City Management Research Articles

Smart Parking System

As urbanization progresses, the surge in vehicle numbers has amplified the need for efficient parking management in cities. Traditional parking methods frequently lead to traffic congestion, poor space usage, and user dissatisfaction. This study introduces a smart parking solution that operates independently of Internet of Things (IoT) technology, instead leveraging software-based strategies with minimal hardware dependency. Key Words: Traffic Congestion,Urban Mobility,Space Optimization,Real-Time Availability.

Implementation of Smart Government, Smart Mobility, and Smart Living Policies in Realizing the Quality of Public Services in Yogyakarta City

Major cities around the world believe in the concept of "smart cities" or "intelligent cities". This concept encourages people to participate in city management through a citizen-centered approach. This results in more active and close interaction between citizens and service providers, in this case the local government. This study examines how the Yogyakarta City government can implement e-government to become a Smart City and improve its public services by implementing smart government, smart mobility, and smart living policies. Cities around the world are currently championing this idea as the central government's idea to address various problems in different places. This research uses a descriptive qualitative method by relying on secondary data from digital mass media news which is assisted by data processing using Nvivo 14 as a research tool. Based on the results of the research, it can be found that Smart City in Yogyakarta City shows that (1) Smart Government regarding its online procedures has started to run well; (2) Smart Mobility is superior in traffic monitoring; (3) Smart Living is more prominent in indicators about security. Where with these results, Policy Implementation in the City of Yogyakarta has started to run well, but there must still be improvements, especially in Smart Government, Smart Mobility, and Smart Living so that the quality of public services in the City of Yogyakarta can run even better. Key word: Implementation of smart government, smart mobility, smart living, Yogyakarta CIty

Management of genomic surveillance for respiratory viral infections circulating in the territory of the city of Moscow

Introduction. Outbreaks of respiratory viral infections have an economic impact on the healthcare system and pose a threat to public well-being. Monitoring for the incidence of acute respiratory viral infections in a metropolitan area, especially during the COVID-19 pandemic, was in dire need of an adequate tool to identify mutations and variants that evade vaccine immunity and initiate an increase in incidence. Genomic surveillance was required for a timely response. Purpose of the study. Management of the Moscow Genomic Center for genomic surveillance for acute respiratory viral infections and COVID-19, with an assessment of the results during seasonal outbreaks in the capital. Materials and methods. With the support of the Moscow City Health Department, management and logistics measures were developed and implemented, the NGS testing program in patient samples on a planned basis and during the period of increased incidence of acute respiratory viral infections and COVID-19, technical procedures for high-throughput sequencing were developed, the security of data exchange and biosafety of medical personnel were ensured. Results. The Moscow Genome Center was established within the structure of the Moscow City Health Department. For the period from October 2023 to April 2024, targeted NGS data (a panel of thirty two pathogens, 6528 samples were tested): SARS-COV-2 dominated the entire period, the incidence of influenza A (H3N2) increased sharply from the beginning of December and decreased sharply by the end of January. Sars-CoV-2 genetic lineage rotation: XBB lineage dominated (most represented: XBB.1.16, XBB.1.16.11) from October to the end of January, then JN lineage prevailed (most represented: JN.1, also found: JN.1.13, JN.1.18, JN.1.19, JN.1.4). Influenza A strain rotation: H3N2 (A/Massachusetts/01/2020) and H3N2 (A/Massachusetts/38/2019) dominated from December to February. Research limitations. A targeted panel of 32 respiratory viruses most common in the Moscow population was used for NGS. Incomplete coverage of the entire viriome is a conditional limitation. Conclusion. Representative, high-quality and continuous genetic surveillance has been managed in the capital. The Moscow Genome Center has successfully implemented the NGS Testing Program and support for the Russian Genomic Resource of Microorganisms VGARus.

Resource Efficiency and the Role of Renewable Energy in Miskolc: The City’s Journey Towards Becoming a Smart City

Miskolc, which is the focus of our investigation, is the fourth most populous city in Hungary and the center of one of the most underdeveloped NUTS2 (basic territorial category for the regional policy of the European Union) regions in the European Union. The socialist heavy industry played a decisive role in the development of the city, the decline of which also left deep traces in the city. In its current position, the city tries to manage its available resources as efficiently as possible, and the city management is open to the use of modern urban development tools. This is supported by the fact that Miskolc was the first Hungarian city to join the Green Cities for Sustainable Europe movement in 2011, and then in 2015, it joined the Triangulum project of the EU Smart Cities and Communities program as a follower city. In the process of becoming a smart city, the dimensions of environmental sustainability and energy efficiency were given a prominent role, which should not be surprising considering the traditions of the city. Within this, we must first mention the construction of the geothermal central heating system, with which the city really took significant steps in this field. The main goal of the study is to develop a new smart local concept closely linked to regional development and the key energy sector, through which the local adaptation of the defining elements of the internationally defined smart city in several forms for the city of Miskolc will be presented. In our study, we review how the results achieved by Miskolc so far and the development plans for the future fit in with the smart energy developments of smart cities. Before exploring the processes in Miskolc, we will deal in more detail with the possibilities inherent in district heating and geothermal energy utilization and Hungary’s capabilities.

Policy, innovation, market or behavior driven effects: energy green transition and environmental management in Chinese heterogeneous cities

Energy green transition (EGT) is currently one of the main measures for countries around the world to address the contradiction between economic growth and increasingly deteriorating environmental and climate issues. Cities are the center of energy consumption. The key to EGT lies in urban energy green transition. Therefore, the focus of this study is on the driving mechanism of urban EGT. Firstly, the spatial-temporal characteristic of EGT in Chinese heterogeneous cities is analyzed by using methods such as gravity model. Secondly, the possible paths includes policy driven, innovation driven, market driven, and behavior driven for urban EGT are discussed through theoretical analysis. Finally, combined with panel data of 236 Chinese cities in 2007-2022, this study empirically analyzes the complex driving mechanism of urban EGT. Results show that: (1) The EGT in Chinese cities is continuing. From the perspective of urban heterogeneous, EGT in 1-tier and 2-tier cities is significantly faster than that in 3-, 4-, and 5-tier cities. The EGT speed in eastern cities is the fastest, while that in northeastern cities is the slowest. The difficulty of EGT in energy resource-based cities is actually the greatest. From the perspective of spatial features, the spatial center of EGT in Chinese cities generally shows a changing trend from northwest to southeast. (2) Policy driven, innovation driven, market driven, and behavior driven constitute the complex driving mechanism of urban EGT, and policy driven is the primary driving force for this round of EGT. (3) Positive effect of economic development level and education level improvement on EGT in Chinese cities is significant while resource endowment and population agglomeration level exhibit significant inhibitory effects. (4) There are significant differences in the core driving force for EGT in heterogeneous cities. Both policy driven effect and market driven effect have the highest impact in 1- and 2-tier cities. Innovation driven effect, market driven effect, and behavior driven effect are only significant in eastern and central cities. In energy resource-based cities, innovation driven effect of green innovation is not significant. This study can assist government departments better in formulating relevant policies to support energy transition, promote technological innovation, design market mechanisms, and guide energy consumption behavior.

Prospects for the development of cartography through the integration of SLAM technology with GIS technology

Abstract. With the continued development of Simultaneous Localization and Mapping (SLAM) and Geographic Information Systems (GIS) technologies, their application scenarios have become increasingly complex. Combining these technologies can significantly enhance operational efficiency in challenging environments. This paper presents an analysis of existing cases where SLAM and GIS technologies have been integrated, demonstrating that such a merger facilitates the consolidation and complementarity of spatial data. This integration allows robots or systems to simultaneously utilize the global information provided by GIS and the dynamic local data captured by SLAM for a more comprehensive and detailed environmental analysis, which is highly beneficial for the field of cartography. Further research has developed a series of operational procedures for integrating SLAM and GIS, utilizing MATLAB as a tool. This study also reviews several existing technical challenges, including real-time performance and computational capacity, environmental complexity and dynamic changes, and multi-scale data processing, and proposes potential solutions. The paper concludes by predicting that the integration of SLAM and GIS will play a crucial role in areas such as smart city management and disaster emergency response, indicating that this research area will become a hot topic in future cartographic technology.

IoT-enabled smart cities towards green energy systems: a review

<span>Integration of internet of things (IoT) in smart city management to improve various functions and living standards due to increasing population growth has dramatically evolved ubiquitous and essential services at various stages of urbanization. Hence, smart cities need to be eco-friendly by improving various sectors like education, health, and transport to provide an urban and sustainable quality of life through solving complicated green energy networks, controlling toxic pollution risks, and public safety. Linking optimized green energy systems with the production and automation of advanced applications is crucial to compose implementation strategies for smart city services. This paper aims to conduct a review on eco-friendly plans and infrastructure of IoT-enabled smart cities by exploiting green energy approaches. This study performs critical observations, ideas, and analyses of recent research in the context of our mentioned research theme. This paper points out the technical and functional challenges of an optimal performance-based green IoT-enabled smart city infrastructure. In this sense, this study organizes observations of relevant initiatives, technologies, and experiences in IoT-enabled smart cities, as well as how to embed it with green energy. Moreover, it can provide significant directions to intellectuals and authorities to develop IoT-enabled smart city applications for prospective research.</span>

Study of Access Control Techniques on the Blockchain-enabled Secure Data Sharing Scheme in Edge Computing

Background: The pervasive adoption of edge computing is reshaping real-time big data analysis, smart city management, intelligent transportation, and various other domains. Its appeal lies in its distributed nature, decentralization, low latency, mobile support, and spatial awareness. However, this ubiquity exposes data to security threats, jeopardizing privacy and integrity. Consequently, access control assumes paramount importance in securing data sharing within edge computing and blockchain technologies. Methods: This research addresses this critical issue by conducting a comprehensive study on access control techniques within the context of edge computing and blockchain for secure data sharing. Our methodology commences with an exhaustive review of relevant articles, aiming to identify and expound upon gaps in existing research. Subsequently, we perform a meticulous analysis of access control methods, mechanisms, and performance metrics, seeking to establish a holistic understanding of the landscape. Results: The culmination of this research effort is a multifaceted contribution. We distill insights from a diverse range of access control schemes, shedding light on their nuances and effectiveness. Our analysis extends to evaluating the performance metrics vital for ensuring robust access control. Through this research, we also pinpoint critical research gaps within traditional access control methods, creating a foundation for innovative approaches to address the evolving challenges within edge computing and blockchain environments. Conclusion: In conclusion, this research venture paves the way for secure data sharing in edge computing and blockchain by offering a thorough examination of access control. The findings from this study are anticipated to guide future developments in access control techniques and facilitate the evolution of secure, privacy-conscious, and efficient data sharing practices in the dynamic landscape of digital technology.

Artificial intelligence and IoT driven system architecture for municipality waste management in smart cities: A review

Numerous devices, including sensors, RF ID, and other types of smart devices, have been developed as a result of the Artificial Intelligence (AI), and Internet of Things (IoT) revolution. Urban areas can become smart by monitoring and collecting data about their surroundings through the deployment of technologies with powerful computational capabilities and those that are converted into intelligent things. Waste management is among the most significant issues in smart cities, a rise in metropolitan regions and faster increases in population are the main reasons. When it comes to gathering data about waste management, intelligent services can serve as the front line. Waste management with IoT support is a common example of a service offered by smart cities. Various duties, like gathering, processing, and use of waste in appropriate facilities, are included in waste management. The present study proposed an updated waste management system architecture design after reviewing existing artificial intelligence and IoT-based waste management systems and automation in smart cities. The proposed system architecture deals with the automation of municipality trash in smarter urban areas, using IoT technology and sending notification messages based on sensor data relating to the dustbin state, such as full or empty. The notifications are sent simultaneously to the municipality office and the waste carrier vehicle driver, so that waste can be emptied on time. The proposed system architecture represents a scalable and adaptable model for municipalities that aim to transform their waste collection processes and play a key step in minimizing municipality waste in smart cities. By deploying this proposed system architecture with smart sensors and IoT devices, municipalities can monitor waste levels to ensure that bins are emptied when it is necessary. This reduces the frequency of waste collection, lowers fuel consumption, and minimizes operational costs. The Route optimization algorithms further enhance efficiency by determining the most efficient paths for waste collection trucks, so they can reduce travel time and fuel emissions.

Plant responses to urban gradients: Extinction, plasticity, adaptation

Abstract Biodiversity‐oriented urban management and planning require information on the drivers of wildlife composition and ecosystem function within cities. Urban landscapes impose environmental gradients along which species may be filtered away, or respond by showing adaptive variation in functional trait values. Such trait variation may in turn be due to a species' phenotypic plasticity, or a consequence of microevolution leading to local adaptation. This study investigates three possible plant responses to urban environmental gradients, with different evolutionary consequences: extinction, plasticity and adaptation. We assessed whether individual functional traits (LMA—leaf mass per area, plant height and flower length), population performance traits (seed mass and germination rate), as well as species frequency in the plant community, responded to gradients in mowing frequency, soil fertility and structure, temperature and surrounding mean building height, among four herbaceous plant species present in the metropolitan area of Strasbourg. Using a common garden experiment, we tested whether the observed trait variation was hereditary, and may thus constitute evidence for local adaptation. Our results detected the three types of expected responses. Plantago lanceolata is plastic to urban gradients, and Trifolium pratense showed both plastic and hereditary responses. Dactylis glomerata and Medicago lupulina showed all three responses: they both declined under increasing mowing frequency, were plastic to surrounding mean building height, and showed hereditary responses to different urban gradients. Urban management and planning therefore impact on the evolutionary capabilities of plants in cities. In the case of management this is highlighted by the detected trends in species' traits and frequency in response to mowing. The consequences of urban planning are evidenced by mean building height eliciting most often plastic and adaptive responses. Synthesis. Herbaceous plants often change their morphology in response to urban conditions: grass cutting, altered soils, warmer temperatures and being surrounded by tightly packed buildings. These changes are sometimes hereditary, which suggests that city management and planning affect the ability of plants to survive and evolve in urban environments.

Total Quality Management (TQM) for the development of future smart and integrated cities and sustainable development

Sustainable cities have been among governments prevailing approaches for promoting and achieving goal 11 of the United Nations in urban areas. However, development of sustainable cities policies and measures of cities does not follow an organized and consistent approach for planning, designing, applying, and evaluating a comprehensive sustainable cities system. In that context, the objective of this article is to propose and present total quality management (TQM) as a tool that would aid cities in systematically developing to achieve this goal, literature review of research publications analyze. The research fellow descriptive analysis method.the results obtained in this literature review to find total quality management related to sustainable cities. It was concluded that creating a sustainable city and transportation urban requires understanding the total quality management (TQM) tools by policymakers and practitioners and urban center managers with the implementation and management of this tool. The total management for sustainable cities can assist as a viable management tool to more efficient use of resources in order to develop sustainable cities and achieve sustainability and sustainable transportation.

Problematizing street vending: The uncanniness of a disembodied urban policy

ABSTRACT Street vending is an important source of livelihood for many urban dwellers in Africa but remains a challenge for urban policy and planning. This paper uses Ghana as a case study to examine how street vending is treated in urban policy frameworks. We use qualitative content analysis to critically review four major urban policies that shape the planning, development, and management of Ghanaian cities. Our findings reveal a misalignment of local urban policies with national policy on the regulation of street vending. The regulation of street vending is unclear, contradictory, and ineffective, failing to provide a clear policy direction and adequate planning tools for integrating street vending into urban spaces. We argue that these policy inconsistencies persist due to power imbalances and governance challenges, with local authorities maintaining ambiguous policies that benefit powerful stakeholders while keeping street vendors in a state of insecurity. We call for more coherent and inclusive policies that recognize the socioeconomic value of street vending and foster more inclusive urban spaces. We also highlight the need for reformed urban governance that empowers marginalized voices and builds cross-scale alliances to address the complexities of urban informality and support the informal economy.

Tracing offshore marine ecosystem asset changes based on physical accounting: A case of Xiamen Sea Area

Assessing temporal and spatial changes in marine ecosystem assets is critical for the capitalized management of natural resources and their contribution to human well-being. However, quantifying these changes using different inclusion methods in the System National Account is a challenge. Ecosystem accounting has become a popular topic in recent years, particularly after the System of Environmental and Economic Accounting-Ecosystem Accounting was published in 2021. However, few practical studies have been conducted on offshore marine ecosystem accounting, including marine ecosystem assets and services. This study provides a systematic framework to prioritize the assessment of offshore marine ecosystem assets (extent and condition) in physical terms, including the determined indicators of physical accounts and the presentation of accounting results. We then used this framework to assess marine ecosystem assets in the Xiamen Sea Area selected from 2006 to 2020. The results show that: (a) The extent of the marine ecosystem in Xiamen decreased by 8.34% from 349.64 km2 to 320.49 km2, mainly caused by converting intertidal ecosystem into the land due to the demand for urban and industrial use sea. (b) The area of intertidal vegetation habitats (mangroves and salt marshes) increased from 23.22 hm2 to 132.2 hm2, with an increase of 4.7 times. The results also show that the restoration of the marine ecology in Xiamen has achieved remarkable results. (c) The marine ecosystem condition comprehensive index in Xiamen increased by 3.13% from 0.32 to 0.33. This shows that the marine ecosystem conditions in Xiamen remain well and have even slightly improved under the pressure of a rapidly developing economy and increasing population. This study provides practical technical methods for assessing offshore marine ecosystem in physical term to support integrated coastal management in coastal cities, and is important for assessing marine ecosystem services in the future.

High‐Accuracy Liquid Flow Monitoring via Triboelectric Nanogenerator Combined with Bionic Design and Common‐Mode Interference Suppression

AbstractIn the development of smart cities, accurate liquid flow monitoring is essential for the efficient operation of water supply systems. Current flow sensors often face limitations in sensitivity and environmental adaptability, affecting measurement accuracy, and restricting their application in smart city infrastructure. To address these challenges, this study proposes a high‐accuracy flow monitoring method. Specifically, by combining the bionic design with advanced signal processing techniques, the sensitivity and anti‐interference ability are improved, respectively, to enhance the measurement accuracy. Based on this method, a self‐powered flow sensor (SPFS) is developed using noncontact triboelectric nanogenerators (NC‐TENGs) as the sensing unit. The SPFS achieves a sensitivity of 2.07 Hz L−1 min−1 and improves the signal‐to‐noise ratio by more than 13 times over the initial sensing signal. In addition, an intelligent system is developed to accurately measure water resources. The maximum flow rate error rate is less than 0.97% compared to commercial flow sensors. The SPFS demonstrates higher sensitivity and accuracy compared to the existing TENG flow sensors. This study addresses the limitations of existing flow sensors and pioneers a novel solution for enhanced water resource management in smart cities.

A Practice of City-scale 3D Geographic Entity Representation and Application in China: The Smart Chongqing City Centre

Abstract. The Chinese photogrammetry industry is currently experiencing a significant revolution driven by digital intelligence technologies. Traditional city-scale 3D modelling, typically reconstructed from oblique photogrammetry or laser points, is facing increasing limitations in refined expression, real-time rendering, machine recognition, data sharing, and interoperability due to its large data volume and mesh structure. These limitations make it difficult to meet the substantial demands of spatial analysis, simulation, and prediction. The concept of geographic entities, which are machine-recognizable spatial data structures, is anticipated to provide a competitive solution for these challenges. This work employs 3D scene modelling and geographic knowledge graphs to construct geographic entities with semantic relationships at city-scale, addressing the limitations encountered in the application of traditional mesh/point-based 3D models. A case study in Chongqing city centre demonstrates the feasibility and effectiveness of the proposed approach and its potential applications in simulation, prediction, early warning, and feedback for urban planning and smart city management.

Visualising human life in volumetric cities: City digital twins and other disasters

City digital twins (CDTs) are attracting considerable attention as a tool for the management of cities facing many kinds of crises. CDTs are three-dimensional, animated digital models of cities that display the big data generated by urban sensors in near-real-time. Most of the academic literature on CDTs consists of technical discussions of their design or implementation. This paper instead proposes a different context for understanding CDTs. It adopts an intersectional feminist and techno-cultural analysis of the visual form of power enacted by CDTs, as exemplary of a much wider turn in contemporary visual culture towards visualising the world digitally in three dimensions. Many critical accounts of this visual and volumetric regime are attentive to the forms of corporealised human life that emerge in relation to the picturing of various volumes. This paper examines how CDTs visibly co-constitute a number of digitally-mediated forms of human life, including its user and its human inhabitants. The paper argues that, while both the technological affordances and the cultural imaginary of CDTs perform the kind of powerful white masculinity that sees space as transparent and actionable, discussions of CDTs also persistently generate an excessive form of human life that is understood as untwinnable. The paper argues that masculinist anxieties around this excess erupt in another form of digitally-animated volumetric city facing disaster: the disaster movie. The paper thus makes an argument about the specific form of white masculinity co-constituted with CDTs and insists on the necessity of critical techno-cultural analyses of urban management technologies.

A multi-source domain feature-decision dual fusion adversarial transfer network for cross-domain anti-noise mechanical fault diagnosis in sustainable city

Rotating machinery forms the critical backbone of infrastructure in a sustainable city, with bearings playing a pivotal role as key mechanical transmission components. Therefore, the health status of these bearings directly influences the safe operation of the infrastructure. Accurate and reliable diagnosis of defects in these components minimizes downtime, reduces maintenance costs, and prevents major accidents, ultimately providing insights in the construction and management of a sustainable city. Typically, in actual industrial scenarios, varying working conditions and various types of machines can result in significant discrepancies in the distribution of sample data. Moreover, the non-negligible noise may degrade the diagnostic performance. Therefore, realizing an accurate and reliable bearing diagnosis considering the cross-domain and noise environment remains a challenge. Leveraging the merits of information fusion and multi-source domain transfer learning, this article proposes a multi-source domain feature-decision dual fusion adversarial transfer network (DFATN) to break through the aforesaid limitations. Initially, an adversarial transfer framework is developed, incorporating novel feature matching evaluation and joint distribution difference losses. This framework is designed to facilitate the learning of feature invariants across domains and to enhance the sharing of domain-specific knowledge, even in noise. Relying on channel-spatial interactive feature fusion, a multi-scale feature extractor (MFE) is constructed to share the interaction and enhance the modeling of complex features in multiple dimensions. Additionally, a fault state-related decision fusion mechanism (SDF) is also implemented to integrate diagnostic information, significantly enhancing the generalization performance and robustness of the proposed network. By employing both public Paderborn University (PU) and laboratory-collected (Lab) datasets, the effectiveness and superiority of the proposed DFATN on bearing fault diagnosis are validated. For cross-working condition tasks, the proposed method realizes impressive performance, with average accuracies of 96.52% and 98.76% for Paderborn University (PU) and laboratory-collected (Lab) datasets, respectively. For cross-machine tasks, the average accuracy is 83.36%, outperforming other latest cross-domain fault diagnosis techniques.

Dynamic patterns and potential drivers of river water quality in a coastal city: Insights from a machine-learning-based framework and water management

River water quality continues to deteriorate under the coupled effects of climate change and human activities. Machine learning (ML) is a promising approach for analyzing water quality. Nevertheless, the spatiotemporal dynamics of river water quality and their potential mechanisms in changing environments remain incomprehensively understood through available ML-based researches. Here, we developed a ML-based framework integrating a self-organizing map (SOM) model with a random forest (RF) model. This framework was applied to simultaneously investigate the spatiotemporal patterns and potential drivers of river permanganate (CODMn), ammonia nitrogen (NH3-N), and total phosphorus (TP) dynamics across 34 sites from 2010 to 2020 in a coastal city threatened by deteriorating water environment in southeastern China. The sites were divided into two clusters in the spatial context with different water quality conditions. The year of 2015 for NH3-N and 2018 for CODMn and TP were identified as the key turning points of water quality variations. Features including sewage discharge, population dynamics, percentage of cultivated land, and fertilizer application contributed greatly to water quality deterioration. The increase in forest vegetation reflected by percentage of forest and leaf area index may improve water quality. The ML-based modeling framework demonstrated a promising way to address the spatiotemporal dynamics of river water quality, and provided insights for water management in a coastal city with intensifying human-nature interactions.

The responses of ecosystem services in coastal cities to urbanization in 30 years: A case study of Qingdao City, China

Coastal urban areas play a pivotal role in regional economic growth, yet they are also sensitive regions where the natural environment interacts with human activities. Rapid urbanization has led to ecological issues, such as the degradation of key ecosystem services (ESs) in coastal cities. Understanding the impact of urbanization on the supply and demand of ESs in coastal cities is crucial for ensuring the healthy and sustainable development of the socio-economy in these areas. This study takes the coastal city of Qingdao as a case study, with a focus on the supply and demand relationships of seven key ESs—carbon sequestration, food production, habitat quality, recreational capacity, soil retention, water yield and marine aquaculture—as well as their responses to population growth, economic development, and spatial expansion, which are three distinct aspects of urbanization. The findings indicate that: (I) From 1992 to 2022, the evolution of different ESs exhibited variability, all displaying notable spatial imbalances. (II) During this period, the urbanization process in Qingdao had a pronounced negative effect on the supply and demand of ESs. Spatial urbanization had the most significant impact on carbon sequestration, food production, habitat quality, water yield, and marine aquaculture, while population urbanization most notably affected recreational capacity. (III) The sensitivity of different types of ESs to population, economic, and spatial urbanization varies. Carbon sequestration, food production, recreational capacity, soil retention, water yield, and marine aquaculture were most sensitive to economic urbanization, whereas habitat quality was most sensitive to spatial urbanization. This research provides a basis for decision-making in the sustainable development and comprehensive management of coastal cities and their ecosystems.

Application of AI and IoT in Traffic Management of Large Metropolitan Cities

Urban traffic congestion is a growing problem worldwide, leading to wasted time, fuel, and increased emissions. To address this challenge, smart traffic management systems powered by the Internet of Things (IoT) and Artificial Intelligence (AI) are emerging as a promising solution. This paper explores how these technologies can be combined to create a more efficient and sustainable traffic management system for large cities.