Urban Management Research Articles

The Relationship Between Municipal Management and Sustainable Tourism in Urban Protected Areas: A Quantitative Study

The Relationship Between Municipal Management and Sustainable Tourism in Urban Protected Areas: A Quantitative Study

Reframing resilience-oriented urban water management: Learning from social-ecological-technological system interactions and uncertainties in a water-scarce city

Reframing resilience-oriented urban water management: Learning from social-ecological-technological system interactions and uncertainties in a water-scarce city

Review of UAV based on Reconfigurable Intelligent Surface and ISAC

This paper discusses the application of Reconfigurable Intelligent Surface (RIS) technology combined with Unmanned Aerial Vehicle (UAV) technology, and discusses their innovative potential in 6G communication networks. Especially in the field of Integrated Sensing and Communication (ISAC) application prospects. RIS is a two-dimensional surface based on electromagnetic metamaterial technology. Through dynamic regulation of electromagnetic units, parameters such as phase and amplitude of incident electromagnetic waves can be flexibly controlled, so as to optimize wireless channel and improve communication performance. Compared with traditional communication systems, RIS has the advantages of low cost, low power consumption and easy deployment, which can significantly improve the stability and coverage of UAS communication links. Through in-depth analysis of the working principle, hardware architecture and advantages of RIS combined with UAV, this paper elaborates its application scenarios in UAV network, including disaster relief, urban management, intelligent transportation and battlefield reconnaissance. With RIS’s intelligent beamforming and channel optimization, UAVs can not only provide efficient communication services, but also enable accurate environment awareness and target tracking. In addition, this paper analyzes the key challenges faced by RIS-assisted UAVs in practical engineering applications, such as the complexity of channel estimation, multi-UAV cooperative communication, and adaptability in dynamic environments. By proposing several solutions, including optimizing channel estimation technology and improving beamforming design, this paper provides theoretical basis and technical support for the wide application of RIS and UAV in 6G networks in the future. The research shows that the introduction of RIS technology will greatly improve the communication ability of UAVs in complex environments, and promote the integration of communication and perception in the 6G era.

Energy-Efficient and Comprehensive Garbage Bin Overflow Detection Model Based on Spiking Neural Networks

With urbanization and population growth, waste management has become a pressing issue. Intelligent detection systems using deep learning algorithms to monitor garbage bin overflow in real time have emerged as a key solution. However, these systems often face challenges such as lack of dataset diversity and high energy consumption due to the extensive use of IoT devices. To address these challenges, we developed the Garbage Bin Status (GBS) dataset, which includes 16,771 images. Among them, 8408 images were generated using the Stable Diffusion model, depicting garbage bins under diverse weather and lighting scenarios. This enriched dataset enhances the generalization of garbage bin overflow detection models across various environmental conditions. We also created an energy-efficient model called HERD-YOLO based on Spiking Neural Networks. HERD-YOLO reduces energy consumption by 89.2% compared to artificial neural networks and outperforms the state-of-the-art EMS-YOLO in both energy efficiency and detection performance. This makes HERD-YOLO a promising solution for sustainable and efficient urban waste management, contributing to a better urban environment.

Building Segmentation in Urban and Rural Areas with MFA-Net: A Multidimensional Feature Adjustment Approach

Deep-learning-based methods are crucial for building extraction from high-resolution remote sensing images, playing a key role in applications like natural disaster response, land resource management, and smart city development. However, extracting precise building from complex urban and rural environments remains challenging due to spectral variability and intricate background interference, particularly in densely packed and small buildings. To address these issues, we propose an enhanced U2-Net architecture, MFA-Net, which incorporates two key innovations: a Multidimensional Feature Adjustment (MFA) module that refines feature representations through Cascaded Channel, Spatial, and Multiscale Weighting Mechanisms and a Dynamic Fusion Loss function that enhances edge geometric fidelity. Evaluation on three datasets (Urban, Rural, and WHU) reveals that MFA-Net outperforms existing methods, with average improvements of 6% in F1-score and 7.3% in IoU and an average increase of 9.9% in training time. These advancements significantly improve edge delineation and the segmentation of dense building clusters, making MFA-Net especially beneficial for urban planning and land resource management.

Sources and Use of Bicycle Traffic Data in Research and Urban Mobility Management

Sources and Use of Bicycle Traffic Data in Research and Urban Mobility Management

Exploring impact of digital economy on sustainable urban development by panel data of 30 underdeveloped cities in China

How the digital economy affects sustainable urban development is a worthwhile research problem. Based on the panel data of 30 underdeveloped cities in China from 2011–2020, the Super-SBM model and dynamic QCA (Qualitative Comparative Analysis) method were combined to analyze the complex relationship between the digital economy and sustainable urban development. From the configuration perspective, exploring how to use the digital economy to enhance the green development level of some underdeveloped cities in northwest China is of great significance for filling this gap, establishing a cross-city green coordinated development mechanism, and formulating sustainable development policies for urban management departments. It has been found that individual digital economy elements are not necessary to enhance the level of sustainable urban development. There exist three different models for achieving green and high-quality development: high penetration effect, high scale-penetration effect, and high scale effect. For the high penetration effect model, policies promoting the development of high digital industries, high levels of economic development, a high degree of openness to the outside world, and non-high industrial structures, can be made as core conditions, with digital infrastructure construction as auxiliary conditions to fully generate high green development efficiency. For the large-scale penetration effect model, policies promoting the development of high digital industries, high digital infrastructure, high digital finance, high economic development level, high industrial structure, and non-high technique progress can generate high green development efficiency. For the large-scale effect model, policies promoting the development of non-high digital industries, high digital finance, high degree of openness to the outside world, high industrial structure, and non-high technique progress can be made as core conditions, with economic development level as auxiliary conditions, can fully generate high green development efficiency.

Urban facility management as an intermediary: navigating the trade-off between sustainable development and democracy

Purpose This paper explores the inherent conflicts within the sustainable development paradigm, particularly the tension between long-term environmental planning and short-term economic and social priorities in democratic governance. It argues that resolving these conflicts requires an intermediary actor with specific characteristics, capable of balancing rational planning with participatory, incremental decision-making. Urban facility management (FM) is introduced as a potential candidate for this role, offering a structured yet adaptive approach to sustainable urban governance. This paper aims to highlight the importance of recognizing and legitimizing urban FM as a key facilitator in achieving both sustainability and democratic engagement. Design/methodology/approach This study adopts a primarily theoretical and reflective approach, critically evaluate collective and personal research experiences, using diverse methods such as literature review, active engagement in international and local conferences, seminars and collaborative workshops with stakeholders. Findings Urban FM, which integrates principles of FM and urban planning, has the potential to act as a crucial intermediary within governance networks. By merging distinct yet complementary approaches − one rooted in rational calculation, which focuses on long-term planning and sustainability, and the other in culture-bound, rule-following behavior, which emphasizes ongoing maintenance, efficiency and value creation − urban FM offers a comprehensive framework for addressing the complexities of urban sustainability. Despite its significance, urban FM often does not receive the recognition and attention it deserves within the broader landscape of urban governance. This paper advocates for strategies aimed at enhancing public understanding and raising awareness of urban FM among various professional circles. By doing so, we can foster greater collaboration and stakeholder engagement, ultimately leading to more sustainable and inclusive urban environments. Originality/value This paper offers a novel interdisciplinary perspective by applying governance and democracy theories to urban FM. Few studies have explored urban FM’s role as an intermediary in multilevel governance networks. It addresses the lack of deep research into intermediaries’ roles, especially in balancing sustainable development and democratic engagement.

Improvement of 3D Green Volume Estimation Method for Individual Street Trees Based on TLS Data

Vertical structure monitoring of urban vegetation provides data support for urban green space planning and ecological management, playing a significant role in promoting sustainable urban ecological development. Three-dimensional green volume (3DGV) is a comprehensive index used to characterize the ecological benefit of urban vegetation. As a critical component of urban vegetation, street trees play a key role in urban ecological benefits evaluation, and the quantitative estimation of their 3DGV serves as the foundation for this assessment. However, current methods for measuring 3DGV based on point cloud data often suffer from issues of overestimation or underestimation. To improve the accuracy of the 3DGV for urban street trees, this study proposed a novel approach that used convex hull coupling k-means clustering convex hulls. A new method based on terrestrial laser scanning (TLS) data was proposed, referred to as the Convex Hull Coupling Method (CHCM). This method divides the tree crown into two parts in the vertical direction according to the point cloud density, which better adapts to the lower density of the upper layer of TLS data and obtains a more accurate 3DGV of individual trees. To validate the effectiveness of the CHCM method, 30 sycamore (Platanus × acerifolia (Aiton) Willd.) plants were used as research objects. We used the CHCM and five traditional 3DGV calculation methods (frustum method, convex hull method, k-means clustering convex hulls, alpha-shape algorithm, and voxel-based method) to calculate the 3DGV of individual trees. Additionally, the 3DGV was predicted and analyzed using five fitting models. The results show the following: (1) Compared with the traditional methods, the CHCM improves the estimation accuracy of the 3DGV of individual trees and shows a high consistency in the data verification, which indicates that the CHCM method is stable and reliable, and (2) the fitting results R² of the five models were all above 0.75, with the exponential function model showing the best fitting accuracy (R2 = 0.89, RMSE = 74.85 m3). These results indicate that for TLS data, the CHCM can achieve more accurate 3DGV estimates for individual trees, outperforming traditional methods in both applicability and accuracy. The research results not only offer a novel technical approach for 3DGV calculation using TLS data but also establish a reliable quantitative foundation for the scientific assessment of the ecological benefits of urban street trees and green space planning.

IoT-driven smart solutions: transforming urban tourism destinations

PurposeThis research explores how IoT contributes to the smart management of Tehran as an emerging urban tourism destination. It examines IoT’s potential to enhance smartification efforts, supporting DMOs and SMTEs in achieving integrated destination management and sustainable economic outcomes.Design/methodology/approachThis study employs a future-oriented mixed-method research design, using cross-impact analysis (CIA) and cross-impact balance (CIB) to identify plausible IoT implementation scenarios for transforming Tehran into a smart urban tourism destination. These methodologies assess potential future developments and IoT integration strategies in tourism.FindingsThe analysis identifies four potential scenarios, with the first emerging as the most plausible. This scenario highlights multi-sided business models, transformative innovation, a smart technological ecosystem, blockchain mechanisms, an instrumented supply chain, agile software development and an intra-organizational participative model. Together, these elements enhance the perceived usefulness of IoT, driving smart urban tourism management and sustainable development in Tehran.Practical implicationsThis study provides actionable insights for policymakers, planners and stakeholders to enhance urban tourism through IoT. For cities like Tehran, adopting the proposed nine-strategy scenario can improve smartness, sustainability and competitiveness. Collaboration among DMOs, SMTEs and local governments is essential for developing IoT infrastructure, optimizing energy, enhancing marketing and integrating blockchain, ultimately improving operations and tourist experiences.Originality/valueThis study provides a unique contribution by combining future-oriented techniques to evaluate IoT’s role in the smartification of urban tourism destinations. It offers a strategic vision for integrating advanced technologies, which can significantly improve Tehran’s urban development, competitiveness and sustainability as a smart tourism destination.

Alavancas de políticas públicas de gestão urbana para a transição para a economia circular no CONLESTE

The linear economy is based on the model of producing, consuming and generating waste with economic growth based on the exploitation of finite resources, and contributes to the formation of cities in crisis pressured by speculative capital and propagating various contaminants. In response, the circular economy stands out, which proposes economic growth dissociated from the use of finite resources without generating these contaminants. As a result of circularity, public policy instrument for urban management emerge to promote the redesign of territorial space. This article seeks to assess whether and how CONLESTE will be able to implement these urban management levers to introduce and promote the circular economy in its regional territory based on the provisions of ENEC. It is expected that the results achieved by this work will be useful in disseminating debates on the transition to circularity with the recovery of the human dimension for cities.

Predicting and simulating urban expansion patterns in the Asan watershed, Uttarakhand, India using the SLEUTH model for sustainable land management

Urban growth monitoring and assessment are crucial for sustainable long-term planning and the efficient utilization of natural resources. Unplanned urbanisation poses risks such as pollution and environmental disruption, emphasizing the need for proactive management. Changes in Land Use and Land Cover (LULC) with time indicate ongoing urbanisation trends. This study aims to predict and simulate urban growth in the Asan watershed, Uttarakhand, India, through 2040 using the SLEUTH model. The SLEUTH model (Slope, Land use, Exclusion, Urban, Transportation, Hillshade) grounded in Cellular Automata (CA) principles is applied to analyse and project LULC changes from 2016 to 2040, assessing their impacts on the surrounding areas. The research utilizes primary datasets including the Digital Elevation Model (DEM) and LULC maps, to forecast future urban expansion. Recent methodologies prioritize the detection of LULC changes through multispectral satellite images, emphasizing factors like radiometric efficiency, spatial uniformity, and climatic conditions. The predicted urban growth output revealed that the projected increase of urban area by 2040 will be 67.73 km2 from 36.5 km2 in 2016 with an increment of 31.23 km2. Additionally, by 2040, urban settlement is expected to occupy around 9.5% of the total watershed area, an increase of 4.3% from the urban area observed in 2016. The study aims to guide infrastructure planning and promote sustainable development practices by comprehending urban dynamics, growth patterns, and resource management.

Urban Freight Traffic Management and Control Mode under the Concept of Green Traffic

In the context of global advocacy for sustainable development, the concept of green transportation has emerged as a core guiding principle in urban traffic reform. With the acceleration of urbanization, the demand for urban freight is growing steadily. As the lifeline of urban economic operations, freight transport not only provides efficient logistics services but also poses significant challenges to urban environments and traffic congestion management. In this context, this paper explores innovative models for managing and controlling urban freight transport based on the principles of green transportation. This effort is not only crucial for improving urban ecological environments but also holds profound implications for enhancing overall urban competitiveness and achieving coordinated development between the economy and the environment.

Emission Dynamics and Public Health Implications of Airborne Pathogens and Antimicrobial Resistance from Urban Waste Collection Facilities.

Airborne pathogens and antimicrobial resistance (AMR) present significant global health threats. Household waste collection facilities (WCFs), crucial initial nodes in urban waste management systems, have been understudied in regards to their role in emitting these hazards. This study investigated the abundance, composition, sources, driving mechanisms, and health risks associated with pathogens and AMR originating from WCFs in a major city, using culture-based analysis, high-throughput sequencing, and health risk modeling, respectively. The atmospheric escape rates of culturable bacteria (43.4%), fungi (71.7%), and antibiotic-resistant bacteria (ARB) (43.7%) were estimated based on the concentration differences between the interior and exterior of the WCFs by using SourceTracker2 analysis. Health risk assessments showed that annual infection risks for waste-handling workers ranged from 0.194 to 0.489, far exceeding the World Health Organization's acceptable limit of 10-4. Community exposure risks were notable up to 220 m downwind from WCFs, marking the maximum extent of pathogen dispersion. Our analysis suggests that approximately 6.3% of the megacity's area (equivalent to 400 km2) is within potential risk zones influenced by WCF emissions. These results underscore the critical need to evaluate and mitigate the public health risks posed by airborne pathogens and AMR emitted from WCFs in megacities globally.

Identification and Temporal Distribution of Typical Rainfall Types Based on K-Means++ Clustering and Probability Distribution Analysis

Characterizing rainfall events with recurrence periods of 1–5 years is crucial for urban flood risk assessment and water management system design. Traditional hydrological frequency analysis methods inadequately describe the temporal structure and intensity distribution of rainfall. In this study, we analyzed 1580 independent rainfall events in central Hangzhou (1950–2023) using PCA dimension reduction and K-means++ clustering to investigate typical rainfall types across different recurrence periods. The integrated approach effectively captures temporal characteristics while reducing dimensionality and improving clustering efficiency. Our results indicate that concentrated single-peak rainfall with short duration and a mid-to-late peak dominates the region, with longer recurrence periods exhibiting higher intensity, shorter duration, and greater temporal concentration. Furthermore, cumulative distribution function (CDF) and probability density function (PDF) analyses were conducted on these typical rainfall types, quantifying their distributional characteristics and yielding precise mathematical expressions. These standardized rainfall curves provide direct applications for engineering design and hydrological modeling, enabling more accurate flood prediction and mitigation strategies for Hangzhou’s urban infrastructure.

Evaluating the Greenness of Sanandaj City Using Sentinel Imagery in Google Earth Engine

Urban greenery and cooling initiatives have become top priorities for municipalities worldwide as they contribute to improved environmental quality and urban resilience. This study leverages advancements in remote sensing (RS) and cloud-based processing to assess and monitor changes in public urban green spaces (PUGS) in Sanandaj, Iran. Using high-resolution Sentinel-2 imagery (10 m) processed in Google Earth Engine (GEE), we calculated and mapped the normalized difference vegetation index (NDVI) across 20 major PUGSs over a five-year period, from 2019 to 2023. A total of 507 Sentinel-2 images were analyzed, offering a comprehensive view of seasonal and annual greenness trends. Our findings reveal that May is the peak month for greenery, while February consistently shows the lowest NDVI values, indicating seasonal greenness variability. Specifically, the mean NDVI of PUGSs decreased significantly between 2019 and 2022, with values recorded at 0.735, 0.737, 0.622, 0.417, and 0.570 in the greenest month of each respective year, highlighting a noticeable decline in vegetation health and extent. This reduction can be attributed to water scarcity and suboptimal management practices, as evidenced by dried or underperforming green spaces in recent years. Our results underscore the potential of integrating NDVI-based assessments within urban development frameworks to more accurately define and sustain PUGSs in Sanandaj. This methodology provides a replicable approach for cities aiming to optimize urban greenery management through RS technology.

Bio-chelation for sustainable heavy metal remediation in municipal solid waste compost: A critical review of chelation technologies.

Municipal solid waste (MSW) compost is a promising solution for sustainable urban waste management, widely used as a soil amendment and for carbon sequestration. However, heavy metals in MSW compost pose risks to ecosystems, food safety and human health. This review critically examines three decades of research (1994-2024) on heavy metal contamination in MSW compost and household hazardous waste (HHW), identifying gaps in managing these pollutants, particularly regarding hazardous waste co-disposal. It evaluates existing remediation strategies for heavy metal removal, with a focus on chemical-assisted leaching using chelating agents. Key treatment parameters-such as chelating agent concentration, pH, contact time, liquid/solid ratio, temperature and flow rate-are analysed in both batch and continuous modes. The study advocates for biodegradable chelating agents as an effective approach to enhancing MSW compost quality, with applications in landfill reclamation and agriculture. Emphasizing the need for eco-friendly heavy metal mitigation, the review underscores the importance of safe urban composting practices. The findings contribute to the circular economy and Sustainable Development Goals by promoting sustainable and safe MSW compost applications, fostering environmental protection and public health and guiding research and industry toward scalable, marketable remediation solutions.

Urban green spaces and flood disaster management: toward sustainable urban design

This paper explores how rapid urbanization and frequent extreme rainfall affect urban flood risk, highlighting that stormwater management challenges worsen due to increased impervious surfaces, altered urban hydrological cycles, and damaged natural retention systems. This paper, grounded in sustainable urban design principles, underscores the significance of green infrastructure for bolstering urban flood resilience. Urban green spaces not only reduce surface runoff through natural storage and infiltration but also improve water quality, regulate urban microclimate, and enhance biodiversity. Furthermore, this paper examines the application of green infrastructure in stormwater management, drawing inspiration from international success stories such as the Amsterdam Canal Network, Singapore’s ABC Waters Program, and Kazan’s “Resilient Belt” project. The study suggests that an integrated “blue-green-gray” strategy, which combines natural ecosystems with engineered facilities, should be adopted to optimize stormwater management efficiency. Despite the significant advantages of green infrastructure, there are still challenges in scaling up the application, connecting decentralized green spaces, and integrating real-time monitoring systems for dynamic regulation. Future research should focus on the synergistic effects of green infrastructure at different urban scales and explore a balanced path between urban expansion and ecological sustainability.

Municipal Solid Waste Management Using Machine Learning: A Case Study in Sheger City, Koye Sub-city, Ethiopia

Municipal Solid Waste Management is an increasingly critical challenge in urban areas, intensified by rapid urbanization, population growth, and evolving consumption patterns. This study investigates the application of machine learning techniques to predict municipal solid waste generation in Sheger City, Koye Sub-city, Ethiopia, using data from 2009 to 2023. Three machine learning models, ARIMA, RF, and LSTM, were employed to forecast waste generation trends for the period 2024–2028, considering various socio-economic and demographic factors. Among the models, LSTM demonstrated the highest accuracy, with MSE of 1.62 × 10⁸ tonnes, MAE of 9,500 tonnes, and R² of 0.93. These results outperformed ARIMA (MSE = 3.84 × 10⁸ tonnes², MAE = 15,200 tonnes, R² = 0.85) and RF (MSE = 2.91 × 10⁸ tonnes², MAE = 12,800 tonnes, R² = 0.89). The forecasts predict an 8.5% increase in total waste generation, from 3,852,150 tonnes in 2023 to 4,177,500 tonnes by 2028. Notable growth is expected in high-volume waste streams, including food waste (13.5% increase) and plastic waste (8.9% increase). These findings highlight the urgent need for enhanced waste management strategies, including expanded recycling programs and policy interventions. This study provides a robust framework for leveraging machine learning models to guide waste management decisions, contributing to more sustainable urban waste management practices in rapidly growing cities.

Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning

Effective urban traffic management is vital for sustainable city development, relying on intelligent systems with machine learning tasks such as traffic flow prediction and travel time estimation. Traditional approaches usually focus on static road network and trajectory representation learning, and overlook the dynamic nature of traffic states and trajectories, which is crucial for downstream tasks. To address this gap, we propose TRACK, a novel framework to bridge traffic state and trajectory data for dynamic road network and trajectory representation learning. TRACK leverages graph attention networks (GAT) to encode static and spatial road segment features, and introduces a transformer-based model for trajectory representation learning. By incorporating transition probabilities from trajectory data into GAT attention weights, TRACK captures dynamic spatial features of road segments. Meanwhile, TRACK designs a traffic transformer encoder to capture the spatial-temporal dynamics of road segments from traffic state data. To further enhance dynamic representations, TRACK proposes a co-attentional transformer encoder and a trajectory-traffic state matching task. Extensive experiments on real-life urban traffic datasets demonstrate the superiority of TRACK over state-of-the-art baselines. Case studies confirm TRACK’s ability to capture spatial-temporal dynamics effectively.