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Smart Cities, Volume 7, Issue 3 (June 2024) – 20 articles

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21 pages, 18092 KiB  
Article
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 (registering DOI) - 14 Jun 2024
Viewed by 175
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)
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27 pages, 1485 KiB  
Article
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 (registering DOI) - 13 Jun 2024
Viewed by 209
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
24 pages, 7593 KiB  
Article
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 (registering DOI) - 12 Jun 2024
Viewed by 313
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
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44 pages, 908 KiB  
Review
Artificial Intelligence in Smart Cities—Applications, Barriers, and Future Directions: A Review
by Radosław Wolniak and Kinga Stecuła
Smart Cities 2024, 7(3), 1346-1389; https://doi.org/10.3390/smartcities7030057 - 10 Jun 2024
Cited by 1 | Viewed by 464
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Multidisciplinary Research on Smart Cities)
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16 pages, 4629 KiB  
Article
Characterizing Smart Cities Based on Artificial Intelligence
by Laaziza Hammoumi, Mehdi Maanan and Hassan Rhinane
Smart Cities 2024, 7(3), 1330-1345; https://doi.org/10.3390/smartcities7030056 - 7 Jun 2024
Cited by 1 | Viewed by 588
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 [...] Read more.
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. Full article
(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)
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26 pages, 6942 KiB  
Article
Effectiveness of the Fuzzy Logic Control to Manage the Microclimate Inside a Smart Insulated Greenhouse
by Jamel Riahi, Hamza Nasri, Abdelkader Mami and Silvano Vergura
Smart Cities 2024, 7(3), 1304-1329; https://doi.org/10.3390/smartcities7030055 - 6 Jun 2024
Viewed by 310
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 [...] Read more.
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. Full article
(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)
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15 pages, 683 KiB  
Article
Towards Municipal Data Utilities: Experiences Regarding the Development of a Municipal Data Utility for Intra- and Intermunicipal Actors within the German City of Mainz
by Philipp Lämmel, Jonas Merbeth, Tim Cleffmann and Lukas Koch
Smart Cities 2024, 7(3), 1289-1303; https://doi.org/10.3390/smartcities7030054 - 28 May 2024
Viewed by 545
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 [...] Read more.
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. Full article
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28 pages, 11761 KiB  
Article
Radiometric Infrared Thermography of Solar Photovoltaic Systems: An Explainable Predictive Maintenance Approach for Remote Aerial Diagnostic Monitoring
by Usamah Rashid Qureshi, Aiman Rashid, Nicola Altini, Vitoantonio Bevilacqua and Massimo La Scala
Smart Cities 2024, 7(3), 1261-1288; https://doi.org/10.3390/smartcities7030053 - 28 May 2024
Viewed by 373
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Smart Electronics, Energy, and IoT Infrastructures for Smart Cities)
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40 pages, 1309 KiB  
Article
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
by Jarosław Brodny, Magdalena Tutak and Peter Bindzár
Smart Cities 2024, 7(3), 1221-1260; https://doi.org/10.3390/smartcities7030052 - 22 May 2024
Viewed by 510
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 [...] Read more.
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. Full article
22 pages, 1273 KiB  
Article
Exploring Sustainable Urban Transportation: Insights from Shared Mobility Services and Their Environmental Impact
by Ada Garus, Andromachi Mourtzouchou, Jaime Suarez, Georgios Fontaras and Biagio Ciuffo
Smart Cities 2024, 7(3), 1199-1220; https://doi.org/10.3390/smartcities7030051 - 20 May 2024
Viewed by 681
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 [...] Read more.
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. Full article
(This article belongs to the Section Smart Transportation)
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30 pages, 4153 KiB  
Article
Camera-Based Crime Behavior Detection and Classification
by Jerry Gao, Jingwen Shi, Priyanka Balla, Akshata Sheshgiri, Bocheng Zhang, Hailong Yu and Yunyun Yang
Smart Cities 2024, 7(3), 1169-1198; https://doi.org/10.3390/smartcities7030050 - 19 May 2024
Viewed by 394
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 [...] Read more.
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. Full article
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20 pages, 3393 KiB  
Article
Redesigning Municipal Waste Collection for Aging and Shrinking Communities
by Andante Hadi Pandyaswargo, Chaoxia Shan, Akihisa Ogawa, Ryota Tsubouchi and Hiroshi Onoda
Smart Cities 2024, 7(3), 1149-1168; https://doi.org/10.3390/smartcities7030049 - 16 May 2024
Viewed by 480
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Inclusive Smart Cities)
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23 pages, 1261 KiB  
Article
Enabling Alarm-Based Fault Prediction for Smart Meters in District Heating Systems: A Danish Case Study
by Henrik Alexander Nissen Søndergaard, Hamid Reza Shaker and Bo Nørregaard Jørgensen
Smart Cities 2024, 7(3), 1126-1148; https://doi.org/10.3390/smartcities7030048 - 14 May 2024
Viewed by 439
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 [...] Read more.
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. Full article
(This article belongs to the Section Smart Grids)
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17 pages, 1170 KiB  
Article
Smart Delivery Assignment through Machine Learning and the Hungarian Algorithm
by Juan Pablo Vásconez, Elias Schotborgh, Ingrid Nicole Vásconez, Viviana Moya, Andrea Pilco, Oswaldo Menéndez, Robert Guamán-Rivera and Leonardo Guevara
Smart Cities 2024, 7(3), 1109-1125; https://doi.org/10.3390/smartcities7030047 - 12 May 2024
Viewed by 703
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 [...] Read more.
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. Full article
(This article belongs to the Section Smart Transportation)
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20 pages, 12511 KiB  
Article
Integration of Smart City Technologies with Advanced Predictive Analytics for Geotechnical Investigations
by Yuxin Cong and Shinya Inazumi
Smart Cities 2024, 7(3), 1089-1108; https://doi.org/10.3390/smartcities7030046 - 6 May 2024
Cited by 1 | Viewed by 870
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 [...] Read more.
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. Full article
(This article belongs to the Section Smart Urban Infrastructures)
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29 pages, 3585 KiB  
Article
Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes
by Mariano Gallo
Smart Cities 2024, 7(3), 1060-1088; https://doi.org/10.3390/smartcities7030045 - 3 May 2024
Viewed by 638
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 [...] Read more.
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. Full article
(This article belongs to the Section Smart Transportation)
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16 pages, 497 KiB  
Article
Smart Cities for All? Bridging Digital Divides for Socially Sustainable and Inclusive Cities
by Johan Colding, Caroline Nilsson and Stefan Sjöberg
Smart Cities 2024, 7(3), 1044-1059; https://doi.org/10.3390/smartcities7030044 - 3 May 2024
Viewed by 836
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Inclusive Smart Cities)
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37 pages, 4794 KiB  
Review
Off-Grid Electrification Using Renewable Energy in the Philippines: A Comprehensive Review
by Arizeo C. Salac, Jairus Dameanne C. Somera, Michael T. Castro, Maricor F. Divinagracia-Luzadas, Louis Angelo M. Danao and Joey D. Ocon
Smart Cities 2024, 7(3), 1007-1043; https://doi.org/10.3390/smartcities7030043 - 26 Apr 2024
Viewed by 1526
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, [...] Read more.
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. Full article
(This article belongs to the Section Smart Grids)
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16 pages, 5338 KiB  
Article
3D Point Cloud and GIS Approach to Assess Street Physical Attributes
by Patricio R. Orozco Carpio, María José Viñals and María Concepción López-González
Smart Cities 2024, 7(3), 991-1006; https://doi.org/10.3390/smartcities7030042 - 25 Apr 2024
Viewed by 514
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 [...] Read more.
The present research explores an innovative approach to objectively assessing urban streets attributes using 3D point clouds and Geographic Information Systems (GIS). Urban streets are vital components of cities, playing a significant role in the lives of their residents. Usually, the evaluation of some of their physical attributes has been subjective, but this study leverages 3D point clouds and digital terrain models (DTM) to provide a more objective perspective. This article undertakes a micro-urban analysis of basic physical characteristics (slope, width, and human scale) of a representative street in the historic centre of Valencia (Spain), utilizing 3D laser-scanned point clouds and GIS tools. Applying the proposed methodology, thematic maps were generated, facilitating the objective identification of areas with physical attributes more conducive to suitable pedestrian dynamics. This approach provides a comprehensive understanding of urban street attributes, emphasizing the importance of addressing their assessment through advanced digital technologies. Moreover, this versatile methodology has diverse applications, contributing to social sustainability by enhancing the quality of urban streets and open spaces. Full article
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18 pages, 8995 KiB  
Article
Evaluating the Feasibility of Intelligent Blind Road Junction V2I Deployments
by Joseph Clancy, Dara Molloy, Sean Hassett, James Leahy, Enda Ward, Patrick Denny, Edward Jones, Martin Glavin and Brian Deegan
Smart Cities 2024, 7(3), 973-990; https://doi.org/10.3390/smartcities7030041 - 24 Apr 2024
Viewed by 531
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 [...] Read more.
Cellular Vehicle-to-Everything (C-V2X) communications is a technology that enables intelligent vehicles to exchange information and thus coordinate with other vehicles, road users, and infrastructure. However, despite advancements in cellular technology for V2X applications, significant challenges remain regarding the ability of the system to meet stringent Quality-of-Service (QoS) requirements when deployed at scale. Thus, smaller-scale V2X use case deployments may embody a necessary stepping stone to address these challenges. This work assesses network architectures for an Intelligent Perception System (IPS) blind road junction or blind corner scenarios. Measurements were collected using a private 5G NR network with Sub-6GHz and mmWave connectivity, evaluating the feasibility and trade-offs of IPS network configurations. The results demonstrate the feasibility of the IPS as a V2X application, with implementation considerations based on deployment and maintenance costs. If computation resources are co-located with the sensors, sufficient performance is achieved. However, if the computational burden is instead placed upon the intelligent vehicle, it is questionable as to whether an IPS is achievable or not. Much depends on image quality, latency, and system performance requirements. Full article
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