Journal Description
Smart Cities
Smart Cities
is an international, scientific, peer-reviewed, open access journal on the science and technology of smart cities, published bimonthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Inspec, AGRIS, and other databases.
- Journal Rank: JCR - Q1 (Engineering, Electrical and Electronic) / CiteScore - Q1 (Urban Studies)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20.2 days after submission; acceptance to publication is undertaken in 4.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
7.0 (2023);
5-Year Impact Factor:
5.8 (2023)
Latest Articles
Unlocking Artificial Intelligence Adoption in Local Governments: Best Practice Lessons from Real-World Implementations
Smart Cities 2024, 7(4), 1576-1625; https://doi.org/10.3390/smartcities7040064 (registering DOI) - 28 Jun 2024
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)
Open AccessArticle
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
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)
►▼
Show Figures
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00063/article_deploy/html/images/smartcities-07-00063-g001-550.jpg?1719446827)
Figure 1
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00063/article_deploy/html/images/smartcities-07-00063-g003-550.jpg?1719446829)
Figure 3
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00063/article_deploy/html/images/smartcities-07-00063-g004-550.jpg?1719446832)
Figure 4
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00063/article_deploy/html/images/smartcities-07-00063-g005-550.jpg?1719446833)
Figure 5
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00063/article_deploy/html/images/smartcities-07-00063-g006-550.jpg?1719446834)
Figure 6
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00063/article_deploy/html/images/smartcities-07-00063-g007-550.jpg?1719446835)
Figure 7
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00063/article_deploy/html/images/smartcities-07-00063-g008-550.jpg?1719446835)
Figure 8
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00063/article_deploy/html/images/smartcities-07-00063-g009-550.jpg?1719446836)
Figure 9
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00063/article_deploy/html/images/smartcities-07-00063-g010-550.jpg?1719446836)
Figure 10
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00063/article_deploy/html/images/smartcities-07-00063-g011-550.jpg?1719446837)
Figure 11
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00063/article_deploy/html/images/smartcities-07-00063-g012-550.jpg?1719446837)
Figure 12
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00063/article_deploy/html/images/smartcities-07-00063-g013-550.jpg?1719446838)
Figure 13
Open AccessSystematic 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
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)
►▼
Show Figures
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00062/article_deploy/html/images/smartcities-07-00062-g001-550.jpg?1719390040)
Figure 1
Open AccessReview
A Review of IoT-Based Smart City Development and Management
by
Mostafa Zaman, Nathan Puryear, Sherif Abdelwahed and Nasibeh Zohrabi
Smart Cities 2024, 7(3), 1462-1501; https://doi.org/10.3390/smartcities7030061 - 20 Jun 2024
Abstract
►▼
Show Figures
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
[...] Read more.
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.
Full article
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00061/article_deploy/html/images/smartcities-07-00061-g001-550.jpg?1719220070)
Figure 1
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00061/article_deploy/html/images/smartcities-07-00061-g003-550.jpg?1719220073)
Figure 3
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00061/article_deploy/html/images/smartcities-07-00061-g004-550.jpg?1719220074)
Figure 4
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00061/article_deploy/html/images/smartcities-07-00061-g005-550.jpg?1719220075)
Figure 5
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00061/article_deploy/html/images/smartcities-07-00061-g006-550.jpg?1719220077)
Figure 6
Open AccessArticle
Methodology for Identifying Optimal Pedestrian Paths in an Urban Environment: A Case Study of a School Environment in A Coruña, Spain
by
David Fernández-Arango, Francisco-Alberto Varela-García and Alberto M. Esmorís
Smart Cities 2024, 7(3), 1441-1461; https://doi.org/10.3390/smartcities7030060 - 14 Jun 2024
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
[...] Read more.
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.
Full article
(This article belongs to the Topic SDGs 2030 in Buildings and Infrastructure)
►▼
Show Figures
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00060/article_deploy/html/images/smartcities-07-00060-g001-550.jpg?1718623497)
Figure 1
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00060/article_deploy/html/images/smartcities-07-00060-g003-550.jpg?1718623499)
Figure 3
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00060/article_deploy/html/images/smartcities-07-00060-g004-550.jpg?1718623499)
Figure 4
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00060/article_deploy/html/images/smartcities-07-00060-g005-550.jpg?1718623502)
Figure 5
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00060/article_deploy/html/images/smartcities-07-00060-g006-550.jpg?1718623503)
Figure 6
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00060/article_deploy/html/images/smartcities-07-00060-g007-550.jpg?1718623504)
Figure 7
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00060/article_deploy/html/images/smartcities-07-00060-g008-550.jpg?1718623505)
Figure 8
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00060/article_deploy/html/images/smartcities-07-00060-g009-550.jpg?1718623506)
Figure 9
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00060/article_deploy/html/images/smartcities-07-00060-g010-550.jpg?1718623506)
Figure 10
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00060/article_deploy/html/images/smartcities-07-00060-g011-550.jpg?1718623511)
Figure 11
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00060/article_deploy/html/images/smartcities-07-00060-g012-550.jpg?1718623518)
Figure 12
Open AccessArticle
Turning Features Detection from Aerial Images: Model Development and Application on Florida’s Public Roadways
by
Richard Boadu Antwi, Michael Kimollo, Samuel Yaw Takyi, Eren Erman Ozguven, Thobias Sando, Ren Moses and Maxim A. Dulebenets
Smart Cities 2024, 7(3), 1414-1440; https://doi.org/10.3390/smartcities7030059 - 13 Jun 2024
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
[...] Read more.
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.
Full article
(This article belongs to the Special Issue Paving the Future: Sustainable Road Design and Urban Mobility in Smart Cities)
►▼
Show Figures
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00059/article_deploy/html/images/smartcities-07-00059-ag-550.jpg?1719306438)
Graphical abstract
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00059/article_deploy/html/images/smartcities-07-00059-g002-550.jpg?1719306402)
Figure 2
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00059/article_deploy/html/images/smartcities-07-00059-g003-550.jpg?1719306403)
Figure 3
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00059/article_deploy/html/images/smartcities-07-00059-g004-550.jpg?1719306406)
Figure 4
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00059/article_deploy/html/images/smartcities-07-00059-g005a-550.jpg?1719306408)
Figure 5
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00059/article_deploy/html/images/smartcities-07-00059-g005b-550.jpg?1719306412)
Figure 5 Cont.
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00059/article_deploy/html/images/smartcities-07-00059-g005c-550.jpg?1719306416)
Figure 5 Cont.
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00059/article_deploy/html/images/smartcities-07-00059-g006a-550.jpg?1719306418)
Figure 6
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00059/article_deploy/html/images/smartcities-07-00059-g006b-550.jpg?1719306423)
Figure 6 Cont.
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00059/article_deploy/html/images/smartcities-07-00059-g006c-550.jpg?1719306425)
Figure 6 Cont.
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00059/article_deploy/html/images/smartcities-07-00059-g007-550.jpg?1719306430)
Figure 7
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00059/article_deploy/html/images/smartcities-07-00059-g008a-550.jpg?1719306432)
Figure 8
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00059/article_deploy/html/images/smartcities-07-00059-g008b-550.jpg?1719306435)
Figure 8 Cont.
Open AccessArticle
Optimization of Geothermal Heat Pump Systems for Sustainable Urban Development in Southeast Asia
by
Thiti Chanchayanon, Susit Chaiprakaikeow, Apiniti Jotisankasa and Shinya Inazumi
Smart Cities 2024, 7(3), 1390-1413; https://doi.org/10.3390/smartcities7030058 - 12 Jun 2024
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
[...] Read more.
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.
Full article
(This article belongs to the Topic Smart Cities: Infrastructure, Innovation, Technology, Governance and Citizenship)
►▼
Show Figures
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00058/article_deploy/html/images/smartcities-07-00058-g001-550.jpg?1718181646)
Figure 1
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00058/article_deploy/html/images/smartcities-07-00058-g003-550.jpg?1718181649)
Figure 3
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00058/article_deploy/html/images/smartcities-07-00058-g004-550.jpg?1718181650)
Figure 4
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00058/article_deploy/html/images/smartcities-07-00058-g005-550.jpg?1718181650)
Figure 5
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00058/article_deploy/html/images/smartcities-07-00058-g006-550.jpg?1718181652)
Figure 6
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00058/article_deploy/html/images/smartcities-07-00058-g007-550.jpg?1718181653)
Figure 7
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00058/article_deploy/html/images/smartcities-07-00058-g008-550.jpg?1718181653)
Figure 8
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00058/article_deploy/html/images/smartcities-07-00058-g009-550.jpg?1718181654)
Figure 9
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00058/article_deploy/html/images/smartcities-07-00058-g010-550.jpg?1718181655)
Figure 10
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00058/article_deploy/html/images/smartcities-07-00058-g011a-550.jpg?1718181656)
Figure 11
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00058/article_deploy/html/images/smartcities-07-00058-g011b-550.jpg?1718181658)
Figure 11 Cont.
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00058/article_deploy/html/images/smartcities-07-00058-g012-550.jpg?1718181659)
Figure 12
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00058/article_deploy/html/images/smartcities-07-00058-g013-550.jpg?1718181660)
Figure 13
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00058/article_deploy/html/images/smartcities-07-00058-g014-550.jpg?1718181661)
Figure 14
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00058/article_deploy/html/images/smartcities-07-00058-g015-550.jpg?1718181662)
Figure 15
![](https://pub.mdpi-res.com/smartcities/smartcities-07-00058/article_deploy/html/images/smartcities-07-00058-g016-550.jpg?1718181662)
Figure 16