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Smart Cities, Volume 7, Issue 4 (August 2024) – 15 articles

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14 pages, 5736 KiB  
Article
Smart Non-Intrusive Appliance Load-Monitoring System Based on Phase Diagram Analysis
by Denis Stanescu, Florin Enache and Florin Popescu
Smart Cities 2024, 7(4), 1936-1949; https://doi.org/10.3390/smartcities7040076 (registering DOI) - 23 Jul 2024
Viewed by 161
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 [...] Read more.
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. Full article
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29 pages, 8332 KiB  
Article
Energy Management in Residential Microgrid Based on Non-Intrusive Load Monitoring and Internet of Things
by Rawda Ramadan, Qi Huang, Amr S. Zalhaf, Olusola Bamisile, Jian Li, Diaa-Eldin A. Mansour, Xiangning Lin and Doaa M. Yehia
Smart Cities 2024, 7(4), 1907-1935; https://doi.org/10.3390/smartcities7040075 (registering DOI) - 23 Jul 2024
Viewed by 198
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 [...] Read more.
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. Full article
(This article belongs to the Topic Application of Smart Technologies in Buildings)
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19 pages, 6613 KiB  
Article
Multi-Type Structural Damage Image Segmentation via Dual-Stage Optimization-Based Few-Shot Learning
by Jiwei Zhong, Yunlei Fan, Xungang Zhao, Qiang Zhou and Yang Xu
Smart Cities 2024, 7(4), 1888-1906; https://doi.org/10.3390/smartcities7040074 (registering DOI) - 22 Jul 2024
Viewed by 223
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 [...] Read more.
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. Full article
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10 pages, 10073 KiB  
Article
Modeling Strategic Interventions to Increase Attendance at Youth Community Centers
by Alejandro Moro-Araujo, Luis Alonso Pastor and Kent Larson
Smart Cities 2024, 7(4), 1878-1887; https://doi.org/10.3390/smartcities7040073 (registering DOI) - 22 Jul 2024
Viewed by 209
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 [...] Read more.
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. Full article
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21 pages, 3905 KiB  
Article
Data Governance to Counter Hybrid Threats against Critical Infrastructures
by Gabriel Pestana and Souzanna Sofou
Smart Cities 2024, 7(4), 1857-1877; https://doi.org/10.3390/smartcities7040072 (registering DOI) - 22 Jul 2024
Viewed by 218
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Digital Innovation and Transformation for Smart Cities)
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21 pages, 11086 KiB  
Article
AI-Driven Prediction and Mapping of Soil Liquefaction Risks for Enhancing Earthquake Resilience in Smart Cities
by Arisa Katsuumi, Yuxin Cong and Shinya Inazumi
Smart Cities 2024, 7(4), 1836-1856; https://doi.org/10.3390/smartcities7040071 - 17 Jul 2024
Viewed by 421
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Inclusive Smart Cities)
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34 pages, 6369 KiB  
Article
Energy Management System for a Residential Positive Energy District Based on Fuzzy Logic Approach (RESTORATIVE)
by Tony Castillo-Calzadilla, Jesús Oroya-Villalta and Cruz E. Borges
Smart Cities 2024, 7(4), 1802-1835; https://doi.org/10.3390/smartcities7040070 - 16 Jul 2024
Viewed by 544
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 [...] Read more.
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. Full article
(This article belongs to the Section Energy and ICT)
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26 pages, 34701 KiB  
Article
Enhancing Property Valuation in Post-War Recovery: Integrating War-Related Attributes into Real Estate Valuation Practices
by Mounir Azzam, Valerie Graw, Eva Meidler and Andreas Rienow
Smart Cities 2024, 7(4), 1776-1801; https://doi.org/10.3390/smartcities7040069 - 5 Jul 2024
Viewed by 1170
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 [...] Read more.
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. Full article
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53 pages, 7565 KiB  
Review
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
by Amr Adel and Noor HS Alani
Smart Cities 2024, 7(4), 1723-1775; https://doi.org/10.3390/smartcities7040068 - 5 Jul 2024
Viewed by 376
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 [...] Read more.
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. Full article
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17 pages, 5891 KiB  
Article
Data-Driven Reliability Prediction for District Heating Networks
by Lasse Kappel Mortensen and Hamid Reza Shaker
Smart Cities 2024, 7(4), 1706-1722; https://doi.org/10.3390/smartcities7040067 - 2 Jul 2024
Viewed by 447
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, [...] Read more.
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. Full article
(This article belongs to the Section Smart Grids)
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36 pages, 1493 KiB  
Article
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
by Katarzyna Turoń
Smart Cities 2024, 7(4), 1670-1705; https://doi.org/10.3390/smartcities7040066 - 2 Jul 2024
Viewed by 392
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, [...] Read more.
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. Full article
(This article belongs to the Section Smart Transportation)
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44 pages, 1100 KiB  
Article
Business Models Used in Smart Cities—Theoretical Approach with Examples of Smart Cities
by Radosław Wolniak, Bożena Gajdzik, Michaline Grebski, Roman Danel and Wiesław Wes Grebski
Smart Cities 2024, 7(4), 1626-1669; https://doi.org/10.3390/smartcities7040065 - 1 Jul 2024
Viewed by 690
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Business Model Innovation in Smart Cities)
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50 pages, 3271 KiB  
Review
Unlocking Artificial Intelligence Adoption in Local Governments: Best Practice Lessons from Real-World Implementations
by Tan Yigitcanlar, Anne David, Wenda Li, Clinton Fookes, Simon Elias Bibri and Xinyue Ye
Smart Cities 2024, 7(4), 1576-1625; https://doi.org/10.3390/smartcities7040064 - 28 Jun 2024
Viewed by 673
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 [...] Read more.
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. Full article
(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)
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25 pages, 821 KiB  
Article
Enhancing Service Quality of On-Demand Transportation Systems Using a Hybrid Approach with Customized Heuristics
by Sonia Nasri, Hend Bouziri and Wassila Aggoune Mtalaa
Smart Cities 2024, 7(4), 1551-1575; https://doi.org/10.3390/smartcities7040063 - 26 Jun 2024
Viewed by 849
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 [...] Read more.
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. Full article
(This article belongs to the Section Smart Transportation)
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49 pages, 1086 KiB  
Systematic Review
The Role of Smart Homes in Providing Care for Older Adults: A Systematic Literature Review from 2010 to 2023
by Arian Vrančić, Hana Zadravec and Tihomir Orehovački
Smart Cities 2024, 7(4), 1502-1550; https://doi.org/10.3390/smartcities7040062 - 26 Jun 2024
Viewed by 894
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Inclusive Smart Cities)
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