Search Results (4,254)

The automotive industry in Mexico faces significant logistical challenges in optimizing distribution routes, particularly in border regions, where traffic variability directly affects operational performance. This study proposes a multiperiod route optimization approach for a Tier 1 automotive supplier by applying the Floyd–Warshall algorithm to a cross-border transportation network. Distance matrices are constructed for multiple time windows to capture traffic-related variations in route efficiency. The algorithm is applied independently to each scenario, enabling the identification of time-dependent optimal routes and the development of alternative routing strategies. The results show that optimal routes vary across different periods of the day, leading to measurable improvements in routing efficiency and enhanced decision-making flexibility. The proposed approach supports more realistic logistics planning in congested urban environments and improves operational performance in cross-border automotive supply chains. Full article

With the increasing scale and complexity of network traffic, anomaly detection faces significant challenges, particularly under the scarcity of labeled data in real-world environments. Although graph neural networks (GNNs) effectively model relational structures, most existing approaches rely on supervised learning, limiting their applicability in weakly labeled or unlabeled scenarios. To address these limitations, this paper proposes a self-supervised graph neural network framework, termed EGSCA, for network traffic anomaly detection. The framework employs a GNN to jointly model node and edge information, enabling the learning of discriminative representations. On this basis, a graph contrastive learning strategy is designed, where diverse subgraphs are generated via breadth-first search (BFS) to effectively capture local structural patterns. Meanwhile, a hybrid contrastive loss based on Wasserstein distance and Gromov–Wasserstein distance is introduced to achieve collaborative optimization between feature-space alignment and structural consistency under unlabeled conditions. Experimental results on multiple benchmark datasets demonstrate that the proposed method achieves competitive performance. Notably, it achieves the best results on datasets NF-BoT-IoT and NF-BoT-IoT-v2, with average improvements of approximately 3.2% in F1-score and 1.7% in DR over the strongest baseline. Further analysis indicates that the model yields more pronounced performance gains in scenarios with high class separability. Full article

Forests in Poland play a key recreational role, and the growing interest in sylvaturism requires optimized management. Despite the growing body of research on forest recreation, existing studies rarely address the role of small-scale infrastructure in shaping user preferences and its integration into spatial planning frameworks, which constitutes a research gap in this study. This study aimed to identify user preferences for small infrastructure and to develop an application-oriented, socially sensitive model for forest trail design that supports sustainable management. The research was conducted in 2021–2024 using the CAWI method on a group of 402 adult Poles. Data analysis included descriptive statistics, Pearson’s chi-square tests to assess demographic differences, and correspondence analysis to identify user preference profiles. The results not only confirmed a clear hierarchy of needs but also demonstrated that differences between user groups relate primarily to the intensity rather than the structure of preferences. A clear hierarchy of needs was confirmed, with route map boards (86.32%), educational boards (72.64%), and benches (71.14%) dominating. Based on the results, a modular design model was developed (modules: basic, comfort, accessibility, and activity), which constitutes a conceptual advancement over existing planning approaches by introducing a flexible, user-oriented framework that links social preferences with spatial decision-making. By integrating empirical social data into the planning process, the proposed framework extends current knowledge on recreation planning and provides a structured basis for adaptive forest trail design. This tool could help managers efficiently channel tourist traffic, protect ecosystems, and promote public health. Full article

To optimize the freight distribution structure of ports and reduce carbon emissions from freight transportation, this paper develops a bi-level programming model for freight traffic shifting between roadway and waterway networks that incorporates carbon emissions. First, a complex freight network based on the roadway–water transport system is constructed, comprising roadway networks, inland waterway networks, maritime networks, and transshipment nodes. A traffic impedance model is then formulated within this complex network framework, integrating the roadway BPR function, the M/M/1 queuing model for lock passage time on inland waterways, and the M/M/c queuing model for port cargo handling into the impedance function. This allows micro-level congestion effects to be combined with macro-level traffic assignment. Next, a bi-level programming model for freight traffic shifting in the roadway–water network system is established, with carbon emissions incorporated. The NSGA-II algorithm is employed to determine the optimal carbon subsidy level, based on which the traffic distribution in the complex freight network is analyzed. Finally, the proposed model is applied to the roadway–waterway bimodal network in the Hangzhou Bay port area of Cixi. The results indicate that without subsidies, the waterway transport share is only 1.74%. The optimal subsidy efficiency frontier is identified at CNY 350,000/day, where the waterway share increases to 22.7% and carbon emissions decrease by 33.27 tons/day. The subsidy strategy evolves through three stages: first, prioritizing maritime shipping; second, jointly promoting inland and maritime shipping; and finally, shifting focus to infrastructure investment once subsidies reach saturation. This study offers a quantitative analytical tool for designing differentiated carbon subsidy policies to facilitate the road-to-waterway modal shift under fiscal constraints. Full article

The rapid evolution of intelligent cluster systems—such as UAV swarms and networked autonomous agents—has brought spatial optimization and decision-making to the forefront of intelligent systems research. This paper provides a systematic and critical review of recent advances in spatial optimization for multi-agent intelligent clusters, focusing on four core domains: UAV swarm path planning, resource allocation, traffic network analysis, and visualization technologies. A bibliometric analysis based on the Web of Science Core Collection (2000–2024) identifies two major methodological transitions. In path planning, research has moved from traditional algorithms (A*, Dijkstra, dynamic programming), effective in static settings but limited in dynamic and large-scale applications, to bio-inspired optimization and deep reinforcement learning methods that improve adaptability and efficiency. In resource allocation, studies have shifted from centralized single-algorithm models to distributed, self-organizing hybrid frameworks that enhance robustness and real-time responsiveness. Moreover, intelligent cluster technologies are increasingly applied to urban traffic management and visualization, where analysis has advanced from static 2D mapping to interactive 3D and immersive VR/AR environments. A comparative framework is proposed to evaluate existing algorithms by adaptability, computational complexity, and scalability. The review concludes that future research should emphasize hybrid algorithm integration, cross-disciplinary data-driven modeling, and immersive visualization to support real-time decision-making. This study consolidates the evolutionary trajectory of intelligent cluster optimization, identifies critical research gaps, and outlines a roadmap for the next generation of intelligent spatial optimization systems. Full article

The smart environment hides numerous security challenges that need to be addressed promptly. Smart cities have emerged as a novel concept, integrating emerging technologies and data-driven solutions to improve urban living conditions. Traffic surveillance cameras at intersections enable continuous traffic monitoring and rapid incident detection, optimizing signal timing to improve road safety and reduce traffic congestion and travel delay. These cities present new challenges for the police force, forcing them to blend into the environment. The paper proposes novel hierarchical Police Internet of Things (PIoT) concepts that should enable and secure timely, high-priority policing forecasting and decision-making processes in smart cities. Hierarchical edge, fog, and cloud computing were presented according to the police decision-making process. This concept is carefully developed to improve the timeliness of predictive policing, planning, management, and decision-making using artificial intelligence and fuzzy logic. The proposed vertical PIoT concept is supported by vertical data processing. In hierarchical computing, machine learning models for time series prediction and fuzzy-logic-based decision-making are applied to enable comprehensive analysis in a smart environment. Two case studies dealing with crime and traffic issues are presented in detail. Full article

Increased flight volumes necessitate urgent reforms in Airspace Management (ASM) to mitigate risks of fatalities and near-misses. In order to enhance aviation system safety, the International Civil Aviation Organization (ICAO) mandates that state parties must conduct the Universal Safety Oversight Audit Program (USOAP) to continuously monitor civil aviation. This research aims to identify critical factors influencing Thailand’s ASM by employing experimental design and Structural Equation Modeling (SEM) to analyze influences and relationships among communication, surveillance, navigation, Air Traffic Management (ATM), and ASM. The methodology includes stimulation and a questionnaire-based survey conducted with aviation professionals and mapping out their answers to find the influences, relationships, and importance of the different factors. The results were validated using various statistical tools. The findings indicate signi1ficant direct and indirect effects on ASM, emphasizing that effective communication and robust surveillance are essential for safety and operational efficiency. This study highlights the need to increase the ASM framework, providing actionable insights for optimizing air traffic control in response to the growing air traffic demand. Furthermore, SEM for Airspace optimization can be applied internationally to significantly reduce accidents and incidents in the future. Full article

With the rapid increase in electric vehicle (EV) ownership, the strategic planning and layout of charging infrastructure have become essential to encourage EV adoption. This study introduces a comprehensive multi-objective optimization method for selecting locations and designing layouts for high-power and extreme fast charging stations. By thoroughly accounting for user charging demands, economic expenses, and traffic conditions, a multi-objective optimization mathematical model is created aiming to minimize user time and costs while maximizing service capacity and user satisfaction. The model combines queuing theory, network topology analysis, and genetic algorithms to simultaneously handle discrete variables related to station placement, continuous variables for charging pile setup, and complex constraints. Using Panyu District in Guangzhou as a case study, a simulation model with 20,000 electric vehicles and 20 high-power and extreme fast charging stations is developed, focusing on the optimal arrangement of 120 kW, 240 kW, and 480 kW charging piles. The simulation results demonstrate that the optimized charging station layout scheme (13 units of 120 kW, 6 units of 240 kW, and 1 unit of 480 kW) lowers overall costs by 6.74%, reduces user charging waiting time from 1.54 h to 0.65 h, improves user satisfaction by 8.1%, and cuts the peak-to-valley difference in charging load from 900 kW to 450 kW. This work offers both theoretical insights and practical recommendations for the effective planning of electric vehicle charging infrastructure. Full article

Human performance in critical environments is frequently degraded by mistimed communication delivered during periods of visual–cognitive saturation. In such settings, failures arise not only from individual limitations but also from poor coordination between operators under rapidly changing workload conditions. We present a dual-loop neuro-adaptive simulation framework based on real-time spectral–topographic EEG representations, in which multichannel cortical activity is transformed into dynamic spatial maps and decoded to regulate both operator assistance and team communication. The system integrates 14-channel wireless EEG (Emotiv EPOC X, 256 Hz), gaze tracking, telemetry, and communication events through an LSL-based multimodal synchronization pipeline. A hybrid CNN–LSTM model processes sequences of spectral-topographic EEG maps to classify three operationally actionable neurocognitive states—Channelized Attention, Diverted Attention, and Surprise/Startle—while also estimating a continuous Cognitive Load Index (CLI). These representation-derived features are then used by a multi-agent proximal policy optimization (MAPPO) controller to generate two coordinated outputs: (i) adaptive haptic guidance for the pilot, designed to reduce reliance on overloaded visual and auditory channels, and (ii) a traffic-light communication gate for the telemetry engineer, regulating whether radio intervention should proceed, be delayed, or be withheld. In a high-fidelity dual-station simulation with 25 pilot–engineer pairs, the proposed framework was associated with a reduction of more than 30% in communication breakdown errors relative to open-loop telemetry, with the strongest effects observed during peak-load windows, while preserving realistic task progression. It also improved pilot reaction time to time-critical warnings and reduced engineer decision load under the tested conditions. These findings support the use of spectral-topographic EEG representations as a practical basis for combining multimodal neurophysiological sensing, spatiotemporal pattern decoding, and adaptive coordination in high-pressure human–machine teams. At the same time, the study should be interpreted as evidence of controlled feasibility in a simulated setting rather than as definitive proof of field-level generalization. We further discuss deployment constraints and propose privacy-by-design safeguards to ensure that neurocognitive signals are used exclusively for operational adaptation rather than employability assessment or performance scoring. Full article

Under the constraints of limited maintenance budgets, research on pavement performance prediction is of considerable practical significance for improving the efficiency of maintenance decision-making, optimizing fund allocation, and ensuring highway serviceability. This study was conducted in conjunction with pavement maintenance projects on ordinary national and provincial highways in Shanxi Province, China. Representative routes were selected based on the natural zoning and road network scale of Linfen City. A comprehensive factor system influencing pavement service performance was established, encompassing pavement characteristics, traffic attributes, climatic and environmental conditions, and maintenance management levels. By employing Particle Swarm Optimization (PSO) to tune the hyperparameters of a Long Short-Term Memory (LSTM) network, a PSO-LSTM prediction model incorporating a sliding window mechanism was constructed for the Pavement Condition Index (PCI) and Ride Quality Index (RQI). The model achieves coefficients of determination (R²) of 0.845 and 0.869 for PCI and RQI, respectively, enabling dynamic prediction of pavement service performance and thereby providing scientific support and a data-driven basis for the formulation of pavement maintenance strategies. Full article

This paper develops a unified α-cut optimization framework for modular electric vehicle (EV) fast-charging station design under fuzzy uncertainty. Uncertain peak demand, annual delivered energy, electricity price, ambient temperature, arrival rate, and energy per session are represented by triangular or trapezoidal fuzzy numbers and reformulated through α-cut bounds. The resulting design problem is expressed as a hybrid discrete–continuous model in which the number of modules, the selected catalog module rating, installed power, cooling provision, and a station-volume proxy are jointly optimized. An aggregated representation of interchangeable modules is adopted to remove permutation-equivalent descriptions and preserve a compact search space. Three planning views are examined: minimum CAPEX at a prescribed α-cut level, minimum loss-driven OPEX under a CAPEX budget, and a service-oriented admissibility/coverage analysis that avoids interpreting larger α values as greater robustness. The strengthened numerical study includes a deterministic nominal benchmark, peak demand sensitivity regimes, feasibility threshold and budget sweep results, explicit service stress scenarios, and a queueing sensitivity check against Erlang-C and discrete-event simulation indicators. The results show that baseline CAPEX designs may be dominated by catalog thresholds, whereas OPEX and service-oriented conclusions become informative once budget and traffic regimes are varied. The proposed framework is therefore positioned as a tractable α-cut-based design screening and comparative optimization tool for representative modular EV charging station scenarios, rather than as a universally validated operational design rule. Full article

With the rapid expansion of the tourism industry in Xinjiang, which received a record 328 million tourists in 2025, identifying development bottlenecks is crucial for regional sustainability. This study aims to identify the core obstacles hindering sustainable tourism in Southern Xinjiang—the region’s fastest-growing sector—and proposes evidence-based optimization pathways. Utilizing a deep learning approach, we deployed a Gated Recurrent Unit (GRU) sentiment analysis model to parse 5800 online reviews from 38 representative A-level scenic spots. The analysis identified 28 distinct obstacle clusters across three categories: landscape, cultural, and comprehensive destinations. The results reveal significant site-specific differentiation: natural landscape sites like Bayanbulak are primarily constrained by environmental risks and safety hazards, while high-traffic cultural sites like the Ancient City of Kashgar face acute challenges from over-commercialization and cultural erosion. Based on these findings, this study introduces a macro-level diagnostic tool and proposes targeted optimization strategies within the ESG (Environmental, Social, and Governance) framework. These insights offer actionable references for policymakers to enhance tourism resilience and achieve high-quality sustainable development in sensitive frontier regions. Full article

Aiming at the power fluctuation of the distribution network caused by the fluctuation of renewable energy output in the sending-end power grid. In this paper, a coordinated optimization method of mobile energy storage for distribution networks with a high proportion of renewable energy sending end systems is proposed to improve the energy support of the sending end distribution network and improve the resilience of the distribution network. Firstly, based on the mobile characteristics of mobile energy storage, a space-time transfer and charge-discharge model of mobile energy storage based on the traffic network is established. Secondly, by analyzing the network structure of the distribution network of the sending end system. The mobile energy storage scheduling mode is adopted, and the mobile energy storage support model of the sending end system is established with the minimum cost of load shedding and the minimum cost of mobile energy storage scheduling. Then, the power balance of the sending end system and the power balance of the distribution network are respectively targeted. The power balance equation of the sending end system based on the balance constraint of charging and discharging of mobile energy storage is established. Finally, a distribution network in the sending-end system is taken as an example for simulation. It is verified that the strategy proposed in this paper can effectively improve the stability of the sending power of the sending end system and maintain the balance of the distribution network. Full article

Asphalt pavement maintenance is critical for road service life and traffic safety, yet conventional crack detection and Pavement Condition Index (PCI) assessment methods suffer from inefficiency and subjectivity. This paper presents an integrated system for intelligent crack recognition and automated PCI evaluation, aiming to bridge the gap between automated identification and intelligent assessment. The system employs an optimized YOLOv11l-seg model for precise crack segmentation and geometric parameter extraction, and introduces a refined PCI model incorporating geometry-based adjustment factors for differentiated scoring. Using unmanned aerial vehicle (UAV) data, a fully automated workflow is established—from image acquisition and stitching to crack detection, PCI calculation, and result visualization. Experimental results demonstrate the accuracy of extracted crack parameters and the superior discriminative capability and engineering rationality of the proposed PCI model over conventional approaches. The generated panoramic condition maps provide intuitive visual support for maintenance decision-making. This research validates the feasibility of a fully auto-mated closed-loop system from detection to evaluation, offering a practical solution for intelligent pavement maintenance. Full article

The rapid development of tropical island tourism has put forward a higher demand for asphalt pavement construction on the island. However, the asphalt pavement engineering in the offshore area is generally faced with high material transportation costs. Additionally, challenges such as high-temperature climate and heavy-load traffic may lead to permanent pavement deformation. As a typical marine solid waste, shells have high calcium carbonate content and porous structures, which have the potential advantage of modified asphalt. In this study, mixed shell powder was used as a modified material, and 70 # base asphalt and SBS-modified asphalt were mixed, respectively. The effect of asphalt modification was analyzed by basic performance tests and high-temperature rheological tests. An asphalt mixture was prepared by replacing limestone powder with mixed shell powder in equal volume, and its road performance was systematically tested. The modification mechanism was revealed by means of a microscopic test. The results show that the recommended content of mixed shell powder in SBS-modified asphalt is 9%, and 50–100% mixed shell powder can be used to replace mineral filler in base asphalt and single SBS modified asphalt mixture. This study provides effective technical support for the utilization of shell solid waste in offshore areas and the optimization of asphalt pavement performance. Full article