, it explores how third-party logistics (3PL) activities impact last-mile delivery management in smart cities. It examines how 3PL affects delivery volumes, expanding the predictive capabilities of logistics operators. A research question included in the Introduction of this paper is also posed to explore the problem in depth. The research conducted focuses mainly on a case study conducted on the operations of an international 3PL logistics operator. In addition, predictive methods are used to analyse the shipment volume data for individual barcodes in the two analysed cities in Poland. Currently, the concept of a smart city assumes the limited participation of logistics operators in creating improvements for cities. The case study analysis shows that in the cities studied, 3PL companies, through predictive actions, can regulate the flow of vehicles out of the logistics centre and into the city, thus influencing the traffic volume in the city. The research is limited to two cities in Poland implementing smart city solutions and one logistics operator. The research also does not include e-commerce. The authors acknowledge that the results obtained cannot be generalised to a larger scale. This paper bridges the research gap on 3PL activities for last-mile logistics improvements. In addition, the paper proposes the first concept related to the implementation of a 3PL company’s predictive activities associated with the operator’s ability to control the impact on urban traffic.
Citation: Smart Cities
PubDate: 2024-02-08
DOI: 10.3390/smartcities7010022
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 566-596: Climate Change Mitigation through
Modular Construction
Authors: Zeerak Waryam Sajid, Fahim Ullah, Siddra Qayyum, Rehan Masood
First page: 566
Abstract: Modular construction (MC) is a promising concept with the potential to revolutionize the construction industry (CI). The sustainability aspects of MC, among its other encouraging facets, have garnered escalated interest and acclaim among the research community, especially in the context of climate change (CC) mitigation efforts. Despite numerous scholarly studies contributing to the understanding of MC, a holistic review of the prevailing literature that systematically documents the impact of utilizing MC on CC mitigation remains scarce. The study conducts a systematic literature review (SLR) of the pertinent literature retrieved from the Scopus repository to explore the relationship between MC and CC mitigation. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, the SLR was conducted on 31 shortlisted articles published between 2010 and 2023. The findings of the study reveal that MC can mitigate the climate crisis by reducing GHG emissions, curtailing resource intensiveness by enabling a circular economy (CE), fomenting energy efficiency, and fostering resourceful land use and management in the CI. A conceptual framework based on the findings of the previous literature is proposed in this study, which outlines several strategies for CC mitigation that can be implemented by the adoption of MC in the CI. The current study is a humble effort to review various offerings of MC to help mitigate CC in the era of striving for global sustainability. For industry practitioners and policymakers, this study highlights the viability of leveraging MC for CC mitigation, aiming to inspire better decision making for sustainable development in the CI. Similarly, for researchers, it presents MC as a potential tool for CC mitigation that can be further explored in terms of its associated factors, and focused frameworks can be developed.
Citation: Smart Cities
PubDate: 2024-02-08
DOI: 10.3390/smartcities7010023
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 597-614: Bibliometric Study on the
Conceptualisation of Smart City and Education
Authors: Debora Scala, Ángel Ignacio Aguilar Cuesta, Maria Ángeles Rodríguez-Domenech, María del Carmen Cañizares Ruiz
First page: 597
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-02-10
DOI: 10.3390/smartcities7010024
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 615-632: Urban Design and Planning
Participation in the Digital Age: Lessons from an Experimental Online
Platform
Authors: Marshall, Farndon, Hudson-Smith, Kourniotis, Karadimitriou
First page: 615
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-02-13
DOI: 10.3390/smartcities7010025
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 633-661: Toward Establishing a Tourism Data
Space: Innovative Geo-Dashboard Development for Tourism Research and
Management
Authors: Dolores Ordóñez-Martínez, Joana Maria Seguí-Pons, Maurici Ruiz-Pérez
First page: 633
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-02-14
DOI: 10.3390/smartcities7010026
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 662-679: Flood-Resilient Smart Cities: A
Data-Driven Risk Assessment Approach Based on Geographical Risks and
Emergency Response Infrastructure
Authors: João Paulo Just Peixoto, Daniel G. Costa, Paulo Portugal, Francisco Vasques
First page: 662
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-02-16
DOI: 10.3390/smartcities7010027
Issue No: Vol. 7, No. 1 (2024)
- Smart Cities, Vol. 7, Pages 680-711: Edge Offloading in Smart Grid
Authors: Gabriel Ioan Arcas, Tudor Cioara, Ionut Anghel, Dragos Lazea, Anca Hangan
First page: 680
Abstract: 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.
Citation: Smart Cities
PubDate: 2024-02-19
DOI: 10.3390/smartcities7010028
Issue No: Vol. 7, No. 1 (2024)
