Search Results (2,850)

Road cracks represent a significant challenge that impacts the long-term performance and safety of transportation infrastructure. Early identification of these cracks is crucial for effective road maintenance management. However, traditional crack recognition methods that rely on visible light images often experience substantial performance degradation in weak-light environments, such as at night or within tunnels. This degradation is characterized by blurred or ‌deficient image textures, indistinct target edges, and reduced detection accuracy, which hinders the ability to achieve reliable all-weather target detection. To address these challenges, this study introduces a dual-modal crack detection method named YOLOv11-DCFNet. This method is based on an enhanced YOLOv11 architecture and incorporates a Cross-Modality Fusion Transformer (CFT) module. It establishes a dual-branch feature extraction structure that utilizes both infrared and visible light within the original YOLOv11 framework, effectively leveraging the high contrast capabilities of thermal infrared images to detect cracks under weak- or no-light conditions. The experimental results demonstrate that the proposed YOLOv11-DCFNet method significantly outperforms the single-modal model (YOLOv11-RGB) in both weak-light and no-light scenarios. Under weak-light conditions, the fusion model effectively utilizes the weak texture features of RGB images alongside the thermal radiation information from infrared (IR) images. This leads to an improvement in Precision from 83.8% to 95.3%, Recall from 81.5% to 90.5%, mAP@0.5 from 84.9% to 92.9%, and mAP@0.5:0.95 from 41.7% to 56.3%, thereby enhancing both detection accuracy and quality. In no-light conditions, the RGB single modality performs poorly due to the absence of visible light information, with an mAP@0.5 of only 67.5%. However, by incorporating IR thermal radiation features, the fusion model enhances Precision, Recall, and mAP@0.5 to 95.3%, 90.5%, and 92.9%, respectively, maintaining high detection accuracy and stability even in extreme no-light environments. The results of this study indicate that YOLOv11-DCFNet exhibits strong robustness and generalization ability across various low illumination conditions, providing effective technical support for night-time road maintenance and crack monitoring systems. Full article

Aboveground biomass (AGB) is a key indicator of forest structure and carbon sequestration, yet its dynamics under concurrent anthropogenic disturbances remain poorly understood. This study investigates the spatiotemporal dynamics and driving mechanisms of AGB in the Jianfengling tropical lowland rainforest (JFLTLR) within Hainan Tropical Rainforest National Park (NRHTR) from 2015 to 2023. Six machine learning models—Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF)—were evaluated, with RF achieving the highest accuracy (R² = 0.83). Therefore, RF was employed to generate high-resolution annual AGB maps based on Sentinel-1/2 data fusion, field surveys, socio-economic indicators, and topographic variables. Human pressure was quantified using the Human Influence Index (HII). Threshold analysis revealed a critical breakpoint at ΔHII ≈ 0.1712: below this level, AGB remained relatively stable, whereas beyond it, biomass declined sharply (≈−2.65 mg·ha⁻¹ per 0.01 ΔHII). Partial least squares structural equation modeling (PLS-SEM) identified plantation forests as the dominant negative driver, while GDP (−0.91) and road (−1.04) exerted strong indirect effects through HII, peaking in 2019 before weakening under ecological restoration policies. Spatially, biomass remained resilient within central core zones but declined in peripheral regions associated with road expansion. Temporally, AGB exhibited a trajectory of decline, partial recovery, and renewed loss, resulting in a net reduction of ≈ 0.0393 × 10⁶ mg. These findings underscore the urgent need for a “core stabilization–peripheral containment” strategy integrating disturbance early-warning systems, transportation planning that minimizes impacts on high-AGB corridors, and the strengthening of ecological corridors to maintain carbon-sink capacity and guide differentiated rainforest conservation. Full article

Accelerated urbanization has induced marked biodiversity loss in metropolitan regions, with urban parks emerging as critical habitat patches for avian species within intensively developed built environments. As a global pioneer in park city conceptualization, Chengdu (China) has achieved notable advancements in urban green space extent and quality through systematic planning efforts. This investigation examines the avian–habitat relationships in Chengdu’s central urban area (2010–2020) using multispectral remote sensing data, employing the ENVI5.6 (Environment for Visualizing Images) software for spatial analysis, and applying the InVEST3.2.0 (Integrated Valuation of Ecosystem Services and Tradeoffs) model to identify high-quality habitats, evaluate landscape connectivity, and analyze community composition dynamics. Through a correlation analysis of seven environmental characteristic factors with avian biodiversity in 24 urban parks, the impact mechanism of avian habitat functions was explored. On this basis, measures such as optimizing the plant community structure of riverside greenways and road green spaces, expanding small-scale green spaces near parks, and so on are proposed to promote the enhancement of urban park habitat functions and the protection of avian biodiversity. Full article

Accurate prediction of the International Roughness Index (IRI) is critical for road safety and maintenance decisions. In this study, we propose a novel Physics-Aware Informer (PA-Informer) model that integrates the efficiency of the Informer structure with physics constraints derived from partial differential equations (PDEs). The model addresses two key challenges: (1) performance degradation in short-sequence scenarios, and (2) the lack of physics constraints in conventional data-driven models. By embedding residual PDEs to link IRI with influencing factors such as temperature, precipitation, and joint displacement, and introducing a dynamic weighting strategy for balancing data-driven and physics-informed losses, the PA-Informer achieves robust and accurate predictions. Experimental results, based on data from four climatic regions in China, demonstrate its superior performance. The model achieves a Mean Squared Error (MSE) of 0.0165 and R² of 0.962 with an input window length of 30 weeks, and an MSE of 0.0152 and R² with an input window length of 120 weeks. Its accuracy is superior to that of other models, and the stability of the model when the input window length changes is far better than that of other models. Sensitivity analysis highlights joint displacement and internal stress as the most influential features, with stable sensitivity coefficients (S_p ≈ 0.89 and S_p ≈ 0.81). These findings validate the PA-Informer as a reliable and scalable tool for predicting pavement performance under diverse conditions, offering significant improvements over other IRI prediction models. Full article

This study explores how Brazilian logistics companies manage road safety by identifying key practices, motivators, and barriers. While traffic safety has been widely studied, few investigations adopt an organizational perspective, especially in the logistics sector. Addressing this gap, we applied a mixed-methods approach combining expert input (qualitative phase) and a structured survey of industry professionals (quantitative phases). The findings reveal that practices such as infrastructure development, driver training, and compliance monitoring are perceived as most effective. Motivators include operational planning and economic incentives, while major barriers involve lack of internal motivation, awareness, and resource constraints. Factor analysis confirmed the structure of practices, motivators, and barriers, while a SWOT framework provided strategic insights into internal strengths and external challenges. This study offers practical recommendations for integrating safety into strategic planning, improving training, and strengthening collaboration with public actors. By aligning safety efforts with long-term business goals, logistics providers can enhance both operational performance and social responsibility. These results contribute to global discussions on sustainable logistics by supporting key Sustainable Development Goals, including SDG 3.6 (road safety), SDG 8.8 (safe working environments), SDG 9.1 (sustainable infrastructure), and SDG 11.2 (safe and accessible transport). Full article

High-definition maps (HDMaps) are critical for safe autonomy on structured roads. Yet traditional production—relying on dedicated mapping fleets and manual quality control—is costly and slow, impeding large-scale, frequent updates. Recently, standard-definition maps (SDMaps) derived from remote sensing have been adopted as priors to support HDMap perception, lowering cost but struggling with subtle urban changes and localization drift. We propose Loop-MapNet, a self-evolving, multimodal, closed-loop mapping framework. Loop-MapNet effectively leverages surround-view images, LiDAR point clouds, and SDMaps; it fuses multi-scale vision via a weighted BiFPN, and couples PointPillars BEV and SDMap topology encoders for cross-modal sensing. A Transformer-based bidirectional adaptive cross-attention aligns SDMap with online perception, enabling robust fusion under heterogeneity. We further introduce a confidence-guided masked autoencoder (CG-MAE) that leverages confidence and probabilistic distillation to both capture implicit SDMap priors and enhance the detailed representation of low-confidence HDMap regions. With spatiotemporal consistency checks, Loop-MapNet incrementally updates SDMaps to form a perception–mapping–update loop, compensating remote-sensing latency and enabling online map optimization. On nuScenes, within 120 m, Loop-MapNet attains 61.05% mIoU, surpassing the best baseline by 0.77%. Under extreme localization errors, it maintains 60.46% mIoU, improving robustness by 2.77%; CG-MAE pre-training raises accuracy in low-confidence regions by 1.72%. These results demonstrate advantages in fusion and robustness, moving beyond one-way prior injection and enabling HDMap–SDMap co-evolution for closed-loop autonomy and rapid SDMap refresh from remote sensing. Full article

Drive-through establishments are becoming increasingly prominent in urban transport systems; however, their impacts on traffic generation, spatial form, and sustainability remain insufficiently understood. Conventional trip generation manuals often rely on static predictors, such as gross floor area, which can misrepresent demand in high-turnover, convenience-driven contexts and fail to capture operational, behavioral, and environmental effects. This knowledge gap underscores the need for an integrated framework that supports both effective planning and congestion mitigation, particularly in cities experiencing rapid motorization and shifting mobility behaviors. This study investigated the evolving dynamics in trip generation associated with drive-through services and their influence on urban development patterns. A mixed-methods approach was employed, combining a systematic literature review, meta-analysis of queue data, cross-comparison of trip generation rates from international and Asian datasets, and case-based scenario modeling. The results revealed that drive-throughs intensify high-frequency, impulse-driven vehicle trips, thereby causing congestion, reducing pedestrian accessibility, and reinforcing auto-centric land use configurations, while also enhancing consumer convenience and commercial efficiency. This study contributes to the literature by synthesizing inconsistencies in regional datasets; introducing a systems-based framework that integrates structural, behavioral, and environmental determinants with road network topology; and outlining policy applications that align trip generation with zoning, design standards, and sustainable infrastructure planning. Full article

In this work, a radar imagery-based signal processing technique to eliminate dynamic clutter interference in Structural Health Monitoring (SHM) is proposed. This can be considered an application of a joint communication and sensing telecommunication infrastructure, leveraging a base-station as ground-based radar. The dynamic clutter is considered to be a fast moving road user, such as car, truck, or moped. The proposed technique is suitable in case of a dynamic clutter, such that its Doppler contribute alias and falls over the 0 Hz component. In those cases, a standard low-pass filter is not a viable option. Indeed, an excessively shallow low-pass filter preserves the dynamic clutter contribution, while an excessively narrow low-pass filter deletes the displacement information and also preserves the dynamic clutter. The proposed approach leverages the Time Domain Backprojection (TDBP), a well-known technique to produce radar imagery, to transfer the dynamic clutter from the data domain to an image plane, where the dynamic clutter is maximally compressed. Consequently, the dynamic clutter can be more effectively suppressed than in the range-Doppler domain. The dynamic clutter cancellation is performed by coherent subtraction. Throughout this work, a numerical simulation is conducted. The simulation results show consistency with the ground truth. A further validation is performed using real-world data acquired in the C-band by Huawei Technologies. Corner reflectors are placed on an infrastructure, in particular a bridge, to perform the measurements. Here, two case studies are proposed: a bus and a truck. The validation shows consistency with the ground truth, providing a degree of improvement within respect to the corrupted displacement on the mean error and its variance. As a by-product of the algorithm, there is the capability to produce high-resolution imagery of moving targets. Full article

Accurately determining the modulus of each structural layer remains a key challenge in asphalt pavement design, construction quality control, and bearing capacity assessment. This study introduces an ensemble model combining a genetic algorithm-optimized backpropagation neural network (GA-BP) and a convolutional neural network (CNN) to back-calculate the dynamic modulus of asphalt pavement layers over rubblized old cement concrete structures. Using a dynamic deflection basin database created by our research team, we built a dataset of 1,552,000 pavement structure samples with Falling Weight Deflectometer (FWD) data. Based on this dataset, we developed regression models, including a backpropagation (BP) neural network, GA-BP, and CNN, to perform the back-calculation of dynamic modulus values. Performance testing revealed that the CNN model outperformed both the GA-BP and BP models in terms of accuracy and stability, as indicated by evaluation metrics (R², MAE, RMSE, MAPE), with the following ranking: CNN > GA-BP > BP. Nonetheless, the maximum relative error across all three models remained notable. To address this, an ensemble model combining GA-BP and CNN was created, significantly enhancing the accuracy and stability of the back-calculation results. The proposed ensemble model was tested on-site with FWD data to estimate the dynamic modulus of asphalt pavement layers. The results demonstrated strong agreement with actual pavement performance and high consistency with numerical outcomes from three-dimensional (3D) dynamic finite element method simulations. These findings suggest that the GA-BP and CNN ensemble approach offers a reliable method for back-calculating the dynamic modulus of asphalt pavement layers over rubblized old cement concrete structures. Full article

Analyzing the spatial layout equilibrium of highway transportation networks is essential for optimizing transportation networks, enhancing system efficiency and sustainability. To promote the equitable distribution and management of highway traffic resources, this study introduces a framework for assessing the spatial layout equilibrium of highway networks based on community structure. A new algorithm, named the C-Louvain algorithm, is introduced in this paper to address improving the stability of detection results in unconnected networks. The method first constructs a spatial node-based network, then detects the community structure of the highway network using the C-Louvain algorithm, and identifies key communities of the community structure network through a depth-first search. Network spatial layout imbalance is quantitatively assessed through supply–demand equilibrium analysis based on the Gini coefficient. This methodology is applied to the regional highway network in Shenyang, China. Results indicate that the C-Louvain method is optimal, excelling in accuracy, volatility, and efficiency compared to the classic FN, Leiden, and Louvain algorithms, providing a valuable contribution to the literature on graph clustering and data mining. There are significant differences in the number of communities within different connected components, which reflects the heterogeneity of the network’s structure. By this method, the imbalanced area in the highway transportation network layout is quickly found, and the equitable distribution of traffic resources is quantitatively evaluated. The research results can provide a theoretical basis for managers to make scientific investment decisions for road network construction. Full article

Under the discourses of sustainable development and modernization of the Amazon, an iron triangle of governments, companies, and investors often impose large-scale infrastructure projects (LSIPs) on Indigenous peoples to facilitate commodity extraction and market transactions in a context of capitalist market expansion. Indigenous resistance to LSIPs can be understood as a power struggle against coloniality and towards decolonizing environmental justice (DEJ). This study merges DEJ and power frameworks, while involving Indigenous leaders as co-researchers to provide a critical, insider perspective on the (i) motivations, (ii) strategies, and (iii) agency of two effective Indigenous resistance processes: the luchas led by Yunguillo Indigenous Reserve against roads, and by the Mancomunidad de Comunidades de los ríos Beni, Tuichi y Quiquibey against hydroelectric dams. In both cases, motivations reflected DEJ goals: the defense of Indigenous autonomy and territorial sovereignty, as well as Indigenous ontologies and epistemologies, reflecting an alternative vision of sustainability and development. However, locals’ positions regarding the projects were convoluted, partly due to the patronizing and divisive strategies of the iron triangle. To challenge the coloniality of power, both groups applied a diverse, synergistic, and adaptative set of strategies. External and internal alliances (i.e., with other actors and within communities), as well as actions to empower themselves as groups (e.g., self-governance) and individuals (e.g., spirituality) constituted key organizational leveraging strategies to increase their power-with and power-within. The instrumental strategies of collective action, civil disobedience, and direct resistance, in a climate of highly unjust and poorly trusted official institutions, showed great effectiveness to exert pressure on the iron triangle (power-over) and halt the projects (power-to, or agency). Success, nevertheless, was partial and uncertain: one battle won in an unequal war and in a changing context. This study seeks to contribute to previous efforts to decolonize and repoliticize academia, environmentalism, and sustainability, advance debates on strategies that challenge official systems and entrenched power structures, and validate Indigenous perspectives and experiences, producing scientific evidence that contributes to their luchas. Full article

This study introduces the Humanizing Sustainable Corridors Framework (HSCF), developed to guide the transformation of Car-Oriented corridors into Human-centered, sustainable spaces. Rooted in a human-centered approach, the framework emphasizes enhancing social interaction, addressing environmental needs, and supporting local economies through urban design. The framework was applied to the Al-Hada Ring Road in Taif, Saudi Arabia, as a case study. A mixed-methods approach was utilized, incorporating expert field observations, interviews with 15 stakeholders, and a web-based survey that yielded 455 valid responses. The findings revealed that 78% of respondents prioritized natural landscapes, 72% highlighted the importance of walkability, and 69% emphasized the need for shaded areas and culturally rooted design elements that enhance comfort and safety. These results demonstrate that planning strategies reflecting local climate conditions, user behavior, and cultural identity can increase corridor sustainability and resilience by over 65% in terms of perceived user satisfaction and safety. The HSCF offers a structured, adaptable model for planners and decision-makers seeking to align spatial design with community needs and national development goals. Full article

Introduced species of trees and shrubs used in landscaping of cities in the steppe zone are exposed to the combined negative impact of the ever-increasing load of various anthropogenic factors and unfavorable zonal natural and climatic conditions. In this regard, the assessment of the degree of plant resistance to unfavorable factors in the urban ecosystems of the steppe zone is a necessary condition for rationalizing the selection of the assortment and improving the condition of green spaces. This paper presents the results of the analysis of the vital state of 5509 representatives of 78 introduced species of trees and shrubs growing along the road and transport network in the territory with increased anthropogenic pressure. The age structure of plantings, as well as a number of biological and ecological characteristics of the species composition, are analyzed. The variation in the level of vitality in groups united by individual characteristics—taxonomic affiliation, geographical origin, morphobiological characteristics (habitus), growth rate and age of plants—is shown, and groups with the highest level of vitality are identified. As a result, a number of criteria are selected that can serve as indirect markers of plant adaptability to the ecological conditions of steppe zone cities when forming an assortment for landscaping. Using the examples of the features “plant height” and “plant age”, the species-specific reaction of plants is shown, expressed in the limitation of growth and development, as well as the reduction of life expectancy under conditions of increased anthropogenic and climatic loads. The data obtained can be used to adjust the species composition of urban trees and shrubs, optimize their ratio and spatial and functional placement, and thereby optimize the operational characteristics of green spaces and increase the duration of their use. Full article

In this study, the oxidative upgrading of heavy oil residues using polymer-containing waste for the sustainable production of bitumen was investigated. Oxidation was performed at temperatures of 250–270 °C for 3–4 h with the addition of 2–3 wt.% polyethylene-based waste, under an air flow of 7 L/min. The physical and mechanical characterization of the resulting bitumen demonstrated compliance with oxidized modified bitumen grades OMB 100/130 and OMB 70/100. FTIR spectroscopy revealed the formation of carbonyl and sulfoxide functional groups, indicating the effective oxidative transformation of the bitumen matrix and partial incorporation of polyethylene fragments. NMR spectroscopy confirmed increased aromaticity and carbonyl content, while also detecting polyethylene-derived signals, suggesting compatibility and integration of the polymer waste into the oxidized structure. The thermal and rheological results showed that the optimal conditions for producing high-quality oxidized bitumen involved the use of 2% polymer waste at 270 °C for 4 h, yielding enhanced physical properties and chemical stability. These findings support the feasibility of using polymer-containing waste for bitumen upgrading, offering both environmental and technical advantages. The method not only improves the quality of bitumen but also contributes to waste valorization and circular economy practices in the road construction industry. Full article

This study examines the impact of canopy structural characteristics on pedestrians’ visual perception and psychophysiological responses along four roads in the subtropical city of Guangzhou: Huadi Avenue, Jixiang Road, Yuejiang Middle Road, and Huan Dao Road. A Canopy Structural Index (CSI) was innovatively developed by integrating tree height, crown width, diffuse non-interceptance, and leaf area index, establishing a five-tier quantitative grading system. The study used multimodal data fusion techniques combined with heart rate variability (HRV) analysis and eye-tracking experiments to quantitatively decipher the patterns of autonomic nervous regulation and visual attention allocation under different levels of CSI. The results demonstrate that CSI levels are significantly correlated with psychological relaxation states: as CSI levels increase, time-domain HRV metrics (SDNN and RMSSD) rise by 15%–43%, while the frequency-domain metric (LF/HF) decreases by 31%, indicating enhanced parasympathetic activity and a transition from stress to relaxation. Concurrently, the allocation of visual attention toward canopies intensifies. The proportion of fixation duration increases to nearly 50%, and the duration of the first fixation extends by 0.3–0.8 s. The study proposes CSI ≤ 0.15 as an optimization threshold, offering scientific guidance for designing and pruning subtropical urban street tree canopies. Full article