Search Results (159)

This study aims to investigate the tip resistance mechanism of open-ended steel pipe piles under partially plugged conditions by decomposing the load-sharing contribution of the ring zone and the internal soil core. A virtual static loading test was performed using the two-dimensional discrete element method (2D-DEM). Note that the findings of this study were obtained within the range of the 2D-DEM analysis conditions and do not intend to directly reproduce the three-dimensional arching mechanism or to establish equivalence between 2D and 3D responses. Quasi-static conditions were ensured by identifying loading parameters such that the energy residual remained ≤5% during driving, rest, and static loading phases, and the sensitivity criterion |Δq_b|/q_b ≤ 3% was satisfied when the loading rate was halved or doubled. The primary evaluation range of static loading was set to s/D = 0.1 (10% D), corresponding to the displacement criterion for confirming the tip resistance in the Japanese design specifications for highway bridges. For reference, the post-peak mechanism was additionally tracked up to s/D = 0.2 (20% D). Within a fixed evaluation window located immediately beneath the pile tip, high-contact-force (HCF) points were binarized using the threshold τ = μ + σ, and their occupancy ratio φ and normalized force intensity I* were calculated separately for the ring and core regions. A density-based contribution index (“K_{-density share}”) was defined by combining “strength × area” and normalizing by the geometric width. The results suggest that, for the sand conditions and particle-scale ratios examined (D/d_50 = 25–100), the ring zone tends to carry on the order of 85–90% of the tip resistance within the observed cases up to the ultimate state. Even at high plugging ratios (CRs), the internal soil core gradually increases its occupancy and intensity with settlement; however, high-contact-force struts beneath the ring remain active, and it is suggested that the ring-dominant load-transfer mechanism is generally preserved. In the post-peak plastic regime, the K_{-density share} remains around 60%, indicating that the internal core plays a secondary, confining role rather than becoming dominant. These findings suggest that the conventional plug/unplug classification based on PLR can be supplemented by a combined use of plugging ratio CR (a kinematic indicator) and the ring contribution index (K_{-density share}), potentially enabling a continuous interpretation of plugged and unplugged behaviors and contributing to the establishment of a design backbone for tip resistance evaluation. Calibration of design coefficients, scale regression, and mapping to practical indices such as N-values will be addressed in part II of this study. (Note: “Contribution” in this study refers to the HCF-based density contribution index K_{-density share}, not the reaction–force ratio.) Full article

Developing vehicle classification remains a fundamental challenge for intelligent traffic management in the Indian urban environment, where traffic exhibits high heterogeneity, density and unpredictability. In the Indian subcontinent, vehicle movement is erratic, congestion is high, and vehicle types vary significantly. Conventional global benchmarks often fail to capture these complexities, highlighting the need for a region-specific dataset. To address this gap, the present study introduced the EAHVSD dataset, a novel real-world image collection comprising 10,864 vehicle images from four distinct classes, acquired from roadside surveillance cameras at multiple viewpoints and under varying conditions. This dataset is designed to support the development of an automatic traffic counter and classifier (ATCC) system. A comprehensive evaluation of eleven state-of-the-art deep learning models, namely VGG16, VGG19, MobileNetV2, Xception, AlexNet, ResNet50, ResNet152, DenseNet121, DenseNet201, InceptionV3, and NASNetMobile, was carried out. Among these, the highest accuracy result has been achieved by VGG-16, MobileNetV2, InceptionV3, DenseNet-121, and DenseNet-201. We developed a stacking-based meta-ensemble framework to leverage the complementary strengths of its components and overcome their individual limitations. In this approach, a meta-learner classifier integrates the predictions of the best-performing models, thereby improving robustness, scalability, and real-world adaptability. The proposed ensemble model achieved an overall classification accuracy of 96.04%, a Cohen’s Kappa of 0.93, and an AUC of 0.99, consistently outperforming the individual models and existing baselines. A comparative analysis with prior studies further validates the efficacy and reliability of the stacking-based meta-ensemble method. These findings position the proposed frameworks as a robust and scalable solution for efficient vehicle classification under practical surveillance constraints, with potential applications in intelligent transportation systems and traffic management. Full article

Highway traffic incident management is a dynamic and time-dependent process, and rapidly and accurately predicting its complete sequence of actions and corresponding time schedule is essential for improving the refinement and intelligence of traffic control systems. To address the limitations of existing studies that predominantly focus on predicting the total duration while lacking fine-grained modeling of the response procedure, this study proposed a multi-task sequence-to-sequence (Seq2Seq) framework based on a BERT encoder and Transformer decoder to jointly predict incident response steps and their associated time offsets. The model first leveraged a pretrained BERT to encode the incident type and alarm description text, followed by an autoregressive Transformer decoder that generated a sequence of response actions. An action-aware temporal prediction module was incorporated to predict the time offset of each step in parallel, and an adaptive weighted multitask loss was adopted to optimize both action classification and time regression tasks. Experiments based on 4128 real records of highway incident handling in Yunnan Province demonstrated that the proposed model achieved improved performance in duration prediction, outperforming baseline approaches in RMSE (18.05), MAE (14.69), MAPE (37.13%), MedAE (13.23), and SMAPE (33.55%). In addition, the model attained BLEU-4 and ROUGE-L scores of 62.33% and 82.04% in procedure text generation, which confirmed its capability to effectively learn procedural logic and temporal patterns from textual data and offered an interpretable decision-support approach for traffic incident duration prediction. The findings of this study could further support intelligent traffic management systems by enhancing incident response planning, real-time control strategies, and resource allocation for expressway operations. Full article

Highway under-bridge areas represent a valuable land resource while simultaneously constituting a sensitive ecological zone. Achieving a balance between its redevelopment and ecological preservation constitutes a critical challenge within the field of ecological engineering. Although prior research has addressed urban elevated underbridge space, investigations specifically focusing on highway underpasses remain limited. The absence of standardized criteria for assessing the suitability of these spaces has resulted in uncoordinated and fragmented utilization. In response, this study proposes a comprehensive evaluation framework that integrates multi-criteria decision-making (MCDM) methodologies with optimized backpropagation neural networks, specifically genetic-algorithm-optimized BP (GA-BP) and particle-swarm-optimization-optimized BP (PSO-BP). The model incorporates indicators spanning physical characteristics, environmental factors, safety considerations, and accessibility metrics, and is applied to an empirical dataset comprising 134 highway bridge underpasses in Fuzhou City. The results indicate that (1) both the GA-BP and PSO-BP models enhance convergence speed and classification accuracy, with the GA-BP model demonstrating superior stability and suitability for classifying underpass suitability; (2) the principal determinants of suitability include traffic accessibility, safety parameters, and spatial relationships with adjacent water bodies and agricultural lands; and (3) underpasses characterized as hub-type, single-sided road-adjacent, and cross-connection configurations exhibit greater potential for redevelopment. This investigation represents the first integration of MCDM and optimized neural network techniques in this context, offering a robust tool to support the scientific planning and ecological conservation of underbridge space environments. Full article

Marine mammal vocalizations, such as those of the Northern Right Whale (NARW), are often masked by underwater acoustic noise. The acoustic vocalization signals are characterized by features such as their amplitude, timing, modulation, duration, and spectral content, which cannot be robustly captured by a single feature extraction method. These complex signals pose additional detection challenges beyond their low SNR. Consequently, this study proposes a novel low-SNR NARW classifier for passive acoustic monitoring (PAM). This approach employs an ideal binary mask with a bidirectional long short-term memory highway network (IBM-BHN) to effectively detect and classify NARW upcalls in challenging conditions. To enhance model performance, the reported literature limitations were addressed by employing a hybrid feature extraction method and leveraging the BiLSTM to capture and learn temporal dependencies. Furthermore, the integration of a highway network improves information flow, enabling near-real-time classification and superior model performance. Experimental results show the IBM-BHN method outperformed five considered state-of-the-art baseline models. Specifically, the IBM-BHN achieved an accuracy of 98%, surpassing ResNet (94%), CNN (85%), LSTM (83%), ANN (82%), and SVM (67%). These findings highlight the practical potential of IBM-BHN to support near-real-time monitoring and inform evidence-based, adaptive policy enforcement critical for NARW conservation. Full article

Horizontal road markings are crucial for safe driving and for the operation of advanced driver-assistance systems (ADAS), but they have been investigated less thoroughly than vertical signs or lane boundaries. This paper focuses on the detection and classification of horizontal road markings in digital images using modern deep learning techniques. Three YOLO models (YOLOv7, YOLOv8n, YOLOv9t) were trained and tested on a new dataset comprising 6250 images with 13,360 annotated horizontal road-marking objects across nine classes collected on Polish roads in sunny and cloudy conditions. The dataset covers nine classes of markings recorded on urban streets, rural roads and highways. It includes many difficult cases: small markings visible only from long distance or side entry roads, and markings with different levels of wear, from new and bright to faded, dirty or partially erased. YOLOv7 achieved Precision = 0.95, Recall = 0.91 and mAP@0.5 = 0.98. YOLOv8n and YOLOv9t obtained lower Recall but higher mAP@0.5:0.95 (>0.77). The results confirm that YOLO-based detectors can handle horizontal road markings under varied road conditions and degrees of visibility, and the dataset with baseline results may serve as a reference for further studies in intelligent transport systems. Full article

Traffic safety is a critical issue in highway operation management, where accurate identification of accident hotspots enables proactive risk prevention and facility optimization. Traditional methods relying on historical statistics often fail to capture macro-level environmental patterns and micro-level dynamic variations. To address this challenge, we propose a Dual-Branch Feature Adaptive Gated Fusion Network (DFAGF-Net) that integrates satellite remote sensing imagery with traffic flow time-series data. The framework consists of three components: the Global Contextual Aggregation Network (GCA-Net) for capturing macro spatial layouts from remote sensing imagery, a Sequential Gated Recurrent Unit Attention Network (Seq-GRUAttNet) for modeling dynamic traffic flow with temporal attention, and a Hybrid Feature Adaptive Module (HFA-Module) for adaptive cross-modal feature fusion. Experimental results demonstrate that the DFAGF-Net achieves superior performance in accident hotspot recognition. Specifically, GCA-Net achieves an accuracy of 84.59% on satellite imagery, while Seq-GRUAttNet achieves an accuracy of 82.51% on traffic flow data. With the incorporation of the HFA-Module, the overall performance is further improved, reaching an accuracy of 90.21% and an F1-score of 0.92, which is significantly better than traditional concatenation or additive fusion methods. Ablation studies confirm the effectiveness of each component, while comparisons with state-of-the-art models demonstrate superior classification accuracy and generalization. Furthermore, model interpretability analysis reveals that curved highway alignments, roadside greenery, and varying traffic conditions across time are major contributors to accident hotspot formation. By accurately locating high-risk segments, DFAGF-Net provides valuable decision support for proactive traffic safety management and targeted infrastructure optimization. Full article

The implementation of Euro 7 emission standards demands advanced real-time NO_x monitoring systems for diesel vehicles. Existing unified models inadequately capture phase-dependent emission mechanisms during cold-start, urban, and highway operation. This study develops a novel Mixture of Experts (MoE) architecture with data-driven phase classification based on aftertreatment thermal dynamics. Real-world data from a Euro 6d commercial vehicle (3247 PEMS samples) were classified into three phases, cold (<70 °C coolant temperature), hot low-speed (<90 km/h), and hot high-speed (≥90 km/h), validated through t-SNE analysis (silhouette coefficient = 0.73). The key innovation integrates thermal–kinematic domain knowledge with specialized XGBoost regressors, achieving R² = 0.918 and a 58% RMSE reduction versus unified models (RMSE = 1.825 mg/s). The framework operates within real-time constraints (1.5 ms inference latency), integrating autoencoder-based anomaly detection (95.2% sensitivity) and Model Predictive Control (11–13% NO_x reduction). This represents the first systematic phase-specific NO_x modeling framework with validated Euro 7 OBM compliance capability, providing both methodological advances in expert allocation strategies and practical solutions for next-generation emission control systems. Full article

Identifying the activation status of advanced driver assistance systems (ADAS) in real-world driving environments is crucial for safety, responsibility attribution, and accident forensics. Unlike prior studies that primarily rely on simulation-based settings or unsynchronized data, we collected a multimodal dataset comprising synchronized controller area network (CAN)-bus and smartphone-based inertial measurement unit (IMU) signals from drivers on consistent highway sections under both ADAS-enabled and manual modes. Using these data, we developed lightweight classification pipelines based on statistical and deep learning approaches to explore the feasibility of distinguishing ADAS operation. Our analyses revealed systematic behavioral differences between modes, particularly in speed regulation and steering stability, highlighting how ADAS reduces steering variability and stabilizes speed control. Although classification accuracy was moderate, this study provides one of the first data-driven demonstrations of ADAS status detection under naturalistic conditions. Beyond classification, the released dataset enables systematic behavioral analysis and offers a valuable resource for advancing research on driver monitoring, adaptive ADAS algorithms, and accident forensics. Full article

This paper proposes and evaluates two neural network-based approaches for object classification in automotive radar systems, comparing the performance impact of grid search and genetic algorithm (GA) hyperparameter optimization strategies. The task involves classifying cars, pedestrians, and cyclists using radar-derived features. The grid search–optimized model employs a compact architecture with two hidden layers and 10 neurons per layer, leveraging kinematic correlations and motion descriptors to achieve mean accuracies of 90.06% (validation) and 90.00% (test). In contrast, the GA-optimized model adopts a deeper architecture with nine hidden layers and 30 neurons per layer, integrating an expanded feature set that includes object dimensions, signal-to-noise ratio (SNR), radar cross-section (RCS), and Kalman filter–based motion descriptors, resulting in substantially higher performance at approximately 97.40% mean accuracy on both validation and test datasets. Principal Component Analysis (PCA) and SHapley Additive exPlanations (SHAP) highlight the enhanced discriminative power of the new set of features, while parallelized GA execution enables efficient exploration of a broader hyperparameter space. Although currently optimized for urban traffic scenarios, the proposed approach can be extended to highway and extra-urban environments through targeted dataset expansion and developing additional features that are less sensitive to object kinematics, thereby improving robustness across diverse motion patterns and operational contexts. Full article

Accurate cost estimation during the conceptual and feasibility phase of highway projects is essential for informed decision making by public contracting authorities. Existing approaches often rely on pavement cross-section descriptors, general project classifications, or quantity estimates of major work categories that are not reliably available at the early planning stage, while focusing on one or more key asset categories such as roadworks, bridges or tunnels. This study makes a novel contribution to both scientific literature and practice by proposing the first early-stage highway construction cost estimation model that explicitly incorporates roadworks, interchanges, tunnels and bridges, using only readily available or easily derived geometric characteristics. A comprehensive and practical approach was adopted by developing and comparing models across multiple machine learning (ML) methods, including Multilayer Perceptron-Artificial Neural Network (MLP-ANN), Radial Basis Function-Artificial Neural Network (RBF-ANN), Multiple Linear Regression (MLR), Random Forests (RF), Support Vector Regression (SVR), XGBoost Technique, and K-Nearest Neighbors (KNN). Results demonstrate that the MLR model based on six independent variables—mainline length, service road length, number of interchanges, total area of structures, tunnel length, and number of culverts—consistently outperformed more complex alternatives. The full MLR model, including its coefficients and standardized parameters, is provided, enabling direct replication and immediate use by contracting authorities, hence supporting more informed decisions on project funding and procurement. Full article

This article evaluates the potential for integrating planned water transfer infrastructure with existing transport corridors. A novel method was developed to assess the suitability of major road, highway, and railway corridors based on the availability of adjacent state-owned land for water pipeline construction. A five-category classification was introduced, reflecting the ratio of available land area (m²) to corridor segment length (m). This approach was first applied across the whole Czech Republic and then tested in detail on a regional pilot case study involving a planned water pipeline from the Nýrsko reservoir to the city of Plzeň with a total length of 72 km, supplying 250,000 inhabitants. Results showed a promising share of highly suitable corridors (23.6% nationwide; 20.6% in the case study). The method offers a tool for cost-effective planning of water transfer systems and can help to minimize land acquisition costs by utilizing state land along transport infrastructure. It is transferable and replicable for similar applications in other regions. Full article

The rapid evolution of unmanned aerial vehicle (UAV) technology and low-altitude economic development have propelled drone applications in critical infrastructure monitoring, particularly in intelligent transportation systems where real-time aerial image processing has emerged as a pressing requirement. We address the pivotal challenge of highway lane extraction from low-altitude UAV perspectives by applying a novel three-stage framework. This framework consists of (1) a deep learning-based semantic segmentation module that employs an enhanced STDC network with boundary-aware loss for precise detection of roads and lane markings; (2) an optimized polynomial fitting algorithm incorporating iterative classification and adaptive error thresholds to achieve robust lane marking consolidation; and (3) a global optimization module designed for context-aware lane generation. Our methodology demonstrates superior performance with 94.11% F1-score and 93.84% IoU, effectively bridging the technical gap in UAV-based lane extraction while establishing a reliable foundation for advanced traffic monitoring applications. Full article

With accelerating urbanization and the exponential growth in vehicle populations, high-precision traffic monitoring has become indispensable for intelligent transportation systems (ITSs). Conventional sensing technologies—including inductive loops, cameras, and radar—suffer from inherent limitations: restrictive spatial coverage, prohibitive installation costs, and vulnerability to adverse weather. Distributed Acoustic Sensing (DAS), leveraging Rayleigh backscattering to convert standard optical fibers into kilometer-scale, real-time vibration sensor networks, presents a transformative alternative. This paper provides a comprehensive review of the principles and system architecture of DAS for roadway traffic monitoring, with a focus on signal processing techniques, feature extraction methods, and recent advances in vehicle detection, classification, and speed/flow estimation. Special attention is given to the integration of deep learning approaches, which enhance noise suppression and feature recognition under complex, multi-lane traffic conditions. Real-world deployment cases on highways, urban roads, and bridges are analyzed to demonstrate DAS’s practical value. Finally, this paper delineates emerging research trends and technical hurdles, providing actionable insights for the scalable implementation of DAS-enhanced ITS infrastructures. Full article

To address the challenges of real-time monitoring via highway vehicle-mounted cameras—specifically, the difficulty in detecting distant pedestrians and vehicles in real time—this study proposes an enhanced object detection algorithm, YOLOv5s-F. Firstly, the FasterNet network structure is adopted to improve the model’s runtime speed. Secondly, the attention mechanism BRA, which is derived from the Transformer algorithm, and a 160 × 160 small-object detection layer are introduced to enhance small target detection performance. Thirdly, the improved upsampling operator CARAFE is incorporated to boost the localization and classification accuracy of small objects. Finally, Focal EIoU is employed as the localization loss function to accelerate model training convergence. Quantitative experiments on high-speed sequences show that Focal EIoU reduces bounding box jitter by 42.9% and improves tracking stability (consecutive frame overlap) by 11.4% compared to CIoU, while accelerating convergence by 17.6%. Results show that compared with the YOLOv5s baseline network, the proposed algorithm reduces computational complexity and parameter count by 10.1% and 24.6%, respectively, while increasing detection speed and accuracy by 15.4% and 2.1%. Transfer learning experiments on the VisDrone2019 and Highway-100k dataset demonstrate that the algorithm outperforms YOLOv5s in average precision across all target categories. On NVIDIA Jetson Xavier NX, YOLOv5s-F achieves 32 FPS after quantization, meeting the real-time requirements of in-vehicle monitoring. The YOLOv5s-F algorithm not only meets the real-time detection and accuracy requirements for small objects but also exhibits strong generalization capabilities. This study clarifies core challenges in highway small-target detection and achieves accuracy–speed improvements via three key innovations, with all experiments being reproducible. If any researchers need the code and dataset of this study, they can consult the author through email. Full article