Smart Cities, Volume 7, Issue 3 (June 2024) – 12 articles

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

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

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

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

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

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 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

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 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

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

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

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