Search Results (4,347)

In vehicular networks, inter-vehicle data sharing and collaborative computing improve traffic efficiency and driving experience. However, centralized processing faces challenges with privacy, communication bottlenecks, and real-time performance. This paper proposes a trust assessment mechanism for vehicular federated learning based on graph neural network (GNN) edge weight similarity. An explainable asynchronous federated learning data sharing framework is designed, consisting of permissioned asynchronous federated learning and a locally verifiable directed acyclic graph (DAG). The GNN connection weights perform reputation assessment on edge devices through DAG-based verification, while deep reinforcement learning (DRL) enables explainable node selection to improve asynchronous federated learning efficiency. The proposed explainable incentive mechanism based on GNN edge weight similarity and DAG can not only effectively prevent malicious node attacks but also improve the fairness and explainability of federated learning. Extensive experiments across different participant scales (30–200 nodes), various asynchrony degrees ($\alpha$ = 1–5), and malicious node attack scenarios (up to 50% malicious nodes) demonstrate that our method consistently outperforms state-of-the-art approaches, achieving up to 99.2% accuracy with significant improvements of 1.3–3.1% over existing trust-based federated learning methods and maintaining 95% accuracy even under severe attack conditions. The results show that the proposed scheme performs well in terms of learning accuracy and convergence speed. Full article

Dynamic Spectrum Sharing (DSS) enables spectrum sharing between Long-Term Evolution (LTE) and New Radio (NR) systems, addressing spectrum scarcity in NR. To avoid interference when supporting NR traffic within LTE spectrum, key factors must be compatible. Effective DSS techniques are essential for coexistence. This paper discusses these issues in two parts. Part I covers LTE and NR coexistence using DSS, introducing resource grids, control signals, and channels, and explores DSS approaches for NR data traffic, including NR Synchronization Signal/Physical Broadcast Channels (SSB) transmission via LTE Multicast-Broadcast Single-Frequency Network (MBSFN) and non-MBSFN subframes with associated challenges and standardization efforts for DSS improvement. Part II presents a DSS technique using MBSFN subframes in a heterogeneous network with a macrocell and picocells running on LTE, and in-building small cells running on NR, sharing LTE spectrum via DSS. An optimization problem is formulated to manage traffic through MBSFN allocation, determining the optimal number of MBSFN subframes per LTE frame. System simulations indicate DSS improves Spectral and Energy Efficiency in small cells. The paper concludes with key lessons for LTE and NR coexistence. Full article

Extreme weather events, such as storms, pose significant challenges to the reliability and efficiency of urban road networks, making intersection design and management critical to maintaining mobility. This paper addresses the dual objectives of traffic efficiency and resilience by evaluating the performance of roundabouts, signalized, and two-way stop-controlled (TWSC) intersections under normal and storm-disrupted conditions. A mixed-method approach was adopted, combining a heuristic framework from the Highway Capacity Manual with microsimulations in AIMSUN Next. Three Polish case studies were examined; each was modeled under alternative control strategies. The findings demonstrate the superior robustness of roundabouts, which retain functionality during power outages, while signalized intersections reveal vulnerabilities when control systems fail, reverting to less efficient TWSC behavior. TWSC intersections consistently exhibited the weakest performance, particularly under high or uneven traffic demand. Despite methodological differences in delay estimation, the convergence of results through Level of Service categories strengthens the reliability of findings. Beyond technical evaluation, the study underscores the importance of resilient intersection design in climate-vulnerable regions and the value of integrating analytical and simulation-based methods. By situating intersection performance within urban resilience, this research provides actionable insights for policymakers, planners, and engineers seeking to balance efficiency with adaptability in infrastructure planning. Full article

We propose a complexity-aware extension of the Course–Speed (CS) model that integrates an AIS-derived Traffic Complexity Index (TCI) based on change in speed (ΔV) and course (Δθ) to quantify maneuvering complexity in nonlinear crossing waters. The framework consists of: (i) data preprocessing and gating to ensure navigationally valid AIS samples; (ii) CS index computation using distribution-aware statistics; (iii) TCI estimation from variability in speed and course along intersecting flows; and (iv) an integrated CS–TCI for interpretable mapping and ranking. Using one year of AIS data from a high-density crossing area near the Korean coast, we show that the integrated index reveals crossing hotspots and small-vessel maneuvering burdens that are not captured by spatial regularity metrics alone. The results remain robust across reasonable parameter ranges (e.g., speed filter and σ-based weighting), and they align with operational observations in vessel traffic services (VTS). The proposed CS–TCI offers actionable decision support for port and coastal operations by jointly reflecting traffic smoothness and complexity; it can complement collision-risk screening and efficiency-oriented planning (e.g., energy and emission considerations). The approach is readily transferable to other crossing waterways and can be integrated with real-time monitoring to prioritize control actions in complex marine traffic environments. Full article

Driver fatigue is a primary factor in traffic accidents and poses a serious threat to road safety. To address this issue, this paper proposes a multi-feature fusion fatigue detection method based on an improved YOLOv8 model. First, the method uses an enhanced YOLOv8 model to achieve high-precision face detection. Then, it crops the detected face regions. Next, the lightweight PFLD (Practical Facial Landmark Detector) model performs keypoint detection on the cropped images, extracting 68 facial feature points and calculating key indicators related to fatigue status. These indicators include the eye aspect ratio (EAR), eyelid closure percentage (PERCLOS), mouth aspect ratio (MAR), and head posture ratio (HPR). To mitigate the impact of individual differences on detection accuracy, the paper introduces a novel sliding window model that combines a dynamic threshold adjustment strategy with an exponential weighted moving average (EWMA) algorithm. Based on this framework, blink frequency (BF), yawn frequency (YF), and nod frequency (NF) are calculated to extract time-series behavioral features related to fatigue. Finally, the driver’s fatigue state is determined using a comprehensive fatigue assessment algorithm. Experimental results on the WIDER FACE and YAWDD datasets demonstrate this method’s significant advantages in improving detection accuracy and computational efficiency. By striking a better balance between real-time performance and accuracy, the proposed method shows promise for real-world driving applications. Full article

Distributed denial of service (DDoS) attacks pose a serious risk to the operational stability of a network for companies, often leading to service disruptions and financial damage and a loss of trust and credibility. The increasing sophistication and scale of these threats highlight the pressing need for advanced mitigation strategies. Despite the numerous existing studies on DDoS detection, many rely on large, redundant feature sets and lack validation for real-time applicability, leading to high computational complexity and limited generalization across diverse network conditions. This study addresses this gap by proposing a feature-optimized and computationally efficient ML framework for DDoS detection and mitigation using benchmark dataset. The proposed approach serves as a foundational step toward developing a low complexity model suitable for future real-time and hardware-based implementation. The dataset was systematically preprocessed to identify critical parameters, such as packet length Min, Total Backward Packets, Avg Fwd Segment Size, and others. Several ML algorithms, involving Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Cat-Boost, are applied to develop models for detecting and mitigating abnormal network traffic. The developed ML model demonstrates high performance, achieving 99.78% accuracy with Decision Tree and 99.85% with Random Forest, representing improvements of 1.53% and 0.74% compared to previous work, respectively. In addition, the Decision Tree algorithm achieved 99.85% accuracy for mitigation. with an inference time as low as 0.004 s, proving its suitability for identifying DDoS attacks in real time. Overall, this research presents an effective approach for DDoS detection, emphasizing the integration of ML models into existing security systems to enhance real-time threat mitigation. Full article

This study systematically investigates the velocity characteristics and coupling mechanisms of tunnel flow fields under the interactions of natural wind, traffic wind, mechanical ventilation, and structural factors (such as transverse passages and relative positions between vehicles and fans). Using CFD simulations combined with turbulence model analyses, the flow behaviors under different coupling scenarios are explored. The results show that: (1) Under natural wind conditions, transverse passages act as key pressure boundaries, reshaping the longitudinal wind speed distribution into a segmented structure of “disturbance zones (near passages) and stable zones (mid-regions)”, with disturbances near passages showing “amplitude enhancement and range contraction” as natural wind speed increases. (2) The coupling of natural wind and traffic wind (induced by moving vehicles) generates complex turbulent structures; vehicle motion forms typical flow patterns including stagnation zones, high-speed bypass flows, and wake vortices, while natural wind modulates the wake structure through momentum exchange, affecting pollutant dispersion. (3) When natural wind, traffic wind, and mechanical ventilation are coupled, the flow field is dominated by momentum superposition and competition; adjusting fan output can regulate coupling ranges and turbulence intensity, balancing energy efficiency and safety. (4) The relative positions of vehicles and fans significantly affect flow stability: forward positioning leads to synergistic momentum superposition with high stability, while reverse positioning induces strong turbulence, compressing jet effectiveness and increasing energy dissipation. This study reveals the intrinsic laws of tunnel flow field evolution under multi-factor coupling, providing theoretical support for optimizing tunnel ventilation system design and dynamic operation strategies. Full article

Traffic safety continues to be a pressing worldwide issue, with young drivers especially exposed to accidents because of limited experience, reckless behaviors, and risky practices such as driving under the influence of alcohol or other substances. In this scenario, reliable methods to evaluate psychomotor and sensory abilities essential for safe driving are highly needed. This study presents the development of a Virtual Reality (VR) prototype aimed at enhancing psychometric testing. The platform incorporates immersive environments to assess peripheral vision, reaction time, and motor accuracy, implemented with Oculus Quest 2, Blender, and Unity. The VR-based system was validated through black-box testing and user satisfaction surveys with a sample of 80 licensed drivers in single-session evaluations. The findings demonstrate that VR increases both precision and realism in psychomotor evaluations: 81.25% of participants perceived the scenarios as realistic, and 85% agreed that the system effectively measured critical driving skills. While a few users experienced minor discomfort, 97.5% recommended its application in practical assessments. This study highlights VR as a robust alternative to conventional psychometric/psychotechnical tests, capable of improving measurement reliability and user engagement and paving the way for more efficient and inclusive driver training initiatives. Full article

Road safety has emerged as a global issue, driven by the rapid rise in vehicle ownership and traffic congestion. Human error, like distraction, drowsiness, and panic, is the leading cause of road accidents. Conventional driver monitoring systems (DMSs) frequently fail to detect these emotional and cognitive states, limiting their potential to prevent accidents. To overcome these challenges, this work proposes a robust deep learning-based DMS framework capable of real-time detection and response to emotion-driven driver behaviors that pose safety risks. The proposed system employs convolutional neural networks (CNNs), specifically the Inception module and a Caffe-based ResNet-10 with a Single Shot Detector (SSD), to achieve efficient, accurate facial detection and classification. The DMS is trained on a comprehensive and diverse dataset from various public and private sources, ensuring robustness across a wide range of emotions and real-world driving scenarios. This approach enables the model to achieve an overall accuracy of 98.6%, an F1 score of 0.979, a precision of 0.980, and a recall of 0.979 across the four emotional states. Compared with existing techniques, the proposed model strikes an effective balance between computational efficiency and complexity, enabling the precise recognition of driving-relevant emotions, making it a practical and high-performing solution for real-world in-car driver monitoring systems. Full article

Autonomous driving is a cornerstone of intelligent transportation systems, where visual elements such as traffic signs, lights, and pedestrians are critical for safety and decision-making. Yet, existing datasets often lack diversity, underrepresent rare scenarios, and suffer from class imbalance, which limits the robustness of object detection models. While earlier reviews have examined general image enhancement, a systematic analysis of dataset augmentation for traffic visual elements remains lacking. This paper presents a comprehensive investigation of enhancement techniques tailored for transportation datasets. It pursues three objectives: establishing a classification framework for autonomous driving scenarios, assessing performance gains from augmentation methods on tasks such as detection and classification, and providing practical insights to guide dataset improvement in both research and industry. Four principal approaches are analyzed, including image transformation, GAN-based generation, diffusion models, and composite methods, with discussion of their strengths, limitations, and emerging strategies. Nearly 40 traffic-related datasets and 10 evaluation metrics are reviewed to support benchmarking. Results show that augmentation improves robustness under challenging conditions, with hybrid methods often yielding the best outcomes. Nonetheless, key challenges remain, including computational costs, unstable GAN training, and limited rare scene data. Future work should prioritize lightweight models, richer semantic context, specialized datasets, and scalable, efficient strategies. Full article

Real-time control of web traffic is a critical issue for network operators and service providers. It helps ensure robust service and avoid service interruptions, which has an important financial impact. However, due to the high speed and volume of actual internet traffic, standard multivariate time series models are inadequate for ensuring efficient real-time traffic management. In this paper we introduce a new model for functional time series analysis, developed by combining a local linear smoothing approach with an $L^1$-robust estimator of the quantile’s derivative. It constitutes an alternative, robust estimator for functional modal regression that is adequate to handle the stochastic volatility of high-frequency of web traffic data. The mathematical support of the new model is established under functional dependent case. The asymptotic analysis emphasizes the functional structure of the data, the functional feature of the model, and the stochastic characteristics of the underlying time-varying process. We evaluate the effectiveness of our proposed model using comprehensive simulations and real-data application. The computational results illustrate the superiority of the nonparametric functional model over the existing conventional methods in web traffic modeling. Full article

The rapid growth of electric vehicle (EV) charging demand poses significant challenges to distribution networks (DNs), particularly during public holidays when concentrated peaks occur near scenic areas and urban transport hubs. These sudden surges can strain transformer capacity and compromise supply reliability. Fixed soft open points (SOPs) are costly and underutilized, limiting their effectiveness in DNs with multiple transformers and asynchronous peak loads. To address this, from the perspective of power supply companies, this study proposes a mobile soft open point (MSOP)-based approach to enhance the hosting capacity of EV charging stations. The method pre-installs a limited number of fast-access interfaces (FAIs) at candidate transformers and integrates a semi-rolling horizon optimization framework to gradually expand interface availability while scheduling MSOPs daily. An automatic peak period identification algorithm ensures optimization focuses on critical load periods. Case studies on a multi-feeder distribution system coupled with a realistic traffic network demonstrate that the proposed method effectively balances heterogeneous peak loads, matches limited interfaces with MSOPs, and enhances system-level hosting capacity. Compared with fixed SOP deployment, the strategy improves hosting capacity during peak periods while reducing construction costs. The results indicate that MSOPs provide a practical, flexible, and economically efficient solution for power supply companies to manage concentrated holiday charging surges in DNs. Full article

The aim of this study was to empirically examine the effects of China’s Transit Metropolis Construction Pilot (TMCP) policy on pollution and carbon dioxide emission reductions based on annual panel data from 286 prefecture-level cities in China for the period 2011–2019, using a staggered difference-in-differences approach. The results show that the TMCP policy significantly reduced the annual total carbon monoxide and carbon dioxide emissions in pilot cities by approximately 1.624 million and 221.883 million tons, respectively. Further mechanism analysis demonstrated that the TMCP policy reduced pollution and carbon dioxide emissions by improving the operational efficiency of public transit, alleviating urban traffic congestion, and enhancing public environmental awareness. Finally, our heterogeneity analysis indicates that the pollution and carbon dioxide emission reduction effects of the TMCP policy were more pronounced in cities with poor public transit accessibility and low environmental regulation intensity. This study provides policymakers with valuable policy insights into effectively promoting public transit use, reducing urban air pollutants and carbon dioxide emissions, and developing a sustainable urban transportation system. Full article

Vehicular Ad Hoc Network (VANET) is a fundamental component of the intelligent transportation systems (ITS), providing critical road information to users. However, the volatility of VANETs creates significant vulnerabilities from malicious actors. Thus, verifying joining entities is crucial to maintaining the VANET’s communication security. Authentication delays must stay below 100 ms to meet VANET requirements, posing a major challenge for security. Our previous research introduced a Kerberos–Blockchain (KBC) authentication system that contains two main components separately: Authentication Server (AS) and Ticket Granting Server (TGS). However, this KBC architecture required an additional server to accommodate increasing vehicle volumes in urban environments, leading to higher infrastructure costs. This paper presents an integrated authentication server that merges AS and TGS into a Combined Server (CBS) while retaining blockchain security. We evaluate it using OMNeT++ with SUMO for traffic simulation and Ganache for blockchain implementation. Results show that CBS removes the need for an extra server while keeping authentication delays under 100 ms. It also improves throughput by $104\%$ and reduces signaling overhead by $45\%$ compared to KBC. By optimizing authentication without compromising security, the integrated server greatly enhances the cost-effectiveness and efficiency of VANET systems. Full article

The rapid advancement of 5G/6G networks and the Internet of Things has rendered mobile traffic patterns increasingly complex and dynamic, posing significant challenges to achieving precise cell-level traffic forecasting. Traditional deep learning models, such as LSTM and CNN, rely heavily on substantial datasets. When confronted with new base stations or scenarios with sparse data, they often exhibit insufficient generalisation capabilities due to overfitting and poor adaptability to heterogeneous traffic patterns. To overcome these limitations, this paper proposes a meta-learning framework—GMM-MCM-NF. This framework employs a Gaussian mixture model as a probabilistic meta-learner to capture the latent structure of traffic tasks in the frequency domain. It further introduces a multi-component synthesis mechanism for robust weight initialisation and a negative feedback mechanism for dynamic model correction, thereby significantly enhancing model performance in scenarios with small samples and non-stationary conditions. Extensive experiments on the Telecom Italia Milan dataset demonstrate that GMM-MCM-NF outperforms traditional methods and meta-learning baseline models in prediction accuracy, convergence speed, and generalisation capability. This framework exhibits substantial potential in practical applications such as energy-efficient base station management and resilient resource allocation, contributing to the advancement of mobile networks towards more sustainable and scalable operations. Full article