Smart Cities最新文献

{"title":"Transitioning to a Low-Carbon Lifestyle? An Exploration of Millennials’ Low-Carbon Behavior—A Case Study in China","authors":"Yan Wu, Pim Martens, Thomas Krafft","doi":"10.3390/smartcities7040080","DOIUrl":"https://doi.org/10.3390/smartcities7040080","url":null,"abstract":"The Sustainable Development Goals (SDGs) have set the agenda for 2030, calling for collective global efforts to deal with climate change while seeking a balance between economic development and environmental protection. Although many countries are exploring emission reduction paths, mainly from government and corporate perspectives, addressing climate change is also an individual responsibility and requires public participation in collective action. The millennial generation constitutes the current workforce and will be the leaders in climate action for the next 30 years. Therefore, our study focuses on the Chinese millennial generation, conducting in-depth semi-structured interviews with 50 participants in qualitative research to explore their low-carbon lifestyles, the barriers, and enablers in switching to a wider range of low-carbon lifestyles. There are three main results: (1) Based on our study samples, there is an indication that Chinese millennials have a positive attitude towards transitioning to a low-carbon lifestyle. Women demonstrate a stronger willingness to adopt low-carbon behaviors in their daily household activities compared to men. However, their involvement in governance in the context of transitioning to a low-carbon society is limited, with most women assuming execution roles in climate action rather than decision-making positions. (2) Millennial’s low-carbon life transition is accompanied by technological innovation and progress. However, this progress brings some new forms of resource waste, and reasonable policy-making is essential. (3) Personal economic interests and the satisfaction of their consumption needs will drive millennials to reduce carbon emissions in their daily lives, but it requires the guidance of reasonable policy-making and synergies among various stakeholders. This research will help policymakers better understand the current status and potential issues related to people’s low-carbon actions, enabling the formulation of more rational guiding policies. It can also help other stakeholders learn about millennials’ demands and take more effective collective action toward carbon reduction.","PeriodicalId":515895,"journal":{"name":"Smart Cities","volume":"51 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Cycling Safety in Smart Cities: A Data-Driven Embedded Risk Alert System","authors":"José Miguel Ferreira, Daniel G. Costa","doi":"10.3390/smartcities7040079","DOIUrl":"https://doi.org/10.3390/smartcities7040079","url":null,"abstract":"The safety of cyclists on city streets is a significant concern, particularly with the rising number of accidents in densely populated areas. Urban environments present numerous challenges, such as complex road networks and heavy traffic, which increase the risk of cycling-related incidents. Such concern has been recurrent, even within smart city scenarios that have been focused on only expanding the cycling infrastructure. This article introduces an innovative low-cost embedded system designed to improve cycling safety in urban areas, taking geospatial data as input. By assessing the proximity to emergency services and utilizing GPS coordinates, the system can determine the indirect current risk level for cyclists, providing real-time alerts when crossing high-risk zones. Built on a Raspberry Pi Zero board, this solution is both cost-effective and efficient, making it easily reproducible in various urban settings. Preliminary results in Porto, Portugal, showcase the system’s practical application and effectiveness in enhancing cycling safety and supporting sustainable urban mobility.","PeriodicalId":515895,"journal":{"name":"Smart Cities","volume":"59 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Smart City Principles in the Numerical Simulation Analysis on Passive Energy Saving of Small and Medium Gymnasiums","authors":"Feng Qian, Hongliang Sun, Li Yang","doi":"10.3390/smartcities7040078","DOIUrl":"https://doi.org/10.3390/smartcities7040078","url":null,"abstract":"With the increasing energy consumption in buildings, the proportion of energy consumption in public buildings continues to grow. As an essential component of public buildings, sports buildings are receiving more attention regarding energy-saving technologies. This paper aims to study the passive energy-saving design methods of small-and medium-sized sports halls in hot summer and cold winter regions, exploring how to reduce building energy consumption by improving the spatial design and thermal performance of the enclosure structures of sports halls. Taking the Wuhu County Sports Center as an example, this study uses computer simulation software to analyze the building’s wind environment and the thermal performance of its external walls and roof. The results show that the large volume of the sports hall significantly impacts the distribution of wind speed and pressure around it, and this impact decreases with height. The thermal simulation of the enclosure structures demonstrates that adding insulation layers to the interior and exterior of the walls and roof of the sports hall is an effective way to reduce energy consumption in both winter and summer. Additionally, wind environment simulations of different roof shapes reveal that flat roofs have the most significant blocking effect on wind and are prone to inducing strong vortices on the leeward side; concave arch roofs have the least blocking effect on airflow, and arch and wave-shaped roofs maintain lower vortex intensity on the leeward side. Hopefully, this study can provide significant references for the energy-saving design of future small- and medium-sized sports buildings.","PeriodicalId":515895,"journal":{"name":"Smart Cities","volume":"24 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing the Requirements for Smart Pedestrian Applications: Findings from Nicosia, Cyprus","authors":"G. Papageorgiou, Demetris Demetriou, Elena Tsappi, Athanasios Maimaris","doi":"10.3390/smartcities7040077","DOIUrl":"https://doi.org/10.3390/smartcities7040077","url":null,"abstract":"This paper elicits and analyzes the main requirements for Smart Pedestrian applications designed to enhance the pedestrian experience in urban environments by offering optimized walking routes, improved accessibility, and support for social inclusion and connectivity. Utilizing a mixed-methods approach, the research combines qualitative insights with quantitative data analysis based on surveys conducted in two strategically selected urban areas of Nicosia, Cyprus. Through the survey, the requirements and potential use of Smart Pedestrian apps are investigated while accounting for the quality of service of the urban infrastructure in a medium-sized city context. Additionally, the study contrasts the current smartphone applications, as they predominantly facilitate vehicular transportation, with the potential use of ICT/ITS to support pedestrians for sustainable mobility. The findings reveal a significant demand for a Pedestrian Smartphone app, driven by its ability to provide relevant information on optimum pedestrian routes, as well as act as a citizen’s voice for spotting infrastructure problems and improving the pedestrian network. Further, it is also revealed that limitations in the pedestrian infrastructure substantially restrict walking preferences, emphasizing the need for urgent city-level urban planning solutions to support active mobility. Additionally, the research carried out underscores the importance of a sustainable business model to support the successful deployment of Smart Pedestrian apps. Ultimately, the results of the study suggest prioritizing a smart technology leverage with a crowdsourcing social network business model to promote pedestrian mobility, thereby reducing vehicular dependence, enhancing public health, and improving the quality of life. Such an approach would act as catalyst for policymakers to concentrate on sustainability by investing in digital technology for integrated pedestrian networks, fostering the emergence of genuine smart cities.","PeriodicalId":515895,"journal":{"name":"Smart Cities","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy Management in Residential Microgrid Based on Non-Intrusive Load Monitoring and Internet of Things","authors":"Rawda Ramadan, Qi Huang, A. S. Zalhaf, Olusola Bamisile, Jian Li, D. Mansour, Xiangning Lin, D. M. Yehia","doi":"10.3390/smartcities7040075","DOIUrl":"https://doi.org/10.3390/smartcities7040075","url":null,"abstract":"Recently, various strategies for energy management have been proposed to improve energy efficiency in smart grids. One key aspect of this is the use of microgrids. To effectively manage energy in a residential microgrid, advanced computational tools are required to maintain the balance between supply and demand. The concept of load disaggregation through non-intrusive load monitoring (NILM) is emerging as a cost-effective solution to optimize energy utilization in these systems without the need for extensive sensor infrastructure. This paper presents an energy management system based on NILM and the Internet of Things (IoT) for a residential microgrid, including a photovoltaic (PV) plant and battery storage device. The goal is to develop an efficient load management system to increase the microgrid’s independence from the traditional electrical grid. The microgrid model is developed in the electromagnetic transient program PSCAD/EMTDC to analyze and optimize energy performance. Load disaggregation is obtained by combining artificial neural networks (ANNs) and particle swarm optimization (PSO) to identify appliances for demand-side management. An ANN is applied in NILM as a load identification task, and PSO is used to optimize the ANN algorithm. This combination enhances the NILM technique’s accuracy, which is verified using the mean absolute error method to assess the difference between the predicted and measured power consumption of appliances. The NILM output is then transferred to consumers through the ThingSpeak IoT platform, enabling them to monitor and control their appliances to save energy and costs.","PeriodicalId":515895,"journal":{"name":"Smart Cities","volume":"28 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141813148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart Non-Intrusive Appliance Load-Monitoring System Based on Phase Diagram Analysis","authors":"Denis Stanescu, Florin Enache, F. Popescu","doi":"10.3390/smartcities7040076","DOIUrl":"https://doi.org/10.3390/smartcities7040076","url":null,"abstract":"Much of today’s power grid was designed and built using technologies and organizational principles developed decades ago. The lack of energy resources and classic power networks are the main causes of the development of the smart grid to efficiently use energy resources, with stable and safe operation. In such a network, one of the fundamental priorities is provided by non-intrusive appliance load monitoring (NIALM) in order to analyze, recognize and determine the electricity consumption of each consumer. In this paper, we propose a new smart system approach for the characterization of the appliance load signature based on a data-driven method, namely the phase diagram. Our aim is to use the non-intrusive load monitoring of appliances in order to recognize different types of consumers that can exist within a smart building.","PeriodicalId":515895,"journal":{"name":"Smart Cities","volume":"61 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Governance to Counter Hybrid Threats against Critical Infrastructures","authors":"Gabriel Pestana, S. Sofou","doi":"10.3390/smartcities7040072","DOIUrl":"https://doi.org/10.3390/smartcities7040072","url":null,"abstract":"Hybrid threats exploit vulnerabilities in digital infrastructures, posing significant challenges to democratic countries and the resilience of critical infrastructures (CIs). This study explores integrating data governance with business process management in response actions to hybrid attacks, particularly those targeting CI vulnerabilities. This research analyzes hybrid threats as a multidimensional and time-dependent problem. Using the Business Process Model and Notation, this investigation explores data governance to counter CI-related hybrid threats. It illustrates the informational workflow and context awareness necessary for informed decision making in a cross-border hybrid threat scenario. An airport example demonstrates the proposed approach’s efficacy in ensuring stakeholder coordination for potential CI attacks requiring cross-border decision making. This study emphasizes the importance of the information security lifecycle in protecting digital assets and sensitive information through detection, prevention, response, and knowledge management. It advocates proactive strategies like implementing security policies, intrusion detection software tools, and IT services. Integrating Infosec with the methodology of confidentiality, integrity, and availability, especially in the response phase, is essential for a proactive Infosec approach, ensuring a swift stakeholder response and effective incident mitigation. Effective data governance protects sensitive information and provides reliable digital data in CIs like airports. Implementing robust frameworks enhances resilience against hybrid threats, establishes trusted information exchange, and promotes stakeholder collaboration for an emergency response. Integrating data governance with Infosec strengthens security measures, enabling proactive monitoring, mitigating threats, and safeguarding CIs from cyber-attacks and other malicious activities.","PeriodicalId":515895,"journal":{"name":"Smart Cities","volume":"18 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141814569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling Strategic Interventions to Increase Attendance at Youth Community Centers","authors":"Alejandro Moro-Araujo, Luis Alonso Pastor, Kent Larson","doi":"10.3390/smartcities7040073","DOIUrl":"https://doi.org/10.3390/smartcities7040073","url":null,"abstract":"Community centers play a crucial role in urban environments, providing physical and educational services to their surrounding communities, particularly for students. Among the many benefits for students are enhanced academic outcomes, improvement of behavioral problems, and increased school attendance. Such centers are also particularly vital for low-income and racial minority students as they are pivotal in giving them outside-of-school learning opportunities. However, determinants influencing attendance at community centers remain largely unexplored. The novelty of our research comes from using census data, Boston Centers for Youth and Families (BCYF) attendance data, and specific center attributes, to develop human mobility gravitational models that have been used, for the first time, to predict attendance across the BCYF network. Using those models, we simulated the potential effects on general and student attendance by changing center attributes, such as facilities and operating hours. We also researched the impact of changing the walking accessibility to those centers on their respective attendance patterns. After the analysis, we found that the most cost-effective policy to increase BCYF attendance is changing each center’s educational and recreational offerings far beyond any accessibility interventions. Our results provide insights into potential policy changes that could optimize the attendance and reach of BCYF Community Centers to under-served populations.","PeriodicalId":515895,"journal":{"name":"Smart Cities","volume":"18 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Type Structural Damage Image Segmentation via Dual-Stage Optimization-Based Few-Shot Learning","authors":"Jiwei Zhong, Yunlei Fan, Xungang Zhao, Qiang Zhou, Yang Xu","doi":"10.3390/smartcities7040074","DOIUrl":"https://doi.org/10.3390/smartcities7040074","url":null,"abstract":"The timely and accurate recognition of multi-type structural surface damage (e.g., cracks, spalling, corrosion, etc.) is vital for ensuring the structural safety and service performance of civil infrastructure and for accomplishing the intelligent maintenance of smart cities. Deep learning and computer vision have made profound impacts on automatic structural damage recognition using nondestructive test techniques, especially non-contact vision-based algorithms. However, the recognition accuracy highly depends on the training data volume and damage completeness in the conventional supervised learning pipeline, which significantly limits the model performance under actual application scenarios; the model performance and stability for multi-type structural damage categories are still challenging. To address the above issues, this study proposes a dual-stage optimization-based few-shot learning segmentation method using only a few images with supervised information for multi-type structural damage recognition. A dual-stage optimization paradigm is established encompassing an internal network optimization based on meta-task and an external meta-learning machine optimization based on meta-batch. The underlying image features pertinent to various structural damage types are learned as prior knowledge to expedite adaptability across diverse damage categories via only a few samples. Furthermore, a mathematical framework of optimization-based few-shot learning is formulated to intuitively express the perception mechanism. Comparative experiments are conducted to verify the effectiveness and necessity of the proposed method on a small-scale multi-type structural damage image set. The results show that the proposed method could achieve higher segmentation accuracies for various types of structural damage than directly training the original image segmentation network. In addition, the generalization ability for the unseen structural damage category is also validated. The proposed method provides an effective solution to achieve image-based structural damage recognition with high accuracy and robustness for bridges and buildings, which assists the unmanned intelligent inspection of civil infrastructure using drones and robotics in smart cities.","PeriodicalId":515895,"journal":{"name":"Smart Cities","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Driven Prediction and Mapping of Soil Liquefaction Risks for Enhancing Earthquake Resilience in Smart Cities","authors":"Arisa Katsuumi, Yuxin Cong, S. Inazumi","doi":"10.3390/smartcities7040071","DOIUrl":"https://doi.org/10.3390/smartcities7040071","url":null,"abstract":"In response to increasing urbanization and the need for infrastructure resilient to natural hazards, this study introduces an AI-driven predictive model designed to assess the risk of soil liquefaction. Utilizing advanced ensemble machine learning techniques, the model integrates geotechnical and geographical data to accurately predict the potential for soil liquefaction in urban areas, with a specific focus on Yokohama, Japan. This methodology leverages comprehensive datasets from geological surveys and seismic activity to enhance urban planning and infrastructure development in smart cities. The primary outputs include detailed soil liquefaction risk maps that are essential for effective urban risk management. These maps support urban planners and engineers in making informed decisions, prioritizing safety, and promoting sustainability. The model employs a robust combination of artificial neural networks and gradient boosting decision trees to analyze and predict data points, assessing soil susceptibility to liquefaction during seismic events. Notably, the model achieves high accuracy in predicting soil classifications and N-values, which are critical for evaluating soil liquefaction risk. Validation against an extensive dataset from geotechnical surveys confirms the model’s practical effectiveness. Moreover, the results highlight the transformative potential of AI in enhancing geotechnical risk assessments and improving the resilience of urban areas against natural hazards.","PeriodicalId":515895,"journal":{"name":"Smart Cities","volume":" 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141830982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}