Search Results (276)

Critical transportation networks in developing countries often lack structural robustness and functional redundancy due to insufficient planning and preparedness. These deficiencies increase vulnerability to disruptions and impede effective post-disaster response and recovery. Understanding how such networks perform under stress is essential to improving resilience in hazard-prone urban environments. This paper presents an integrated predictive methodology for assessing the operational resilience of urban transportation networks under extreme events, specifically tailored to data-scarce and high-risk contexts. By combining Geographic Information Systems (GISs) with complex network theory, the framework captures both spatial and topological dependencies. The methodology is applied to Beirut, the capital of Lebanon, a densely populated and disaster-prone Mediterranean city, through scenario-based simulations that account for interdependent stressors such as traffic dynamics, structural fragility, and geophysical hazards. Results reveal that the network exhibits low redundancy and high sensitivity to even minor disruptions, leading to rapid performance degradation. These findings indicate that the network should be classified as highly vulnerable. The study offers a robust framework for assessing infrastructure resilience and supporting evidence-based decision-making in critical urban network management. Full article

The technology of road extraction serves as a crucial foundation for urban intelligent renewal and green sustainable development. Its outcomes can optimize transportation network planning, reduce resource waste, and enhance urban resilience. Deep learning-based approaches have demonstrated outstanding performance in road extraction, particularly excelling in complex scenarios. However, extracting roads from remote sensing data remains challenging due to several factors that limit accuracy: (1) Roads often share similar visual features with the background, such as rooftops and parking lots, leading to ambiguous inter-class distinctions; (2) Roads in complex environments, such as those occluded by shadows or trees, are difficult to detect. To address these issues, this paper proposes an improved model based on Graph Convolutional Networks (GCNs), named FR-SGCN (Hierarchical Depth-wise Separable Graph Convolutional Network Incorporating Graph Reasoning and Attention Mechanisms). The model is designed to enhance the precision and robustness of road extraction through intelligent techniques, thereby supporting precise planning of green infrastructure. First, high-dimensional features are extracted using ResNeXt, whose grouped convolution structure balances parameter efficiency and feature representation capability, significantly enhancing the expressiveness of the data. These high-dimensional features are then segmented, and enhanced channel and spatial features are obtained via attention mechanisms, effectively mitigating background interference and intra-class ambiguity. Subsequently, a hybrid adjacency matrix construction method is proposed, based on gradient operators and graph reasoning. This method integrates similarity and gradient information and employs graph convolution to capture the global contextual relationships among features. To validate the effectiveness of FR-SGCN, we conducted comparative experiments using 12 different methods on both a self-built dataset and a public dataset. The proposed model achieved the highest F1 score on both datasets. Visualization results from the experiments demonstrate that the model effectively extracts occluded roads and reduces the risk of redundant construction caused by data errors during urban renewal. This provides reliable technical support for smart cities and sustainable development. Full article

Carbon transfer embodies the spatial redistribution of carbon emissions resulting from interregional economic activities and trade. In recent years, accelerated regional integration and deepening specialization within industrial chains have rendered traditional bilateral analytical frameworks inadequate for capturing the complexity of interregional carbon transfer networks. This evolving context necessitates the incorporation of spatial interaction effects to elucidate the multi-nodal and multi-pathway characteristics inherent in contemporary carbon transfer patterns. Based on the spatial interaction theoretical framework and a multiregional input–output (MRIO) model, we analyze the spatial dependence characteristics of interregional carbon transfer in China. The results reveal that interregional carbon transfer in China exhibited an upward trend from 2012 to 2017, demonstrating statistically significant positive origin dependence, destination dependence, and network dependence. The distance between regions exerts a significantly negative influence on interregional carbon transfer. Interregional carbon transfer is not merely a bilateral phenomenon; its fundamental nature is characterized as a network phenomenon. Our study demonstrates that precise regulation of the allocation of industrial land and transportation infrastructure land, strengthening the decisive role of market mechanisms in resource allocation for regional low-carbon development, and establishing interregional collaboration mechanisms for low-carbon exchange can effectively reduce the occurrence of interregional carbon transfer. These findings provide policymakers with more precise information to achieve equitable carbon emissions distribution across regions. Full article

Auxiliary transport vehicles are essential components of the underground mine auxiliary transportation system, primarily used for tasks such as personnel and material transportation. However, the underground environment is complex, and unstructured roads exhibit significant randomness. Traditional passive hydropneumatic suspension systems struggle to strike a balance between ride comfort and stability, resulting in insufficient adaptability of auxiliary transport vehicles in such challenging underground conditions. To address this issue, this paper proposes a multi-mode hydropneumatic suspension control strategy based on the identification of road surface grades in underground mines. The strategy dynamically adjusts the controller’s parameters in real time according to the identified road surface grades, thereby enhancing vehicle adaptability in complex environments. First, the overall framework of the active suspension control system is constructed, and models of the hydropneumatic spring, vehicle dynamics, and road surface are developed. Then, a road surface grade identification method based on Long Short-Term Memory networks is proposed. Finally, a fuzzy-logic-based sliding mode controller is designed to dynamically map the road surface grade information to the controller’s parameters. Three control objectives are set for different road grades, and the multi-objective optimization of the sliding mode’s surface coefficients and fuzzy-logic-based rule parameters is performed using the Hiking Optimization Algorithm. This approach enables the adaptive adjustment of the suspension system under various road conditions. The simulations indicate that when contrasted with conventional inactive hydropneumatic suspensions, the proposed method reduces the sprung mass’s acceleration by 21.2%, 18.86%, and 17.44% on B-, D-, and F-grade roads, respectively, at a speed of 10 km/h. This significant reduction in the vibrational response validates the potential application of the proposed method in underground mine environments. Full article

Accurate vehicle type recognition in low-light environments remains a critical challenge for intelligent transportation systems (ITSs). To address the performance degradation caused by insufficient lighting, complex backgrounds, and light interference, this paper proposes a Twin-Stream Feature Fusion Graph Neural Network (TFF-Net) model. The model employs multi-scale convolutional operations combined with an Efficient Channel Attention (ECA) module to extract discriminative local features, while independent convolutional layers capture hierarchical global representations. These features are mapped as nodes to construct fully connected graph structures. Hybrid graph neural networks (GNNs) process the graph structures and model spatial dependencies and semantic associations. TFF-Net enhances the representation of features by fusing local details and global context information from the output of GNNs. To further improve its robustness, we propose an Adaptive Weighted Fusion-Bagging (AWF-Bagging) algorithm, which dynamically assigns weights to base classifiers based on their F1 scores. TFF-Net also includes dynamic feature weighting and label smoothing techniques for solving the category imbalance problem. Finally, the proposed TFF-Net is integrated into YOLOv11n (a lightweight real-time object detector) with an improved adaptive loss function. For experimental validation in low-light scenarios, we constructed the low-light vehicle dataset VDD-Light based on the public dataset UA-DETRAC. Experimental results demonstrate that our model achieves 2.6% and 2.2% improvements in mAP50 and mAP50-95 metrics over the baseline model. Compared to mainstream models and methods, the proposed model shows excellent performance and practical deployment potential. Full article

Elevator fault diagnosis is critical for ensuring operational safety and reliability in modern vertical transportation systems. Traditional approaches, which rely on time- and frequency-domain signal analysis, often struggle with the issues such as noise sensitivity, inadequate feature extraction, and limited adaptability to complex scenarios. To address these challenges, this paper proposes a Graph Attention Recurrent Network (GARN) which integrates graph-structured signal representation with spatiotemporal feature learning. The GARN employs a limited penetrable visibility graph to transform raw vibration signals into noise-robust graph topologies, preserving critical patterns while suppressing high-frequency noise through controlled edge penetration. An adaptive attention mechanism dynamically fuses triaxial features to prioritize the most relevant information for fault diagnosis. The GARN combines a graph convolutional network to extract spatial correlations and a gated recurrent unit to capture temporal fault progression, enabling holistic and accurate fault classification. Experimental results based on real-world elevator datasets demonstrate the superior performance of the GARN, showcasing its strong noise resistance, adaptability to complex fault conditions, and ability to provide reliable and timely fault diagnosis, making it a robust solution for modern elevator systems. Full article

Glu-Phe-Asp (GFD) proteinoids represent a class of synthetic polypeptides capable of self-assembling into microspheres, fibres, or combinations thereof, with morphology dramatically influencing their electrical properties. Extended recordings and detailed waveforms demonstrate that microspheres generate rapid, nerve-like spikes, while fibres exhibit consistent and gradual variations in voltage. Mixed networks integrate multiple components to achieve a balanced output. Electrochemical measurements show clear differences. Microspheres have a low capacitance of $1.926\pm 5.735$$\mathsf{\mu }$F. They show high impedance at $6646.282\pm 178.664$ Ohm. Their resistance is low, measuring 15,830.739 ± 652.514 m$\mathsf{\Omega }$. This structure allows for quick ionic transport, leading to spiking behaviour. Fibres show high capacitance ($9.912\pm 0.171$$\mathsf{\mu }$F) and low impedance ($209.400\pm 0.286$ Ohm). They also have high resistance (163,067.613 ± 9253.064 m$\mathsf{\Omega }$). This combination helps with charge storage and slow potential changes. The 50:50 mixture shows middle values for all parameters. This confirms that hybrid electrical properties have emerged. The differences come from basic structural changes. Microspheres trap ions in small, round spaces. This allows for quick release. In contrast, fibers spread ions along their length. This leads to slower wave propagation. In mixed systems, diverse voltage zones emerge, suggesting cooperative dynamics between morphologies. This electrical polymorphism in simple proteinoid systems may explain complexity in biological systems. This study shows that structural polymorphism in GFD proteinoids affects their electrical properties. This finding is significant for biomimetic computing and sheds light on prebiotic information-processing systems. Full article

With the rapid development of intelligent transportation systems, obtaining vehicle status information across large-scale road networks is essential for the coordinated management and control of traffic conditions. Distributed Acoustic Sensing (DAS) demonstrates considerable potential in vehicle status perception due to its characteristics such as high spatial resolution and robustness in complex sensing environments. This study first reviews the limitations of conventional vehicle detection technologies and introduces the operating principles and technical features of DAS. Secondly, it investigates the correlations between DAS sensing characteristics, deployment process, and driving behavior characteristics. The results indicate that both the intensity of driving behavior and the degree of deployment–process coupling are positively associated with DAS signal sensing characteristics. This study further examines the principles, advantages, limitations, and application scenarios of various DAS signal processing algorithms. Traditional methods are becoming less effective in handling massive data generated by numerous distributed nodes. Although deep learning achieves high classification accuracy and low latency, its generalization capability remains limited. Finally, this study discusses DAS-based traffic status perception frameworks and outlines key research frontiers in vehicle status monitoring using DAS technology. Full article

Accurate detection of dangerous driving behaviors is crucial for improving the safety of intelligent transportation systems. However, existing methods often struggle with limited feature extraction capabilities and insufficient attention to multiscale and contextual information. To overcome these limitations, we propose YOLO-AFR (YOLO with Adaptive Feature Refinement) for dangerous driving behavior detection. YOLO-AFR builds upon the YOLOv12 architecture and introduces three key innovations: (1) the redesign of the original A2C2f module by introducing a Feature-Refinement Feedback Network (FRFN), resulting in a new A2C2f-FRFN structure that adaptively refines multiscale features, (2) the integration of self-calibrated convolution (SC-Conv) modules in the backbone to enhance multiscale contextual modeling, and (3) the employment of a SEAM-based detection head to improve global contextual awareness and prediction accuracy. These three modules combine to form a Calibration-Refinement Loop, which progressively reduces redundancy and enhances discriminative features layer by layer. We evaluate YOLO-AFR on two public driver behavior datasets, YawDD-E and SfdDD. Experimental results show that YOLO-AFR significantly outperforms the baseline YOLOv12 model, achieving improvements of 1.3% and 1.8% in mAP@0.5, and 2.6% and 12.3% in mAP@0.5:0.95 on the YawDD-E and SfdDD datasets, respectively, demonstrating its superior performance in complex driving scenarios while maintaining high inference speed. Full article

Neural network (NN)-based controllers have emerged as a paradigm-shifting approach in modern control systems, demonstrating unparalleled capabilities in governing nonlinear dynamical systems with inherent uncertainties. This comprehensive review systematically investigates the theoretical foundations and practical implementations of NN controllers through the prism of Lyapunov stability theory, NN controller frameworks, and robustness analysis. The review establishes that recurrent neural architectures inherently address time-delayed state compensation and disturbance rejection, achieving superior trajectory tracking performance compared to classical control strategies. By integrating imitation learning with barrier certificate constraints, the proposed methodology ensures provable closed-loop stability while maintaining safety-critical operation bounds. Experimental evaluations using chaotic system benchmarks confirm the exceptional modeling capacity of NN controllers in capturing complex dynamical behaviors, complemented by formal verification advances through reachability analysis techniques. Practical demonstrations in aerial robotics and intelligent transportation systems highlight the efficacy of controllers in real-world scenarios involving environmental uncertainties and multi-agent interactions. The theoretical framework synergizes data-driven learning with nonlinear control principles, introducing hybrid automata formulations for transient response analysis and adjoint sensitivity methods for network optimization. These innovations position NN controllers as a transformative technology in control engineering, offering fundamental advances in stability-guaranteed learning and topology optimization. Future research directions will emphasize the integration of physics-informed neural operators for distributed control systems and event-triggered implementations for resource-constrained applications, paving the way for next-generation intelligent control architectures. Full article

Urban transportation systems evolve toward greater diversification, scalability, and complexity. To address the escalating issue of urban traffic congestion, leveraging modern information technologies to enhance the integration of multiple transportation modes and maximize overall efficiency has emerged as a promising strategy. This study focuses on the decision making problem of urban multimodal transportation travel paths, integrating the time-varying characteristics of public transportation schedules and networks. We consider passengers’ diverse needs and systematically investigate how to optimize travel paths to minimize travel time while adhering to constraints, such as the number of interchanges and travel costs. To address this NP-hard problem, we propose and implement two optimization algorithms: a variable-length coding genetic algorithm (V-GA) and a full permutation coding genetic algorithm (F-GA). Detailed numerical analysis validates the effectiveness of both algorithms, with the V-GA demonstrating significant advantages over the F-GA in terms of solution efficiency. Our findings provide novel perspectives and methodologies for optimizing urban multimodal transportation travel paths, offering robust theoretical foundations and practical tools for enhancing urban traffic planning and travel service efficiency. Full article

Personalized urban services are becoming increasingly significant in smart city systems. This shift from intelligent transportation to smart cities broadens the scope of personalized services, encompassing not just travel but a wide range of urban activities and needs. This study proposes a knowledge graph-based Hidden Markov Model (KHMM) to improve personalized route recommendations by incorporating both spatial and semantic relationships between Points of Interest (POIs) in a unified decision-making framework. The KHMM expands the state space of the traditional Hidden Markov Model using a knowledge graph, enabling the integration of multi-dimensional POI information and higher-order relationships. This approach reflects the spatial complexity of urban environments while addressing user-specific preferences. The model’s empirical evaluation, focused on Changsha, China, examined how temporal variations in public attention to POIs influence route selection. The results show that incorporating dynamic temporal and spatial data significantly enhances the model’s adaptability to changing user behaviors, supporting real-time, personalized route recommendations. By bridging individual preferences and road network structures, this research provides key insights into the factors shaping travel behavior and contributes to the development of adaptive and responsive urban transportation systems. These findings highlight the potential of the KHMM to advance intelligent travel services, offering improved spatial accuracy and personalized route planning. Full article

Some complex social networks are driven by adaptive and co-evolutionary patterns. However, these can be difficult to detect and analyse since the links between actors are circumstantial and often not revealed. This paper employs a Geographic Information Systems (GIS) integrated multi-agent-based approach to simulate co-evolution in a complex social network. A case study is proposed for the modelling of contractual relationships between road freight transport companies. The model employs empirical data from a survey of transport companies located in the Basque Country (Spain) and utilises the DBSCAN community detection algorithm to simulate the effect of cluster size in the network. Additionally, a local spatial association indicator is employed to identify potentially favourable environments. The model enables the evolution of the network, leading to more complex collaborative structures. By means of iterative simulations, the study demonstrates how collaborative networks self-organise by distributing activity and knowledge and evolving into complex polarised systems. Furthermore, the simulations with different minimum cluster sizes indicate that clusters benefit the agents that are part of them, although they are not a determining factor in the network participation of other non-clustered agents. Full article

This study investigates the relationship between intercity transportation accessibility and network centrality across South Korea by integrating Global Positioning System (GPS)-based mobility data with graph-theoretic centrality measures, including degree, PageRank, local clustering coefficient, harmonic, Katz, and information centrality. Employing both statistical modeling and machine learning techniques, this analysis uncovers key structural patterns and interaction effects within the national mobility network. The findings yield several important insights. First, the Seoul Metropolitan Area emerges as the dominant mobility hub, with Busan, Daegu, and Daejeon functioning as secondary centers, reflecting a polycentric urban configuration. Second, intermediary transfer hubs—despite having lower direct connectivity—substantially enhance overall network efficiency and interregional mobility. Third, transportation accessibility, particularly in relation to regional transit and highway infrastructure, exhibits a significant association with centrality measures and strong feature importance, identifying these modes as primary determinants of spatial connectivity. Fourth, the impact of accessibility on centrality is characterized by nonlinear relationships and threshold effects. By elucidating the complex interplay between mobility infrastructure and spatial network dynamics, this study contributes to a more comprehensive understanding of regional connectivity and network centrality and offers policy-relevant insights for future transportation planning. Full article

Infrastructure projects that transform ecosystems and landscapes, such as hydropower developments, are essential for economic growth but pose significant socio-environmental challenges. Addressing these complexities requires advanced, dynamic management strategies. This study presents the Bayesian integrated risk mitigation model (BIRMM), a novel probabilistic framework designed to augment traditional environmental impact assessments. BIRMM enables comprehensive risk evaluation, scenario-based analysis, and mitigation planning, empowering stakeholders to make informed decisions throughout project lifecycles. BIRMM integrates socio-environmental and economic risks using a three-dimensional risk assessment approach grounded in a Bayesian belief network influence diagram. It provides a holistic view of risk interactions by capturing interdependencies across spatial, temporal, and magnitude dimensions. Through simulation of risk dynamics and adaptive evaluation of mitigation strategies, BIRMM offers actionable insights for resource allocation, enhancing project resilience, and minimizing socio-environmental disruptions. The framework was validated using the Balakot Hydropower Project in Pakistan. BIRMM successfully simulated proposed risks and assessed mitigation strategies under varying scenarios, demonstrating its reliability in navigating complex socio-environmental challenges. The case study highlighted its potential to support adaptive decision-making across all project phases. With its versatility and practical ease, BIRMM is particularly suited for large-scale energy, transportation, and urban development projects. By bridging gaps in traditional methodologies, BIRMM advances sustainable development practices, promotes equitable stakeholder outcomes, and establishes itself as an indispensable decision-support tool for modern infrastructure projects. Full article