Search Results (245)

Right-turning vehicles at intersections permitting right turn on red (RTOR) frequently conflict with pedestrians, posing significant safety risks. Existing studies often simplify vehicle trajectories by treating vehicles as centroid points, ignoring the spatial disparities between pedestrians and vehicles. To address this gap, we propose a conflict risk assessment framework based on front and rear wheel trajectories (FRWTs), which accounts for the dynamic differences in vehicle segments during turns. First, we partition vehicles into four segments (inner/outer and front/rear wheels) and develop a trajectory prediction model to quantify risk variations across these segments. Our analysis reveals that the inner front wheel poses the highest collision risk due to its speed, trajectory curvature, and pedestrian proximity. Next, we introduce three conflict interaction modes—hard interaction, no interaction, and soft interaction—and evaluate the applicability of conflict indicators (e.g., Time to Collision (TTC) and Post-Encroachment Time (PET)) under each mode. Using a Support Vector Machine (SVM) classification algorithm, we classify risk severity with high accuracy: 96% for hard interaction, 96% for no interaction, and 97% for soft interaction modes when TTC-PET dual indicators are employed. Our findings demonstrate that FRWT-based modeling significantly improves conflict risk assessment accuracy compared to centroid-point approaches. This work provides actionable insights for proactive traffic safety management and supports the development of targeted conflict mitigation strategies at RTOR intersections. Full article

Traffic safety analysis has traditionally relied on historical road collision data. However, this approach has many limitations due to well-known challenges with the availability and quality of collision data. Moreover, collecting sufficient crash data to develop statistical models for traffic safety analysis is only possible after the societal damage due to collisions has been sustained. Those problems are more likely when studying pedestrian safety. To address these constraints, researchers utilize traffic conflict indicators to identify the severity of conflicts and develop strategies to enhance road safety. This study evaluates Ultra-Wideband (UWB) technology for estimating the post-encroachment time (PET) indicator, a commonly used measure in pedestrian safety. Indoor experiments were conducted to explore potential multipath issues commonly encountered in wireless-based localization systems. The time-division multiple access (TDMA) scheme was utilized by assigning 20 ms time slots for stable communication between a tag and an anchor. To address the different clocks in UWB anchors and tags, the master–slave technique was employed for time synchronization between the devices. The experiments also examined the storage of UWB measurements using a cloud-based global clock for time synchronization. The study found that the mean absolute error (MAE) in PET is 4.92 s under interference conditions and 0.148 s with the TDMA technique between the ground truth and the UWB measurements. The findings offer valuable insights for future studies aimed at enhancing UWB accuracy. Full article

The dual-channel structure resulted from manufacturer encroachment could alter the incentives of downstream retailer to ex ante communicate demand forecast. And different types of channel competition need to be investigated in this dual-channel information sharing scenario. This paper aims to investigate retailer’s ex ante imperfect demand information sharing strategy given that upstream manufacturer has set up direct sales channel (manufacturer encroachment). The imperfect information sharing means the demand information shared is uncertain and has some error relative to the real-world demand condition. It examines two types of channel competition: quantity competition and price competition. Additionally, this study discusses the encroaching manufacturer’s incentives for adjusting channel substitution. The paper adopts a stylized game theoretic model to describe interactions between retailer and the encroaching manufacturer. Contrary to conventional wisdom, the paper shows that under manufacturer encroachment, it is always possible for ex ante demand information sharing. Specifically, in the Cournot competition scenario where retailer channel and the encroaching manufacturer direct channel compete in quantity, the encroaching manufacturer could encourage demand information communication through side payment. Furthermore, in the Bertrand competition scenario, retailer may voluntarily share demand information. In addition, in either quantity or price competition, the encroaching manufacturer has incentives to adjust channel substitution for profit maximization. Full article

Small urban rivers are crucial to global freshwater ecosystems, yet they are disproportionately impacted by human-induced modifications. Existing restoration approaches have primarily focused on large river systems. This study aims to provide a comprehensive, high-resolution assessment of the urban stretch of the Assi River (~7 km) in Varanasi, India, to inform restoration strategies as a representative case study of the challenges faced by small rivers. We used high-resolution unmanned aerial vehicle (UAV) imagery to map the river and collected water quality data from seven sampling sites in October 2022. Our findings reveal a severe loss of multidimensional connectivity. Geospatial analysis revealed extensive encroachment, with built-up areas occupying 137,580 m² along a 100 m length within the 30 m buffer zone, and channel widths constricted to as narrow as 1 m in some sections. Water quality is severely impaired, with dissolved oxygen (DO) levels dropping to a minimum of 0.2 mg/L and faecal coliform levels reaching up to 2.1 × 10⁸ MPN/100 mL. We propose a UAV-based restoration framework that integrates geospatial data with policy recommendations to reconnect the river. However, a limitation of this work is that it is based on single-season sampling and temporal variations; multi-seasonal campaigns will likely improve the framework. The proposed model for urban river management directly addresses SDG 6.3 and 6.6, which target the reduction of water pollution and protecting water-related ecosystems, respectively, and SDG 11.7, which aims to provide access to green spaces. Full article

As human activities such as urbanization encroach on natural areas, some wildlife species adapt to these changes and learn ways to utilize newly available resources. We monitored the use patterns of the Eurasian otter in three urban ponds in southern Spain (Málaga province). We compared weekly otter visits and relative spraint abundance between the urban ponds and two control ponds located in natural areas, testing for differences in use patterns between them using Generalized Linear Mixed Models and the Wald–Wolfowitz run test. We also estimated prey survival rates through Kaplan–Meier estimator curves. We also assessed problems of coexistence with human interests. Relative spraint abundance was not affected by pond type (urban or natural). However, the number of otter visits was lower for urban ponds, and the pattern was not random but concentrated over a short time until prey depletion, suggesting consistent use of the urban feeding patches. Available food resources in urban settings can become a viable option for otters, which appear to explore urban habitats when it suits them. However, in the monitored urban ponds, otters competed with human interests and generated a conservation problem that almost led to lethal measures. Full article

Green belts in metropolises face a significant contradiction between ecological protection constraints and urban sprawl, necessitating effective planning and management. Existing studies have primarily focused on a single dimension, while the factors influencing the spatial evolution of green belts are complex and diverse. This study establishes a multi-objective quantitative analysis framework, utilizing quantitative analysis methods such as average nearest neighbor analysis, landscape ecological index analysis, land–use transition matrix, kernel density estimation, and spatial autocorrelation models. Taking the green belt area of Shijiazhuang as a case study, this research systematically analyzes the spatial evolution characteristics of the region from 2015 to 2024. The findings reveal spatial patterns such as the small-scale and dispersed expansion of industrial land, increasing fragmentation of ecological spaces, ongoing encroachment on agricultural land, differentiated growth of service industry spaces, and the uncontrolled sprawl of residential areas in villages and towns during rapid urbanization. These patterns lead to increased ecological risks, imbalanced urban–rural development, and lagging infrastructure. To address these challenges, this study proposes a planning strategy of “adjusting the primary industry, restricting the secondary industry, and promoting the tertiary industry,” aiming to resolve the conflict between ecological protection and urban expansion in metropolitan green belts, ensuring their orderly development. This research provides insights for the sustainable development of green belts in Metropolises of developing countries during the rapid urbanization process. Full article

As an advanced form of community development, Future Communities (Weilai Shequ) is a policy-led urban initiative launched in Zhejiang, China, that prioritizes human-centered development. However, it is currently confronted with an inherent contradiction: the expansion of technological rationality is encroaching upon humanistic values. Centering on the core “technology–human” relationship, this study is dedicated to exploring development measures for Future Community that synergistically integrate technological empowerment and humanistic care. Using natural language processing techniques (LDA topic modeling), we conducted an exploration and analysis of the thematic characteristics and evolution of 40 policy documents related to future communities issued by the central and local governments of China from 2014 to 2024. The study identifies six core topics: Quality Enhancement, Technical Foundation, Intelligent Operations and Maintenance, Green and Low-Carbon, All-Age Friendliness, and Community Services. Analysis revealed that each theme embodies a dual connotation of both technological and humanistic dimensions. Furthermore, the study revealed that the evolution of policy semantics follows a three-stage developmental pattern: technology dominance and nascent human-centered values; human-centered rise and technology empowerment; and human-centered deepening and technological embeddedness. Based on the above findings, and grounded in a phenomenological perspective, this study integrates Alexander’s human-centered architectural philosophy with Ihde’s theory of technological mediation to propose a future community construction pathway jointly driven by “technological mediation” and “human presence.” Theoretically, this research transcends the binary narrative of technology versus humanism. In practice, it provides policymakers with tools to avoid technological pitfalls. It establishes fundamental principles for planners and designers to implement humanistic values, ultimately aiming to realize, at the community level, the vision of technology serving humanity’s aspiration for a better life. Full article

In recent years, remote sensing technologies have become indispensable for the documentation, analysis, and virtual preservation of historical, architectural, and archaeological heritage. Advances in 3D scanning have enabled the precise digital recording of complex structures as large-scale point clouds, facilitating highly detailed virtual reconstructions. This study evaluates the capability of LiDAR-based Terrestrial Laser Scanning (TLS) for documenting historical monument façades within a 3D environment and generating accurate visualisation models from registered, colourised point clouds. The integration of high-resolution RGB imagery, processed through Reality Capture 1.5 software, enables the automatic production of realistic 3D models that combine geometric accuracy with visual fidelity. Simultaneously, Geographic Information Systems (GIS), particularly cloud-based platforms like ArcGIS Pro Online, enhance spatial data management, mapping, and analysis. When combined with TLS, GIS is part of a broader remote sensing framework that improves heritage documentation regarding precision, speed, and interpretability. The digital survey of the Shanasheel house in Al-Basrah, Iraq, demonstrates the effectiveness of this interdisciplinary approach. These architecturally and culturally significant buildings, renowned for their intricately decorated wooden façades, were digitally recorded using CAD-based methods to support preservation and mitigation against urban and environmental threats. This interdisciplinary workflow demonstrates how remote sensing technologies can play a vital role in heritage conservation, enabling risk assessment, monitoring of urban encroachment, and the protection of endangered cultural landmarks for future generations. Full article

In response to the widespread decline in ecosystem service value (ESV) caused by rapid industrialization and urbanization-driven land-use transitions in Coastal China—characterized by shrinking farmland and expanding built-up land and crystallized in the “core-city sprawl and surrounding-farmland encroachment” pattern—this study integrated land-use and socioeconomic data from 1980 to 2020. Employing the equivalent-factor method and Geodetector model, we quantified the spatiotemporal evolution of ESV and its driving mechanisms across the entire coastal region. The results show that (i) the total ESV experienced a fluctuating increase. (ii) Spatially, the ESV exhibited a “high in the south, low in the north, and higher inland than along the immediate coast” pattern, with mountain–hill belts and estuarine wetlands in the south forming high-value clusters, whereas the Bohai Rim in the north emerged as a low-value zone. (iii) Socioeconomic factors increasingly dominated the driving forces, while NDVI became the most influential natural factor; the interactions between the drivers consistently produced bi-factor enhancement effects. These findings provide a scientific basis for implementing the “Two-Mountains Theory” and optimizing coastal territorial spatial planning. Full article

The resilience of power distribution systems is crucial for maintaining the stability and functionality of modern societies. The proximity of natural vegetation to power lines poses significant risks, particularly when combined with adverse weather events. This review paper examines state-of-the-art methods for detecting and managing tree proximity to power distribution lines using advanced machine learning (ML) techniques, including deep learning (DL) applied to remote sensing data. The complex interactions between adverse weather conditions and power outages caused by tree encroachment are explored. The potential of AI-driven monitoring systems to enhance vegetation management strategies, thereby mitigating the risks associated with tree-related power outages, is underlined. A significant gap in the literature is identified, with few studies specifically addressing the application of ML/DL for dynamic monitoring of tree proximity to power lines. A detailed comparative analysis of existing methodologies is provided, emphasizing the unique contributions and limitations of current approaches. Future research directions, including the development of more sophisticated ML/DL models and the integration of multi-sensor data, are outlined. This review serves as a critical resource for researchers, utility managers, and policymakers aiming to improve the resilience and reliability of power infrastructure management. Full article

Water conservation, as a critical ecosystem service, plays a vital role in maintaining regional water resources balance. Against the backdrop of rapid urbanization, the expansion of construction land has intensified the encroachment on ecological spaces, posing significant challenges to water resource carrying capacity. From a supply–demand perspective, this study employs the InVEST model and integrates multi-source data including meteorological and socio-economic datasets to construct models of water conservation supply and demand. Furthermore, spatial analysis methods are applied to examine the evolution of water resource carrying capacity in Gansu Province—a key region within the Yellow River Basin—from 2000 to 2020. The results indicate the following: (1) through desertification control, unused land has been progressively restored to grassland, yet continuous urban expansion has substantially encroached upon surrounding plowland and grassland; (2) the spatial pattern of water conservation supply exhibits a “high in the south and west, low in the north and east” distribution, with the maximum value per pixel increasing from 7.89 × 10⁵ m³ to 8.15 × 10⁵ m³. Overall, water resource carrying capacity has generally declined, with intensified pressure in central cities such as Lanzhou, while some improvement is observed in forested areas of the south; and (3) cold spots in the western Qilian Mountains have expanded toward the Hexi Corridor, reflecting significant spatial changes and indicating ecological degradation. Urbanization has markedly exacerbated regional imbalances in water resource carrying capacity, providing a scientific basis for water–ecological risk management in arid regions. Full article

The maintenance of power distribution lines is critically challenged by vegetation encroachment, posing significant risks to the reliability and safety of power utilities. Traditional manual inspection methods are resource-intensive and lack the precision required for effective and proactive maintenance. This paper presents an automated, accurate, and efficient approach to vegetation management near power lines by leveraging advancements in LiDAR as a remote sensing technology and deep learning algorithms. The RandLA-Net model is employed for semantic segmentation of large-scale point clouds to accurately identify vegetation, poles, and power lines. A comprehensive sensitivity analysis is conducted to optimize the model’s hyperparameters, enhancing segmentation accuracy. Post-processing techniques, including clustering and rule-based thresholding, are applied to refine the semantic segmentation results. Proximity detection is applied using spatial queries based on a KDTree structure to assess potential risks of vegetation near power lines. Furthermore, a digital twin of the power distribution network and surrounding trees is developed by integrating 3D object registration and surface generation, enriching it with semantic attributes and incorporating it into City Information Modeling (CIM) systems. This framework demonstrates the potential of remote sensing data integration for efficient environmental monitoring in urban infrastructure. The results of the case study on the Toronto-3D dataset demonstrate the computational efficiency and accuracy of the proposed method, presenting a promising solution for power utilities in proactive vegetation management and infrastructure planning. The optimized full 9-class model achieved an overall accuracy of 96.90% and IoU scores of 97.05% for vegetation, 88.09% for power lines, and 82.33% for poles, supporting comprehensive digital twin creation. An auxiliary 4-class model further improved targeted performance, with IoUs of 99.55% for vegetation, 88.79% for poles, and 87.18% for power lines. Full article

The Yellow River Delta is one of China’s most ecologically fragile regions, experiencing prolonged pressures from rapid urbanization and ecological degradation. Existing research, however, has predominantly focused on constructing ecological security patterns under single scenarios, with limited systematic multi-scenario comparisons and insufficient statistical support. To address this gap, this study proposes an integrated framework of “land use simulation—multi-scenario ecological security pattern construction—statistical comparative analysis.” Using the PLUS model, three scenarios were constructed—Business-as-Usual (BAU), Priority Urban Development (PUD), and Priority Ecological Protection (PEP)—to simulate land use changes by 2040. Habitat quality assessment, Multi-Scale Pattern Analysis (MSPA), landscape connectivity, and circuit theory were integrated to identify ecological source areas, corridors, and nodes, incorporating a novel hexagonal grid partitioning method. Statistical significance was evaluated using parametric tests (ANOVA, t-test) and non-parametric tests (permutation test, PERMANOVA). Analysis indicated significant differences in ecological security patterns across scenarios. Under the PEP scenario, ecological source areas reached 3580.42 km² (12.39% of the total Yellow River Delta), corresponding to a 14.85% increase relative to the BAU scenario and a 32.79% increase relative to the PUD scenario. These gains are primarily attributable to stringent wetland and forestland protection policies, which successfully limited the encroachment of construction land into ecological space. Habitat quality and connectivity markedly improved, resulting in the highest ecosystem stability. By contrast, the PUD scenario experienced an 851.46 km² expansion of construction land, resulting in the shrinkage of ecological source areas and intensified fragmentation, consequently increasing ecological security risks. The BAU scenario demonstrated moderate outcomes, with a moderately balanced spatial configuration. In conclusion, this study introduces an ecological restoration strategy of “five zones, one belt, one center, and multiple corridors” based on multi-scenario ecological security patterns. This provides a scientific foundation for ecological restoration and territorial spatial planning in the Yellow River Delta, while the proposed multi-scenario statistical comparison method provides a replicable methodological framework for ecological security pattern research in other delta regions. Full article

Urbanization poses growing threats to archaeological heritage sites embedded within cities, necessitating innovative monitoring and documentation strategies. This study investigates the use of Unmanned Aerial Vehicle (UAV) photogrammetry for mapping and 3D modelling of urban archaeological landscapes, focusing on the Byzantine-era Didymoteicho Fortress in northern Greece. High-resolution aerial imagery was captured and processed into an orthophoto mosaic and a detailed 3D model of the site’s monuments and their urban surroundings. The UAV-based survey provided comprehensive, up-to-date spatial data that traditional ground methods could not easily achieve in dense urban settings. The results illustrate how UAV mapping can document complex heritage structures, detect risks (such as structural deterioration or encroachment by development), and inform preservation efforts. The discussion situates these findings within global heritage management practices, highlighting UAV technology as a cost-effective, accurate, and non-invasive tool for safeguarding cultural heritage in urban areas. The suggested methodology enhances heritage documentation and risk assessment, demonstrating strong potential for policy integration and proactive conservation planning in historic cities. Full article

Mongolia’s rapidly expanding capital is encroaching on Bogd Khan Mountain, a UNESCO Biosphere Reserve and the oldest protected area in Eurasia. Gray wolves (Canis lupus) in this wildland–urban interface are locally near-threatened due to hunting, local beliefs, and human–wildlife conflict. In 2022 and 2023, we deployed 72 camera traps (11,539 trap nights) to investigate how wolves respond to overlapping pressures from free-ranging dogs, livestock, and human activity. Using a random habitat-stratified camera design and abundance modeling, we assessed diel activity and spatial co-occurrence. Wolves exhibited nocturnal and crepuscular activity, with the greatest temporal overlap with wild prey (wapiti: ∆⁴ = 0.73; Siberian roe deer: ∆⁴ = 0.79), moderate overlap with dogs (∆⁴ = 0.60) and horses (∆⁴ = 0.68), and minimal overlap with cattle (∆⁴ = 0.40) and people (∆⁴ = 0.43). Mean wolf abundance estimates ranged from λ = 0.91 (CI 95%, 0.05–1.77) in 2022 to λ = 1.52 (CI 95%, 0.44–3.53) in 2023. Wolves were more abundant at higher relative abundance of wild ungulates and in areas with more people. Wolves co-occurred with dogs at 11 sites and were more abundant in areas with a higher number of dogs. Our findings highlight the complex dynamics between wildlife, livestock, and human-associated disturbances at the wildland–urban interface, underscoring the need for integrated management strategies that address both ecological and human dimensions of conservation. Full article