Search Results (1,625)

Urban environments are saturated with imaging sensors deployed for purposes unrelated to vehicle identification, from ATM and dashboard cameras to pole-mounted CCTV and smartphones. We term the use of such non-purpose-built sensors for secondary inference “opportunistic sensing”; its central question is where, under uncontrolled capture conditions, AI-enabled restoration remains reliable. This paper introduces recoverability maps, a task-agnostic methodology for quantifying that boundary, and applies it to oblique-view license plate recognition (LPR). It pairs a full-grid synthetic sweep of the degradation space with two summary measures: a boundary area-under-curve for coverage and a reliability score $F$ for the frequency and depth of interior unrecovered pockets. For LPR, the space is the oblique-angle grid $[0°,89°{]}^2$ sampled by Scrambled Sobol sequences, and the utility is plate-level optical character recognition (OCR) accuracy. Within this synthetic benchmark, approximately $90$–$92\%$ of the angle grid is recoverable (best single model to union of restoration arms), recovery degrades sharply beyond roughly $80°$ in both axes, and lateral rotations are harder to reconstruct than elevational ones. Five restoration architectures cluster within a narrow AUC band of $0.89$–$0.93$, and share the same $\alpha /\beta$ asymmetry, so the recoverable region is set primarily by sensing geometry, with architecture affecting efficiency and interior consistency; discriminative architectures outperform generative models. The methodology is validated on real plates: on CCPD and the Brazilian legacy and Mercosur layouts of RodoSol-ALPR, restoration raises held-out extreme-angle recognition by $+15$ to $+38$ exact-match points under plate-specialized recognizers, and the discriminative-over-generative ordering reproduces on real data. Full article

Accurate extraction of transmission towers from Unmanned Aerial Vehicle (UAV) Light Detection and Ranging (LiDAR) point clouds is a prerequisite for overhead transmission line (OTL) acceptance. This task remains challenging because tower points are heavily entangled with ground, vegetation, conductors, and insulators, especially in complex terrain. To address this issue, we propose a shape prior-guided coarse-to-fine framework for tower extraction from UAV LiDAR point clouds. First, candidate tower regions are localized from the scene point cloud through preprocessing, near-ground suppression, and density-based clustering. Second, the least-disturbed central body of each candidate tower is identified in a slice-wise manner and used to estimate the tower orientation and four principal structural axes. Third, side-view and front-view structural envelopes are progressively inferred to suppress non-tower points around the tower body and tower head. Finally, a base-constrained filtering strategy is introduced to remove residual ground and low-vegetation points within the tower footprint. Experiments conducted on multiple OTL datasets acquired in different regions of China, including plains and mountainous areas, demonstrate that the proposed method achieves robust and efficient tower extraction across diverse scenarios. The results indicate that explicit structural priors offer a promising complement to feature-driven and data-intensive approaches, particularly in scenarios with limited annotated data and strict real-time requirements. The proposed method processes scene point clouds containing tens to hundreds of millions of points, with an average extraction time of approximately 100 to 300 s per tower depending on scene density. Full article

With the rapid growth of urban motorization, personalized travel modes, including taxis and private cars, have expanded considerably. However, conventional public transportation systems, constrained by fixed routes and limited service flexibility, often struggle to satisfy residents’ increasingly diversified and high-quality commuting needs. To address this issue, this study proposes an integrated planning framework for customized bus services using taxi trajectory data. First, passenger origin–destination (OD) information is extracted by detecting changes in the taxi passenger-status field. The extracted OD records are then used to identify potential commuting demand by jointly considering peak-hour travel characteristics and regional OD stability. Second, the identified potential commuting demand is used to generate candidate boarding and alighting stops through an improved DBSCAN-based clustering method, namely IDK-SG. For route planning among the candidate stops, a bi-objective optimization model is developed to simultaneously account for passenger travel-time costs and bus operating costs, and the model is solved using a genetic algorithm. Finally, timetable optimization is formulated as a Markov decision process and solved using a Deep Q-Network (DQN) algorithm. Case studies using taxi GPS trajectory data from Chongqing demonstrate that the proposed framework can effectively identify stable commuting demand, optimize stop layouts and route schemes, and improve vehicle occupancy and service quality. These findings provide practical decision-making support for the operation and dynamic scheduling of customized bus services in urban peak-hour commuting corridors. Full article

Urban freight externalities are increasingly addressed through regulation policies targeting both vehicle access and loading zones management. While urban vehicle access regulations and loading and unloading zone management are widely applied, existing research has largely regarded them as separate policy domains, overlooking their potential interdependence within urban freight governance. This study develops an exploratory comparative typology of UVARs and loading zone management across selected global cities. A hierarchical clustering method was applied to a harmonized set of indicators to identify distinct urban freight governance typologies. The UVAR clustering analysis was conducted on 39 cities with freight-related UVARs, while the loading zone clustering analysis was conducted on 39 cities with formal loading management zones. The cross-analysis suggests some co-occurrence patterns between UVARs and loading zone typologies. But the chi-square test does not provide statistical evidence of dependence. Therefore, this study can be interpreted as an exploratory mapping of regulatory configurations. The findings provide a comparative basis for future research linking urban freight regulatory typologies with environmental, operational, economic, and social performance indicators. Full article

Point-cloud analysis of sedimentary outcrops using Unmanned Aerial Vehicle (UAV) oblique photogrammetry is a crucial approach to sedimentary system characterization, stratigraphic correlation, and petroleum exploration analog studies. In large-scale field settings, however, outcrops are often scattered and fragmented, vegetation and soil cover is extensive, and class imbalance is pronounced. Manual interpretation is labor-intensive, while existing clustering algorithms, conventional machine learning methods, and general-purpose point-cloud segmentation networks struggle to simultaneously ensure geometric fidelity, rare-class recognition, and multi-scale feature integration. To address these challenges, we propose a method for extracting sedimentary outcrop point clouds from field surface point clouds using a UAV oblique photogrammetry acquisition strategy. The core segmentation module of the method, sedimentary cross-scale self-attention network (SedCSA-Net), is an enhanced version of PointNet++ that integrates collaborative improvements across four dimensions: data augmentation, sampling strategy, feature encoding, and loss optimization. Taking the Cretaceous Qingshuihe Formation in the Louzhuangzi area of the southern Junggar Basin as a case study, our experimental results indicate that SedCSA-Net overcomes the natural variability of UAV oblique photogrammetry point clouds—such as shadows, voids, and uneven density—achieving a mean Intersection over Union(mIoU) of 89.51% and an Overall Accuracy(OA) of 96.08%, with an outcrop-class Intersection over Union(IoU) of 86.90%. Attitude measurements derived from segmentation results deviate by less than 3° from manually annotated references, demonstrating that the proposed framework provides an end-to-end, generalizable approach for intelligent segmentation, geometric reconstruction, and attitude extraction of large-scale sedimentary outcrop point clouds. Full article

This study presents the preliminary development of a clustered hybrid propulsion module, and its experimental validation from static motor characterization to dynamic 1-D vertical drop tests to assess the feasibility of a hybrid propulsion system for soft-landing applications. The research progresses from preliminary design of core components (such as fuel, oxidizer supply system, engine configuration), to the performance verification of the clustering module. First, the trade-off between high regression rates and mechanical integrity was evaluated for paraffin-based fuels. However, high-density polyethylene (HDPE) was utilized as the baseline to ensure predictable combustion behavior. Second, cold flow tests of the designed multi-port manifold demonstrated a highly uniform oxidizer distribution, validating the geometric design with a maximum spatial pressure deviation of 2.44% across the four engines. Third, static fire tests confirmed robust dynamic control capabilities, successfully throttling the average chamber pressure from 100% (7.00 bar) down to 43% (3.01 bar) and back to 100% (7.01 bar) with a transient response time of approximately 0.6 s. Finally, the 1-D vertical drop test validated the operational readiness of the system; the open-loop thrust modulation successfully counteracted the module’s dynamic weight, achieving a terminal descent velocity of 1.46 m/s, which strictly satisfies planetary soft-landing safety criteria. These results demonstrate the feasibility and performance of clustered hybrid propulsion systems for planetary exploration, extending to surface launch technology for sample return missions from the Moon and Mars, and precision booster recovery for small launch vehicles. Full article

The design of a railway coach must meet strict certification requirements, especially in crashworthiness analysis under the European standard EN 15227. Performing this analysis with full-scale FEM models is highly demanding in terms of time, computational power and engineering resources, even with large server clusters. To improve efficiency, it is useful to simplify regions of the structure that are less influenced by external loads. In this approach, less critical parts are replaced with flexible one-dimensional elements, reducing the number of degrees of freedom while preserving the vehicle’s main dynamic behaviour. By concentrating on a specific mid-span section, the model becomes more robust and easier to manage. Calibrated elements are introduced to accurately reproduce the mass and stiffness of the removed structural components. The methodology also integrates mass and stiffness elements to capture structural response over a broader frequency range. An iterative non-gradient calibration procedure is then applied to adjust the equivalent stiffness and mass distribution so that the simplified model reproduces the response of the full-scale reference model. The results show that this strategy is effective, achieving a 77.6% reduction in simulation time while maintaining reliable accuracy. However, the process is still labour-intensive, and its performance may decline under large deformation conditions. Full article

Previous research has primarily focused on the restorative effects of environments on the general population, often overlooking the specific restorative capacity of urban settings for the disabled population. There is a lack of comprehensive investigation into the interaction between accessibility elements and urban restorativeness. This study, conducted in Shenzhen, Guangdong Province, China, categorizes streetscape accessibility elements for the disabled population and develops a recognition system based on an enhanced DeeplabV3+ framework. Semantic segmentation of streetscape accessibility elements was performed using 201,860 sampling points and 807,440 street view images. This study employed a combination of TrueSkill scoring, sentiment semantic analysis, LDA topic modeling, and LAB color clustering to quantify and visualize urban restorativeness. The impact of accessibility elements on urban restorativeness was explored using the XGBoost-SHAP model. Results indicate significant effects of architectural space constraints and high-density motor vehicle distribution on the safety of the disabled population’s mobility. The low pixel ratio of accessibility facilities and signs indicates insufficient infrastructure, while high landscape recognition rates exhibit significant spatial coverage heterogeneity. Detection rates for the disabled population in street views are nearly zero, highlighting a severe lack of inclusivity in pedestrian environments. Urban restorativeness exhibited a pattern of being higher in the south and east, and lower in the north and west. Among the accessibility elements, public green spaces (PGS) contributed the most to urban restorativeness, accounting for 25% of the impact, and the study elucidates the mechanisms through which various elements affect urban restorativeness. This absence stems from spatial competition, missing co-design, threshold effect conflicts, and color interference mechanisms. This research breaks away from traditional linear analytical frameworks and reveals the complex non-linear relationship between accessibility elements and urban restorativeness through the XGBoost-SHAP model, providing a quantitative decision-making tool for planning accessible environments in high-density cities. Full article

Unmanned Surface Vehicle (USV) clusters operating in maritime environments face dynamic communication conditions, including varying sea states, electromagnetic interference, and satellite denial, that render static communication topologies suboptimal. Existing approaches assess link quality through single indicators, typically the SNR, and lack mechanisms for automatic topology adaptation. This paper presents a multi-layer adaptive communication framework that achieves a mean communication quality score of 0.72 (vs. 0.51–0.66 for baselines), a message delivery rate of 94.1% under benign conditions, and a failure recovery time of 3.2 s (vs. 5.8–8.4 s for baselines) across five communication failure scenarios. The framework integrates three layers: a weighted multi-indicator communication quality metric fusing the SNR, packet loss rate, latency, and link stability into a unified score; a topology utility function that selects among centralized, distributed, and hierarchical topologies by optimizing a quality–threat–overhead objective; and a multi-modal backup communication manager with physics-based underwater acoustic, optical line-of-sight, and multi-hop relay fallback modes. Simulation results demonstrate consistent improvements over single-indicator and static-topology baselines, with particularly strong performance under satellite denial and jamming scenarios where multi-modal backup communication sustains delivery rates above 85% under simulated conditions. In summary, the framework demonstrates consistent improvements across all metrics (communication quality, delivery rate, recovery time) relative to four baselines, with the largest gains observed under the most challenging conditions (satellite denial and jamming). We emphasize that the framework adaptively selects among pre-defined canonical topologies (star, mesh, tree) based on real-time conditions rather than synthesizing optimal topologies de novo—a distinction between topology management and topology optimization. Full article

To effectively reduce fuel consumption and emissions in personal transport, it is essential to understand how energy transfer inefficiencies arise under real-world driving conditions. This study investigates the behavioral and spatial determinants of energy transfer inefficiency in personal diesel vehicles using high-resolution vehicle telematics data. The research proposes a composite Energy Inefficiency Index (EII) derived from real-world indicators of driving behavior, including acceleration, braking, idling, speed variability, and trip structure. These indicators are normalized and weighted using principal component analysis to quantify inefficiency at trip and spatial levels. Geospatial analysis, including Global Moran’s I and heatmap visualization, is employed to identify spatial clustering of energy inefficiency across urban and extra-urban environments. The results reveal a moderate average level of energy inefficiency across the analyzed vehicle fleet, with braking frequency, acceleration frequency, trip duration, and idling time emerging as the primary behavioral drivers of inefficient energy transfer. A statistically significant positive spatial autocorrelation indicates pronounced clustering of inefficiency in dense urban areas characterized by congestion and stop–start traffic dynamics. Furthermore, this study evaluates potential fuel, cost, and CO₂ emission reductions achievable through improved driving behavior and compares these gains with those associated with vehicle electrification. The findings demonstrate that targeted behavioral interventions—such as eco-driving and idling reduction—can yield substantial efficiency improvements and emission reductions, complementing the benefits of electrification. Overall, this research provides a data-driven framework for managing energy transfer inefficiency in personal diesel vehicles by integrating behavioral analysis, spatial assessment, and telematics-based monitoring, offering practical insights for policymakers, transport planners, and vehicle technology developers. Full article

Public charging infrastructure plays a critical role in supporting the sustainable development of electric vehicles (EVs), yet its effectiveness is often constrained by environmental conditions and spatial mismatches between supply and demand. This study develops a demand-oriented analytical framework to evaluate public charging services and support compensatory layout planning under cold-climate conditions, using Harbin, China, as a case study. The framework integrates demand hotspot identification, climate-adjusted service coverage reconstruction, service mismatch diagnosis, and compensatory layout recommendations. The results show that public charging demand in Harbin exhibits a clear centre-oriented clustering pattern. As cold-climate constraints intensify, the effective service coverage of charging facilities continuously contracts, and service mismatch areas become concentrated in high-demand clusters, forming an overall pattern of prominent central areas and scattered peripheral zones. Under the general winter scenario, a total of 197 service mismatch grids were identified, accounting for 58.98% of all hotspot grids. After the proposed compensatory layout, the number of mismatch grids decreased to 115, representing a reduction of 82 grids or 41.62%. These findings demonstrate that climate-sensitive service evaluation is essential for accurately identifying critical service deficiencies in cold-climate cities. The proposed framework provides a transferable approach for climate-sensitive service evaluation and compensatory layout planning of public charging infrastructure in high-latitude urban areas. Full article

Background: Urban road-traffic fatalities are a public health burden and a governance challenge because protection is uneven across urban space and time. Methods: We analyzed 702,540 administrative road-incident records from Medellín, Colombia (2008–2025), identified 2762 fatal cases, standardized incident categories, and harmonized time and coordinate fields. Spatial analyses were based on 2507 geocoded fatalities. We combined descriptive profiling, chi-square tests, logistic regression comparing pedestrian-strike and collision fatalities, sensitivity analyses using grouped time periods and a pandemic-period indicator, and spatial autocorrelation measures using Moran’s I and Getis–Ord $G_i^{*}$. Results: Incident type composition did not differ significantly between daytime and nighttime, but it varied across districts (comunas). Each later hour was associated with slightly higher odds that a fatality would be classified as a pedestrian strike rather than a collision (OR = 1.033), and fatalities in the urban core had nearly threefold higher odds of being classified as pedestrian strikes (OR = 2.953). Sensitivity analyses did not materially alter these associations. Spatial statistics showed strong clustering among the dominant fatality classes and identified 129 significant hotspot cells. Conclusions: Fatal road-traffic harm in Medellín is spatially concentrated and varies by incident mechanism, with pedestrian fatalities disproportionately concentrated in central areas of intense pedestrian–vehicle interaction. These findings show that transparent surveillance analytics can inform governance prioritization while also underscoring the need to improve data completeness, incorporate exposure measures, and interpret pandemic-period patterns with caution. Full article

The canopy closure stage is a critical phase of rice (Oryza sativa L.) development that influences canopy structure and final grain yield. Accurate and continuous monitoring of canopy closure dynamics is therefore essential for variety screening and cultivation optimization. This study combines unmanned aerial vehicle (UAV) remote sensing technology with deep learning-based semantic segmentation to establish an efficient framework for quantifying rice canopy closure dynamics. UAV RGB images were acquired for 198 hybrid rice varieties during early growth stages and used to build a canopy segmentation dataset. Three semantic segmentation models, i.e., DeepLabv3+, U-Net, and PSPNet, were systematically evaluated. Results show that DeepLabv3+ performed the best and enabled precise extraction of rice canopy features, obtaining a mean intersection over union (mIoU) of 0.86. Based on the extracted canopy coverage, the Gompertz model was utilized to characterize temporal canopy closure trajectories for all varieties, achieving an average R² of 0.978. Subsequently, five key dynamic indicators were derived, including canopy closure limit value (K), initial growth coefficient (a), growth rate coefficient (b), maximum instantaneous growth rate (MGR), and days to maximum growth rate (Tm). K-means clustering analysis was performed on these indicators to categorize all rice varieties into three clusters, disclosing pronounced differences in early-stage canopy development characteristics. Correlation analysis further demonstrated that canopy closure dynamics were closely associated with grain yield. Overall, while acknowledging the limitations of a single-season and single-site dataset, this study provides a scalable and objective framework for quantifying rice canopy closure dynamics, offering valuable support for variety selection, cultivation optimization, and high-yield rice production. Full article

Background: The Vehicle Routing Problem (VRP) is foundational to logistics optimization, yet its alignment with the Triple Bottom Line (TBL) and UN Sustainable Development Goals (SDGs) remains fragmented. This study conducts a strategic bibliometric audit of 301 peer–reviewed publications (1992–2025) to quantify the evolutionary progression and thematic maturity of sustainable routing research. Methods: A four–stage scientometric framework was employed, utilizing Scopus–based data retrieval, longitudinal mapping, and Python 3.14–driven text mining to visualize keyword co–occurrence networks, author collaborations, and regional research clusters. Results: Findings reveal a pronounced “Sustainability Asymmetry,” where 51.5% of studies prioritize economic efficiency, while only 2.6% address the social pillar. Additionally, social sustainability remains an “isolated island” with minimal cross–citation to the research core. Geographic analysis identifies a heavy concentration in China, the USA, and Western Europe, uncovering a critical North–South—collaboration gap. Conclusions: The study proves that while environmental themes reached maturity between 2018 and 2022, social indicators exhibit a significant maturity lag. This quantified social deficit, centered on the neglect of SDG 3 and SDG 10, mandates a fundamental paradigm shift toward a geographically inclusive and socially conscious research agenda to ensure global logistical equity. Full article

The growing penetration of renewable energy has intensified building load fluctuations, substantially increasing balancing costs. Electric vehicles (EVs) in building clusters often have considerable idle parking time beyond essential charging needs, enabling them to provide significant flexibility while meeting scheduled demands. This EV flexibility can balance intra-day load deviations and enable arbitrage in day-ahead electricity markets. However, conventional model-based approaches are fundamentally limited by their dependence on forecasting accuracy under high uncertainty from renewable generation and EV behavior. To address this, we propose a novel bi-level online optimization framework. The upper level employs a Lyapunov optimization-based algorithm that operates without predictions, making real-time decisions on total EV charging power to balance supply-demand mismatches. The lower level introduces novel flexibility metrics for individual EVs—encompassing temporal, volumetric, and cross-day dimensions—and optimizes power allocation by minimizing flexibility loss. Furthermore, we model EV flexibility as virtual queues and rigorously derive mathematical bounds on their limits, providing theoretical support for managing flexibility reserves. Rigorous analysis validates the framework’s feasibility, and comprehensive simulations demonstrate its superiority over benchmark algorithms, achieving significant cost reductions under various uncertainty scenarios. Full article