Search Results (1,906)

Accurate and timely identification of slippery road surfaces is essential for ensuring driving safety and operational efficiency on highways. However, blurred vehicle-background boundaries, uneven illumination, and water splashing caused by passing vehicles make existing image-based recognition methods prone to low accuracy. To address these challenges, this paper proposes an improved SegFormer-based framework with two task-specific innovations: (1) a novel Guided Multi-scale Fusion (GMF) module for task-guided multi-scale feature integration, designed to incorporate auxiliary information such as vehicle type, relative speed, and splash regions, enabling the network to focus on slipperiness-relevant road areas while suppressing background interference; and (2) an improved Boundary Attention Awareness (BAA) module with directional Sobel-based boundary initialization, which provides explicit geometric priors to preserve fine boundary details and reduce ambiguity in slippery regions with irregular or weak edges. A multi-scale input and enhancement strategy is further adopted, along with a weighted combination of cross-entropy loss and Dice loss to mitigate class imbalance. Experimental results on our self-constructed Guangzhou Beierhuan Expressway dataset achieve an mIoU of 95.80%, accuracy of 97.84%, and F1-score of 97.86%. To verify cross-domain generalization, we further evaluate the model on two additional benchmarks: it achieves an mIoU of 93.51% on the synthetic SYN-UDTIRI dataset, and attains an mIoU of 95.80% with an AmIoU of 76.20% on the public Cityscapes dataset, achieving competitive performance against several mainstream architectures. The proposed method offers considerable application potential for highway safety warning systems. Full article

Accurately identifying the functional characteristics of urban rail transit stations and classifying them accordingly helps uncover passenger flow patterns and optimize resource allocation, thereby enhancing the coordination efficiency of multimodal urban transportation systems. Existing studies on the delineation of station influence areas often exhibit overlapping zones, leading to insufficient characterization of regional heterogeneity. Additionally, classification methods predominantly rely on static single indicators and lack integration of multi-scale features. To address these limitations, this paper proposes a non-overlapping zoning algorithm for precisely defining station influence areas. By incorporating multidimensional indicators—including dynamic passenger flows, resident attributes, connection characteristics, and spatial distribution—a fine-grained station classification model is developed using an enhanced Partitioning Around Medoids (PAM) algorithm. Building on the classification outcomes, a dual-scenario framework (weekday vs. weekend) is established, and Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models are applied to analyze the spatiotemporal patterns of passenger flows. A case study of Beijing rail transit stations demonstrates that the enhanced PAM algorithm significantly improves clustering performance. Four distinct station types are identified on weekdays: Peripheral Basic-Service Type, Core Commuting-Aggregation Type, Exurban Residential-Transit-Dependent Type, and Multifunctional-Complex Type. On weekends, stations are classified into three categories: Peripheral Living-Service Type, Core Leisure-Vitality Type, and Central Mixed-Use Type. Furthermore, the driving factors of passenger flows exhibit notable spatiotemporal heterogeneity: on weekdays, commuting demand dominates, with jobs–housing ratio, educational attainment ratio, and road network density serving as core positive factors; on weekends, leisure demand becomes prominent, showing strong synergistic effects among jobs–housing ratio, Points of Interest (POI) density, and road network connectivity. The research findings provide theoretical support for the functional classification and refined management of rail transit stations. Full article

Rapid global urbanization is increasing the need for accurate, large-scale traffic monitoring to support sustainable transportation and city governance. Satellite video remote sensing offers a unique way to continuously observe urban road networks over large areas. It provides high-resolution spatio-temporal data that is essential for traffic flow analysis, infrastructure assessment, and dynamic urban planning. Moving vehicle detection in satellite video sequences is a basic task that turns raw imagery into useful traffic-state information, supporting these applications. Despite the advantages of satellite video data, detecting moving vehicles in practice remains a tough problem. Objects are extremely small and lack clear appearance details, while low local contrast makes them hard to separate from complex backgrounds. Satellite platform motion also introduces background misalignment and intensity fluctuations, resulting in missed detections and false alarms that hurt monitoring reliability. Furthermore, current methods do not fully exploit temporal motion cues or transform-domain priors, creating a performance bottleneck that restricts their practical use. To solve these problems, this paper proposes a Channel-Attentive Spatio-Temporal-Frequency Alignment (CASTFA) framework to effectively use and combine multi-dimensional features for moving vehicle detection in satellite videos, with the goal of providing high-quality traffic monitoring data to help smart city planning. Specifically, a State Space-Guided Temporal Compression (SSGTC) module first collects information along the time dimension with linear computational complexity, greatly reducing overhead while keeping motion cues that are critical for traffic-state estimation. The compressed temporal features are then processed with a multi-scale Haar wavelet transform to get hierarchical time-frequency representations that capture subtle motion dynamics across different frequency bands. At the same time, a pre-trained backbone network extracts multi-scale spatial features. To allow these different domains to work together, a Cross-Domain Feature Alignment (CDFA) mechanism aligns and combines spatial and time-frequency features through channel-attentive operations. Experimental results on the publicly available satellite video moving vehicle detection dataset show that the proposed CASTFA method consistently outperforms existing approaches, with better precision, recall, and F1-scores across diverse urban scenarios. These results show that CASTFA can provide reliable moving vehicle detection performance under difficult real-world conditions, supporting accurate traffic-flow monitoring and providing valuable geospatial intelligence for smart urban planning, transportation management, and sustainable city development. Full article

Accurate pavement crack segmentation is critical for road infrastructure assessment, yet it remains challenging due to complex background noise and highly variable crack topologies. While emerging visual Mamba models excel in long-range contextual modeling, their inherent 1D sequence flattening process compromises local 2D spatial continuity. To address this limitation, we propose GeoMamba, a geometric-prior-infused multi-scale deformable visual Mamba network for road crack semantic segmentation. First, we design a Multi-Scale Deformable Visual State Space (MDVSS) module to extract multi-scale contextual features and dynamically adapt to tortuous crack paths through a novel deformable scanning mechanism. Second, a Geometric-Topology Prior Injection (GTPI) module is introduced to mitigate serialization artifacts. By leveraging deterministic, parameter-free analytical operators (i.e., Sobel and Laplace), the GTPI module explicitly extracts and adaptively infuses multi-scale structural priors into the Mamba decoder via gated skip connections, intrinsically reconstructing crack typologies while suppressing pseudo-structural noise. Comprehensive experiments on DeepCrack and Concrete3K datasets demonstrate that GeoMamba outperforms nine state-of-the-art methods. Specifically, it achieves peak performance on the DeepCrack dataset with an mIoU of 83.79% and an F1 score of 89.27%, demonstrating exceptional semantic segmentation performance, superior topological continuity, and robust generalization across diverse pavement materials. Full article

Driven by the goals of carbon peaking and carbon neutrality, as well as the increasing demand for green construction materials, traditional petroleum-based asphalt can no longer fully meet the requirements of long-life and low-carbon road construction due to its strong resource dependence, susceptibility to aging, and difficulty in balancing high- and low-temperature performance. Bio-based polyurethane-modified asphalt (Bio-PUMA) uses renewable or waste biomass to construct high-performance polyurethane (PU) networks, providing a new way to improve pavement performance, reduce carbon emissions, and support the sustainable development of road materials. This paper reviews the molecular structural characteristics of bio-based precursors, including vegetable oil, rosin, and lignin, and summarizes their effects on PU network formation, asphalt microphase morphology, and pavement performance. Existing studies show that the functionality, molecular backbone, hydroxyl value, and soft-to-hard segment ratio of bio-based polyols govern the crosslinking density, phase continuity, and asphalt compatibility of polyurethane networks, thereby influencing rutting resistance, cracking resistance, aging resistance, interfacial adhesion, and mixture performance. Current challenges include unstable biomass feedstocks, difficulty in balancing low-temperature toughness and high-temperature strength, limited long-term service data, and incomplete life-cycle assessment. Future studies should focus on precursor standardization, precise molecular design, multiscale performance evaluation, and engineering validation to promote the application of Bio-PUMA in long-life, low-carbon, and large-scale road infrastructure. Full article

Vehicle energy-regenerative suspension can convert part of the vibration energy induced by road excitation into electrical energy. However, there are coupled performance conflicts among energy recovery, ride comfort, and suspension safety, and a fixed-weight LQR controller finds it difficult to maintain a reasonable performance compromise under different road conditions. To address this problem, this paper proposes an AWG-NN-LQR control method based on an Adaptive Weight Generation neural network. First, a quarter-car energy-regenerative suspension model, an electromagnetic actuator model, and a random road model are established, and the vertical vehicle responses and energy-regeneration characteristics under different road classes are analyzed. Second, vehicle speed, road roughness coefficient, and statistical features of vehicle responses are used as inputs. LQR weight labels are generated through offline closed-loop simulation and candidate-weight search, and the AWG-NN is trained to learn the nonlinear mapping relationship between road conditions and weight parameters. Finally, closed-loop comparative validation is conducted for the passive suspension, fixed-weight LQR, and AWG-NN-LQR under a typical class-C road condition. The results show that, compared with the fixed-weight LQR, AWG-NN-LQR reduces the RMS of body acceleration from $1.7041{\mathrm{m}/\mathrm{s}}^2$ to $1.6527{\mathrm{m}/\mathrm{s}}^2$, and reduces the RMS of suspension deflection from $0.00863\mathrm{m}$ to $0.00844\mathrm{m}$, while achieving an average regenerated power of $9.41\mathrm{W}$. The proposed method can improve the objective-bias problem of the fixed-weight LQR under a typical operating condition while maintaining a certain energy-regeneration capability, providing a feasible approach for multi-objective adaptive control of energy-regenerative suspension. Full article

Cross-border transportation networks show considerable complexity and vulnerability under the influence of geopolitical risks. To identify critical nodes and simulate the propagation paths of failure risk within cross-border transportation networks, this study develops a multilayer cross-border transportation network model that integrates four transport modes: waterway, road, rail, and air transport. First, composite edge weights are calculated using the Criteria Importance Through Intercriteria Correlation (CRITIC) method based on inter-node transport distance, transport time, and geopolitical risk (GPR) exposure score. These weights capture the joint effects of transport efficiency and risk exposure across different transport corridors. Second, an Improved Weighted K-shell (IWKS) model is proposed for critical-node identification by integrating three network topological indicators: degree centrality, weighted degree, and closeness centrality. Third, an edge-risk-based Susceptible–Infected–Recovered (SIR) propagation model is introduced. Monte Carlo simulations are then used to estimate the simulation-based propagation influence of each node. Finally, the identification performance of the IWKS model is compared with that of conventional centrality indicators. The IWKS scores are also significantly correlated with the simulation-based propagation influence under three types of correlation tests. The findings support the applicability of the proposed IWKS model in critical-node identification and system vulnerability research of multilayer cross-border integrated transportation networks. Full article

Road surface condition monitoring is critical for ensuring safe and efficient transportation networks. This study proposes and evaluates a framework that compares imagery-, Light Detection and Ranging (LiDAR), and accelerometer-based approaches for pavement anomaly detection. The analysis first focused on a 5-mile urban roadway segment, in which all three sensing modalities were evaluated under identical survey conditions using manually interpreted reference anomalies to compare detection accuracy, severity classification, and processing efficiency. The imagery-based Convolutional Transformer-based Crack Segmentation (CT-CrackSeg) model achieved a precision, recall, and F1-score of 88.5%, 88.5%, and 88.5%, respectively, but remained sensitive to environmental factors such as shadows, curbs, roadside features, and pavement texture variations. The LiDAR-based method achieved an F1-score of 93.0%, while the accelerometer-based Isolation Forest and Adaptive Threshold methods achieved F1-scores of 95.2% and 97.2%, respectively. These results indicate strong detection performance under the evaluated validation conditions; however, the reported precision values should be interpreted as dataset-specific rather than universal performance levels. Given the accelerometer-based approach’s strong detection performance, minimal processing time, and low deployment cost, it was further applied across a 36-mile roadway network to evaluate its scalability for network-level monitoring. Across the full route, the spatial agreement among accelerometer systems exceeded 0.91, while the agreement between the two detection methods exceeded 0.96, with 962–996 surface defects detected depending on the sensor and method. Integrating the anomaly detection results into a Potree-based web portal enabled interactive validation with geotagged imagery and point clouds, improving interpretability and diagnostic insight. Overall, the findings highlight that accelerometer-based monitoring, even with consumer-grade sensors, provides a practical, scalable, and low-cost solution for pavement evaluation, while LiDAR and imagery serve as complementary tools for detailed verification and characterization. Full article

Historical and cultural heritage parks are important spaces for heritage conservation, cultural transmission, and public recreation. However, conventional landscape perception research mainly relies on questionnaires and interviews, making it difficult to capture tourists’ visual preferences at scale. This study proposes a dual-task attention-enhanced ResNet framework based on social media user-generated content (UGC) images to investigate tourists’ landscape perception preferences in historical and cultural heritage parks. Using Yellow Crane Tower Park, Guqintai, and Guishan Scenic Area in Wuhan, China, as case studies, 6221 images were collected from Ctrip, Xiaohongshu, Weibo, and field surveys. The framework jointly performs landscape element detection and aesthetic attribute classification through shared feature representation and attention mechanisms. The proposed model achieved a composite Macro-F1 score of 0.7641, demonstrating robust classification performance. The results show that Buildings and Structures exhibited the highest average prediction probability (0.5824), while Spatial Legibility was the dominant aesthetic attribute (0.5322), indicating a perception pattern characterized by cultural-symbol prominence and enhanced spatial cognition. Vegetation and road networks were positively associated with spatial mystery, whereas excessive visual complexity reduced spatial legibility. These findings demonstrate the value of combining deep learning with social media image analytics for cultural landscape perception research and provide practical insights for landscape planning, heritage conservation, and tourism management. Full article

Rapid urban population growth has intensified traffic congestion in smart cities. This has resulted in longer travel times, higher fuel consumption, increased environmental pollution, greater operational costs, and slower emergency response services. Existing traffic congestion prediction models primarily rely on traffic-flow and temporal features; the effects of road accidents and peak-hour conditions are not adequately addressed. This limitation is particularly significant in smart cities where both recurrent congestion (peak-hour demand) and non-recurrent congestion (road accidents) influence traffic conditions they have a significant impact on the performance of the road network. This study introduces a novel Historical Accident-Aware Peak-Hour GAN-GRU (APG-GRU) framework. The proposed framework employs a data processing pipeline integrating traffic data with historical accident-related features to predict traffic congestion using these features. Extensive experiments are conducted on a novel integrated dataset consist on Automatic Number Plate Recognition (ANPR) traffic data and ANPR traffic observations with historical accident features. The results demonstrate that the APG-GRU framework achieved superior performance on the integrated features dataset, attaining an accuracy of 97.50%, a congested precision of 91.86%, a congested recall of 97.31%, and a congested F1-score of 94.51%, outperforming both the ANPR traffic-only dataset and all baseline models. The APG-GRU framework significantly outperforms a suite of benchmark models, including XGBoost, Long Short-Term Memory (LSTM), and Random Forest as baselines, which achieved accuracies between 84% and 95.5% with correspondingly lower precision, recall, and F1-scores. External validation using a traffic dataset collected from Lahore, Pakistan, further demonstrated the robustness and generalizability of the proposed APG-GRU framework. A web-based interface developed for the APG-GRU framework to visualize accident hotspots and route-level traffic conditions. Routes with smooth traffic flow are highlighted in green, whereas congested routes are highlighted in red, demonstrating the practical applicability of the proposed framework for smart city traffic management systems. Full article

Real-time road accident prediction under dynamic climatic conditions remains a critical challenge for intelligent transportation systems, especially in peripheral and rural regions with limited communication infrastructure. This study proposes and evaluates a comprehensive five-layer cyber–physical architecture based on Federated Edge Intelligence to enable climate-aware spatiotemporal road accident prediction across the 24 provinces of Ecuador. The framework integrates low-power IoT sensing nodes equipped with TinyML capabilities (ESP32-S3), long-range LoRaWAN (Long-Range Wide-Area Network) communication networks, containerized edge–cloud orchestration via OpenNebula and K3s, a decentralized Federated Learning ecosystem using the FedAvg algorithm, and a geospatial decision intelligence backend. Leveraging a nationwide multi-source dataset spanning the 2014–2025 period with 27,620 processed records, the architecture successfully handles highly skewed historical accident profiles optimized through a Box–Cox transformation. Empirical results demonstrate that the centralized Stacking ensemble achieves the highest overall baseline performance ($R^2=0.2460,\mathrm{MAE}=0.4748$) in the Box–Cox transformed space. In the decentralized environment, the federated Gradient Boosting implementation establishes a resilient and viable accuracy trade-off ($14.51\%$ increase in MAE) while strictly maintaining localized data sovereignty and compliance with personal data protection legislation. Operationally, the edge nodes achieve a localized inference latency of only $78\mathrm{ms}$, well below the critical $200\mathrm{ms}$ safety threshold, while the global aggregation engine exhibits rapid convergence within just three communication rounds. This cyber–physical ecosystem proves that combining localized TinyML inference with federated aggregation provides a scalable, low-latency, and privacy-preserving foundation for next-generation climate-aware road safety infrastructures in connectivity-constrained environments. Full article

The growing generation of end-of-life tires and the structural deficiencies of tertiary road networks in developing regions represent two interconnected challenges that call for integrated engineering solutions. Although recycled tire geocells have been explored as a reinforcement alternative for unpaved roads, their mechanical performance has not been systematically compared against conventional high-density polyethylene (HDPE) geocells under controlled cyclic loading, nor has the material valorization potential been quantified in terms of waste diversion capacity. This study systematically evaluates and compares the deformation response, stress distribution, and aggregate degradation of both systems through large-scale laboratory testing. An instrumented steel chamber was used to apply up to 100,000 load cycles at 600 kPa contact pressure, across two cell heights (150 mm and 200 mm) and two granular infill qualities. The 200 mm recycled tire geocell reduced permanent surface deformation by up to 84% relative to the unreinforced condition, and Traffic Benefit Ratio values reached up to 1.6 times those recorded for HDPE geocells under lower-quality infill. The viscoelastic response of rubber promoted energy dissipation and limited aggregate particle breakage. Confinement performance was fully preserved after surface reconditioning, confirming the durability of the reinforcement system under maintenance cycles. The fabrication process requires no chemical transformation, valorizing approximately 16 end-of-life tires per square meter—equivalent to 56,000 tires per kilometer of reinforced road. These findings support the large-scale valorization of end-of-life tires as functional geotechnical materials, offering a circular economy pathway for solid waste diversion in regions where both road infrastructure deficits and tire disposal challenges coexist. Full article

In dynamic Vehicular Edge Computing (VEC) environments, rapidly changing vehicle mobility and traffic lead to fluctuating edge resource demands, challenging task offloading and scheduling. Accurate prediction of future traffic flow and traffic-state-derived workload representations is thus crucial for proactive resource management. To address the limitations of existing methods in short-term dynamic characterization, complex spatial interaction modeling, and heterogeneous target prediction, this paper proposes a Spatio-Temporal Graph Attention Network (TA-STGAT). The proposed model constructs multi-dimensional RSU-level state sequences from simulated trajectories generated on a real-world road network and separately forecasts vehicle flow within RSU coverage areas and the associated traffic-state-derived workload representation under a unified spatio-temporal modeling framework. By integrating gated dilated temporal convolutions with a topology-constrained multi-head graph attention mechanism, the model captures multi-scale temporal dependencies and nonlinear spatial correlations. Experimental results show that, compared with the best-performing baseline in terms of RMSE for each forecasting task, TA-STGAT reduces RMSE by 10.89% and 11.29% in workload-representation prediction and traffic flow prediction, respectively, demonstrating its effectiveness for short-term edge-state forecasting. Full article

Microscopic traffic simulation represents one of the most popular tools for analysing and comparing traffic performance in different scenarios of urban mobility. The reproduction of real-world traffic dynamics is its primary requirement for providing time-dependent estimates. This study presents a process to build a microscopic traffic model for an urban area of Athens, developed using high-resolution vehicle trajectories obtained from the pNEUMA dataset. Based on drone-recorded trajectories, a simulation scenario was built, combining a realistic road network model, including traffic light regulation, an estimated traffic demand, and a set of parameters to replicate the observed vehicle behaviour. The modelling process relies on an iterative comparison between simulated outputs and observed vehicle-level trajectory data. The proposed approach evaluates travel time distributions and helps develop an improved model, enhancing its ability to replicate the vehicle’s behaviour. The final calibrated configuration reduced the Wasserstein distance by approximately 55.8% compared with the default SUMO configuration. The results also show that the calibration of microscopic behavioural parameters can substantially affect secondary simulation outputs, including emission estimates relevant for sustainability-oriented traffic analyses. Full article

In the field of road damage detection, the accuracy of existing methods still requires further improvement, particularly for elongated cracks, which are crucial for ensuring driving safety and effective road maintenance. To address this limitation, a novel road damage detection algorithm is proposed based on direction awareness and feature equalization. Specifically, a Direction-aware Strip Convolution (DSC) module is constructed to effectively capture the geometric characteristics of elongated cracks and maintain computational efficiency, by integrating asymmetric strip convolution and depthwise separable convolution respectively. In addition, a Multi-level Feature Equalization (MFE) module is designed to address the complex morphology and significant scale variations of road damage during multi-level feature fusion. Specifically, a set of learnable spatial weighting parameters is introduced in this module, whose weighting coefficients are optimized across different network layers and adaptively generated, thereby modulating the contributions of multi-level features and promoting a more balanced multi-level feature representation. Experimental results on the RDD2022-based experimental dataset demonstrate that the proposed method improves mAP@50 by 5.8 percentage points and recall by 5.9 percentage points, while achieving a processing speed of 122 FPS. Notably, the proposed method improves the detection performance of elongated cracks and achieves relatively balanced performance gains across different road damage categories, compared with the baseline model. Full article