Search Results (514)

Long and narrow shielded confined spaces, represented by traffic tunnels and underground utility tunnels, constitute critical application scenarios for indoor and underground positioning services. Despite their relatively simple geometric configurations, such environments suffer from severe spatial distortion of geometric dilution of precision (GDOP). Coupled with pervasive low-elevation signal propagation and intensive multipath reflection effects, conventional BeiDou Navigation Satellite System (BDS) positioning services are unable to provide continuous and reliable coverage in these scenarios. To date, existing research on high-precision pseudolite positioning for narrow confined spaces remains largely confined to theoretical analysis and laboratory experimental verification, while systematic studies on application-oriented signal atlas feature network design are significantly insufficient, forming a prominent gap that restricts the practical engineering deployment of relevant technologies. To address the aforementioned technical bottlenecks, this paper proposes a novel BDS pseudolite signal atlas network design method to improve the continuity, stability and comprehensive positioning performance in spatially distorted narrow shielded environments. Field vehicular tests were carried out in actual engineering tunnels and underground utility tunnels to systematically analyze the variation characteristics of raw BDS pseudolite observation data, including pseudorange, carrier phase, carrier-to-noise ratio (C/N₀) and Doppler shift. The test results verified that kinematic Doppler parameters exhibited outstanding stability in complex shielded environments with strong multipath interference. On this basis, a spatial feature model based on kinematic Doppler measurements was constructed, and wavelet denoising technology was adopted to extract effective typical spatial feature parameters. Combined with the deterministic one-to-one mapping relationship between Doppler peak characteristics and spatial positions, a multi-peak kinematic Doppler atlas was established, which eliminates the dependence on pre-deployment data collection, dedicated database construction and offline model training. Furthermore, comprehensively considering multi-dimensional constraints such as spatial environment scale, carrier dynamic characteristics and terminal output rate, the atlas network scheme was optimized to achieve a balanced trade-off among positioning detection accuracy, absolute positioning precision and suppression of the pseudolite near-far effect. Comparative experimental results demonstrate that the proposed BDS pseudolite atlas network effectively resolves the inherent GNSS positioning difficulty in long and narrow shielded spaces. Benefiting from the rational spectral peak configuration strategy, the system can satisfy the continuous and stable positioning requirements of multiple carrier types including motor vehicles and railway locomotives under variable motion speeds and terminal output rates. This study provides a robust and feasible technical solution for high-precision BDS positioning services in long and narrow shielded confined spaces, and holds favorable engineering application prospects for underground navigation scenarios. Full article

Suburban areas increasingly shape contemporary urbanisation, yet public-space dynamics in these environments are weakly represented by conventional urban indicators. This study examines suburban public-space use as a behavioural dimension of urban metabolism, understood here as the observable patterns of human movement, activity, and co-presence occurring within suburban public spaces. It addresses the limited ability of density- or infrastructure-based measures to capture everyday spatial practices in dispersed, car-oriented settings. While urban metabolism research has expanded beyond material and energy flows, empirical evidence linking configurational accessibility with directly observed public-space behaviour in suburban contexts remains limited. To address this gap, we integrate district-scale space syntax analysis with site-scale UAV-based observation across five public spaces in and around Gdańsk, Poland. Based on a dataset comprising 30 standard observation sessions conducted in September and October 2024, spatial syntax indicators (integration and choice) were used to characterise configurational accessibility and support location selection, while UAV monitoring captured traffic intensity, stationary presence, diversity of activities, and temporal rhythms of use. The results reveal distinct behavioural metabolic profiles shaped by interactions between spatial configuration, functional programming, and temporal dynamics. These profiles vary depending on the function of public spaces and dominant modes of movement (pedestrian or vehicular). The study demonstrates that suburban urban metabolism cannot be interpreted through configurational accessibility or residential density alone. By linking space syntax measures with a repeatable UAV observation protocol, the proposed framework supports comparative assessment of suburban public-space performance and informs planning interventions aimed at suburban transformation and improved accessibility. Full article

Ensuring the integrity of pavement structures necessitates a thorough evaluation of both surface-level damage and subsurface mechanical performance. This study proposes an integrated, non-destructive assessment framework tailored for semi-rigid base asphalt pavements subjected to repeated vehicular loading via MLS66 full-scale accelerated testing equipment. The proposed methodology integrates ground-penetrating radar (GPR) using the CO4080 system and dynamic response measurements from a falling weight deflectometer (FWD) to characterize structural conditions across multiple depths. Comparative analysis between pre-loading and post-loading data revealed significant deterioration trends in the surface layers, with stiffness loss closely associated with increasing load repetitions. In contrast, the underlying base layers exhibited stable deformation characteristics, with variations in deflection basin indices remaining within ±5%. Subgrade dielectric properties derived from GPR data confirmed consistent compaction quality throughout the test site. Statistical analysis further validated the synergy between GPR and FWD results, demonstrating that the combined application enhances diagnostic accuracy. The dual-method approach improved overall evaluation reliability by approximately 22–35% compared to using individual techniques alone under accelerated pavement testing scenarios. These findings support broader implementation of integrated sensing systems and highlight the potential for application across varied pavement types and loading conditions. Full article

Conventional standards for evaluating the Korean Highway Capacity Manual (HCM) and U.S. HCM often inadequately represent the localized macroscopic traffic dynamics inherent in complex urban networks. To address this limitation, this study proposes an adaptive branching framework for level of service (LOS) reclassification, guided by the empirical identifiability of fundamental diagrams (FDs) and vehicular density distribution patterns. The methodology classifies traffic states into four categories: (a) FD-based LOS, (b) segmented FD-based LOS, (c) single-state LOS, and (d) empirical free-flow speed-based LOS. These categories redefine LOS criteria based on the temporal and spatial conditions prevalent in urban environments. The proposed reclassified LOS framework, applied to twenty-eight urban corridors across four distinct urban typologies using a reference free-flow speed, effectively captures region-specific performance variations. Ultimately, this research establishes a robust, data-driven methodological framework for localized LOS recalibration, thereby significantly enhancing the realism of urban traffic evaluation. Full article

The integration of renewable energy and rising electricity demand strain system flexibility. While price-based demand response (PBDR) improves flexibility through pricing signals, its efficacy hinges critically on accurate consumer modeling. Recognizing this pivotal role, this paper provides a comprehensive review of consumer models in PBDR and their applications to electric vehicles (EVs). First, a unified conceptual framework is presented, delineating the energy, information and financial flows among the system operator (SO), load aggregators (LAs), and end-users, and highlighting the central position of consumer modeling. Second, existing modeling approaches are systematically classified into four categories, namely rule-based, optimization-based, data-driven, and hybrid, to facilitate the selection of appropriate models by researchers and stakeholders for diverse scenarios. Furthermore, the application and adaptation of these models to EVs are critically analyzed, accounting for unique vehicular constraints. Subsequently, a systematic summary of the key characteristics and existing research gaps is provided. Finally, key directions for future research are proposed accordingly, aimed at incorporating bounded rationality into behavioral models, developing individualized consumer modeling coupled with user-specific dynamic pricing, and extending consumer modeling to residential multi-energy prosumers in integrated energy systems. Full article

Highway slope instability under coupled traffic and environmental loading poses critical threats to transportation safety in mountainous regions, where dynamic vehicular forces interact with complex geological conditions in ways that single-modality monitoring cannot fully resolve. This study proposes MMDF-DEWS, a multi-modal data fusion and deep learning-based early-warning system that, for the first time, treats quantified traffic-loading parameters as a first-class input modality alongside Interferometric Synthetic Aperture Radar (InSAR) displacement, Global Navigation Satellite System (GNSS) measurements, and embedded geotechnical sensor outputs. A hybrid Transformer–bidirectional LSTM backbone with hierarchical attention-guided fusion enables the model to capture both long-range temporal deformation trends and short-term dynamic responses triggered by heavy-vehicle passage. To guard against over-fitting on a limited number of instability events, we adopt chronological training/validation/test partitioning, five-fold cross-validation for hyper-parameter selection, stratified focal-loss training, and cross-dataset evaluation on two independent public benchmarks: the Three Gorges Reservoir Area Landslide Monitoring Dataset (TGRA-LMD) and the European Ground Motion Service Sentinel-1 (EGMS-S1) dataset. The framework outperforms six state-of-the-art baselines by 4.7–11.2% in F1-score, and ablation studies confirm that the explicit inclusion of traffic-loading features alone improves Warning-class recall by 6.3 percentage points, demonstrating a direct and physically grounded link between cyclic vehicular loading and slope-state prediction. The system satisfies operationally relevant engineering targets for warning lead time and false-alarm rate, and provides interpretable attention maps suitable for transportation-authority decision support. Full article

The transportation sector is the primary consumer of vehicle fuel worldwide and is thus a major contributor to climate change via carbon dioxide (CO₂) emissions. In addition to severe environmental impacts, such as global warming, droughts, floods, and rising sea levels, these emissions have a negative effect on public health by increasing the prevalence of respiratory disease. Achieving environmental sustainability through regulatory oversight requires a strong understanding of vehicular fuel consumption and CO₂ emissions. However, accurate modeling of these remains challenging due to the complex non-linear relationships between various vehicular characteristics and the lack of interpretability of many predictive models. Traditional linear models often fail to capture high-dimensional data complexities, while black-box methods provide few actionable insights for policymaking. To address these gaps, we developed a robust and data-driven two-stage machine-learning (ML) framework designed to enhance model performance and reliability. First, we implemented standard data preprocessing, enhanced feature engineering, and hyperparameter tuning for 14 cutting-edge ML algorithms and three advanced modeling techniques to explore their predictive performance. Second, we introduced three interpretable explainable AI (XAI) approaches. These were evaluated on a publicly available Kaggle static dataset of 550 vehicles, dominated by gasoline-powered vehicles, with only two diesels and two electric vehicles. The tuned CatBoost model demonstrated strong predictive performance, achieving an impressive R² of 0.9260, a root mean square error (RMSE) of 1.1759, and a mean absolute error (MAE) of 0.8147. In parallel, we deterministically estimated CO₂ emissions from fuel consumption, which provide direct estimates of tailpipe emissions. To ensure transparency and model interpretability, we employed Shapley additive explanations, local interpretable model-agnostic explanations, and permutation importance to identify the key factors contributing to the model predictions. Across the explainability analyses, cylinder count, front-wheel drive (drive_fwd), and the displacement–year interaction were the primary contributors to the predicted combined miles per gallon; in other words, they strongly affected fuel consumption. Collectively, these findings demonstrate the ability of the proposed model to capture complex feature relationships; thus, it offers a valuable tool for researchers and policymakers in sustainability planning and emission control. Future research should focus on real-time driving or dynamic measurements data and enhancing practical applications to further reduce emissions and promote environmental sustainability. Full article

This paper investigates secure and low-latency communications in UAV-mounted simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted urban vehicular networks, where severe blockage, high vehicle mobility, eavesdropping threats, and delay-sensitive traffic services coexist. In the considered system, the UAV is used not only as an aerial carrier for the STAR-RIS but also as a mobile intelligent control node that can dynamically adjust its horizontal aerial position according to vehicle distribution, blockage conditions, and eavesdropping threats. First, a UAV-STAR-RIS-assisted vehicular communication system model is developed by jointly considering urban blockage, vehicle mobility, passive eavesdropping attacks, queueing dynamics, and UAV flight constraints. Then, a high-dimensional, non-convex, and strongly coupled dynamic optimization problem is formulated to maximize the long-term average secure and low-latency utility through the joint optimization of the UAV trajectory, the STAR-RIS transmission–reflection partition ratio, the phase-shift matrices, and the transmit power allocation. Furthermore, the problem is modeled as a Markov decision process with continuous state and action spaces, and a hierarchical constrained soft actor–critic (HC-SAC)-based joint control algorithm is proposed to enable adaptive UAV movement, STAR-RIS configuration, and power control in complex dynamic environments. Simulation results demonstrate that the proposed method outperforms DDPG and several structural benchmark schemes. In the representative evaluation, the proposed HC-SAC achieves an average delay of 10.85 slots and a secrecy outage probability of 0.7160, compared with 11.72 slots and 0.8501 for PPO, and 11.94 slots and 0.8599 for DDPG. Although PPO provides the highest average secrecy rate and successful service ratio, the proposed method still maintains a competitive secure communication capability and service reliability. A normalized composite utility analysis further shows that HC-SAC attains the highest utility value of 0.9254, indicating a more favorable security–latency trade-off in complex urban vehicular scenarios. Full article

Routing in Opportunistic Networks (OppNets) is continuously challenged by intermittent connectivity and severe resource constraints. To address these limitations, this paper proposes CASTRO, a novel routing architecture, alongside its reinforcement learning extension, QL-CASTRO. The primary novelty lies in the mathematical modeling of disconnection intervals (OFF-mode) to extract precise social indicators—Strength, Trend, and Regularity—providing a robust alternative to traditional encounter-frequency metrics. To overcome the latency penalties inherent to conservative social routing, QL-CASTRO integrates a tabular Q-Learning paradigm. This acts as a dynamic acceleration mechanism, fusing social metrics with autonomous delivery delay estimates and strict message retirement policies. Performance was rigorously evaluated using the ONE simulator across dense pedestrian (Helsinki) and sparse vehicular (Manaus) environments. The results demonstrate that both protocols achieve high delivery rates near 90%. Crucially, QL-CASTRO significantly reduces average delivery latency compared to the baseline CASTRO protocol while maintaining moderate overhead and low energy consumption. Ultimately, this hybrid approach offers a scalable, resource-efficient routing solution for dynamic IoT environments where system longevity and information integrity are paramount. Full article

In highly dynamic vehicular ad hoc networks (VANETs), vehicles frequently move across the coverage areas of multiple roadside units (RSUs), making secure and efficient continuous vehicle-to-infrastructure access essential. However, repeated full authentication and key agreement for each new RSU access impose considerable computational and communication overhead. This paper proposes a state-assisted privacy-preserving mutual authentication and key agreement scheme for lightweight multi-RSU access in VANETs. The proposed scheme consists of initial and subsequent authentication phases. In the initial phase, elliptic curve cryptography (ECC) is used to achieve anonymous mutual authentication and session key establishment between vehicles and RSUs. In the subsequent authentication phase, a vehicle leverages follow-up authentication state securely forwarded by the previous RSU to complete fast authentication with a neighboring RSU using only hash and XOR operations. In addition, physically unclonable functions (PUFs) are deployed on both vehicles and RSUs to protect critical secrets. Security analysis shows that the proposed scheme achieves mutual authentication, anonymity preservation, and resistance to common attacks. Performance evaluation shows that it reduces the computational cost of subsequent authentication by more than 90% while maintaining low communication overhead. Full article

Vehicular Ad Hoc Networks (VANETs) face significant challenges in high-mobility environments, where dynamic channel conditions, particularly Doppler Shift (DS), degrade communication reliability and increase latency, thereby undermining safety-critical applications. To address these limitations, this paper proposes a neural network (NN)-based link adaptation strategy trained using the Scaled Conjugate Gradient (SCG) algorithm. SCG is selected as a second-order approximation optimizer that leverages curvature information to produce well-conditioned weight updates particularly suited to the small, physics-constrained training dataset. The SCG-optimized model dynamically adjusts transmission parameters to mitigate DS effects, improving real-time adaptability by explicitly incorporating Doppler Shift as a key input feature. Simulation results demonstrate that the proposed approach outperforms both the conventional Auto Rate Fallback (ARF) method and the SampleRate baseline. Specifically, the SCG-based strategy achieves an overall throughput improvement of +34.6% relative to ARF (1.77 Mbps vs. 1.32 Mbps) across all tested conditions, with condition-specific gains of +16.1% at 5 Hz Doppler (0.9 km/h), +21.7% at 750 Hz (137.3 km/h), and +35.2% at 1500 Hz (274.6 km/h), while consistently reducing transmission duration. A formal ablation study confirms that the Doppler Shift feature alone contributes +67% to +78% throughput gain at high mobility (DS > 900 Hz) compared to an SNR-only model. The main contributions of this work are threefold: (i) the explicit integration of Doppler Shift as a first-class input feature for link adaptation; (ii) the application of SCG optimization for fast, stable training of a lightweight feedforward neural network on a compact, physics-constrained dataset; and (iii) the formal ablation study that isolates and quantifies the Doppler feature’s contribution, establishing that the performance gain is attributable to feature engineering rather than the neural network architecture alone. This approach offers a scalable, real-time solution for Doppler-resilient VANET link adaptation. Full article

Investigating the fundamental link between microscopic vehicular motion parameters and macroscopic traffic flow states is pivotal for advancing refined traffic state estimation research and propelling the progression of Intelligent Transportation Systems. In this paper, a basic Transformer model has been optimized and extended by incorporating embedding and pooling layers, and the model’s hyperparameters have been finely tuned through random search cross-validation. The creation of the Generalized Optimized Transformer (GOT) model ensued, where the multi-head attention mechanism adeptly encapsulates all spatiotemporal dynamics inherent in traffic data. Various benchmark models such as LSTM, RNN, and Transformer were put to test, each demonstrating unique performances in managing different traffic flow states. Among them, the GOT model exhibited superior performance, adeptly handling lane-level traffic state estimation tasks derived from microscopic vehicle trajectory data. In conclusion, this research elucidates the intricate and mutable mapping relationship between microscopic vehicular motion parameters and traffic flow states, proficiently executing lane-level traffic state estimation grounded on microscopic trajectory data. This paper is anticipated to provide fresh insights into the understanding of the complex relationship between microscopic vehicular motion parameters and traffic flow states. Full article

Vehicle-to-everything (V2X) communication systems impose stringent latency and reliability requirements that are difficult to satisfy in highly dynamic wireless environments. Although reconfigurable intelligent surfaces (RISs) and unmanned aerial vehicles (UAVs) have independently demonstrated potential in enhancing wireless coverage, most existing RIS–UAV frameworks rely on idealized assumptions such as perfect channel state information (CSI) and static user scenarios. In this paper, a multi-RIS-assisted UAV-enabled V2X communication framework is proposed that explicitly accounts for vehicular mobility, latency constraints, and mobility-induced CSI aging. Multiple RIS panels are cooperatively deployed to eliminate coverage blind spots and ensure link continuity in realistic V2X environments. A joint UAV mobility and RIS phase optimization approach is proposed under outdated CSI to improve link reliability. Additionally, a time-varying performance analysis is carried out for understanding the dynamic behavior of signal-to-noise ratio (SNR) and average bit error rate (ABER) for mobility-aware CSI aging. Simulation results demonstrate that the proposed framework reduces the ABER by approximately 75% compared to a conventional single-RIS system under outdated CSI at 20 dB SNR ($1.07\times {10}^{-1}$ vs. $4.32\times {10}^{-1}$), while substantially suppressing outage intervals in high-mobility V2X scenarios ($v=20$ m/s, CSI delay $\tau =20$ ms), confirming the effectiveness of cooperative multi-RIS assistance for safety-critical vehicular communications. Full article

Urban traffic congestion presents a complex challenge driven by intricate spatial dependencies and non-stationary temporal dynamics. While Multi-Agent Deep Reinforcement Learning has shown promise for Traffic Signal Control, existing approaches often struggle with partial observability and fail to coordinate effectively across large-scale, heterogeneous road networks. In this paper, we propose HydraLight (HYbrid Deep Reinforcement Learning Architecture for Traffic Lights), a novel spatio-temporal framework that integrates Graph Attention Networks and Temporal Transformers. To overcome the localized myopia of standard graph methods, HydraLight introduces a Global Pooling Context module that broadcasts macroscopic, citywide traffic summaries, enabling agents to proactively mitigate systemic gridlock. Furthermore, to facilitate robust multi-scenario training, we introduce a Unified Prioritized Experience Replay (Unified PER) module that normalizes Temporal-Difference errors, preventing task dominance across diverse topologies. Extensive experiments on the RESCO benchmark across five synthetic and real-world networks demonstrate that HydraLight consistently outperforms state-of-the-art baselines (including X-Light and CoSLight).Byreducing traffic congestion, travel delays, and idle waiting times, the proposed framework also contributes to more sustainable urban mobility through improved traffic flow efficiency, lower fuel consumption, and reduced vehicular carbon emissions. Notably, the proposed architecture excels in structurally irregular environments, achieving up to 13.07% reduction in average travel time on complex arterial networks and consistently improving queue stability and waiting-time minimization across both synthetic and real-world RESCO benchmarks compared to state-of-the-art baselines. Full article

The thermodynamic prediction of the fast refueling process for vehicular hydrogen storage cylinders faces the complex problem of modeling multi-layer composite walls. Drawing on the series thermal resistance principle, this paper introduces an equivalent thermal conductivity approach, simplifying the multi-layer structure into homogeneous material. Combined with the real-gas-state equation, a coupled thermodynamic framework combining zero-dimensional gas dynamics and one-dimensional cylinder wall heat transfer is developed. The comparison and verification with the 70 MPa fast charging experimental data have demonstrated that the proposed model exhibits sufficient accuracy and robustness for the problem. By comparing the temperature rise changes of different volume type-III gas cylinders, it was found that the surface area-to-volume ratio (A/V) was the primary geometric factor—the key geometric parameter that governs the temperature rise behavior. Larger volume gas cylinders exhibit more significant temperature rise due to their lower heat dissipation efficiency. A further comparison of the thermal response characteristics between Type-III and Type-IV cylinders demonstrates that the equivalent thermal conductivity is the dominant parameter determining the temperature rise behavior: The lower this coefficient, the stronger the limitation on the cylinder’s heat dissipation capacity, and the more pronounced the temperature rise. The proposed method not only ensures accuracy but also reduces the complexity of the modeling process, providing an efficient theoretical tool for optimizing the refueling strategy and conducting thermal safety assessment of vehicular hydrogen storage systems. Full article