Search Results (331)

This study, based on the maintenance engineering of regular national and provincial highways in Shanxi Province, aims to achieve refined maintenance of aging asphalt pavements under heavy-duty traffic conditions. Lightweight inspection equipment was used to perform frequent distress collection on the study sections, and for the first time, the EPCI (Economic Pavement Surface Condition Index, which can quickly improve the overall condition level of the pavement by identifying simple two-dimensional diseases such as transverse and longitudinal joints and tortoise net cracks, and low-cost maintenance measures can be carried out through the detection data, which does not include diseases such as subsidence, which are more complex and costly.) is proposed to assess pavement distress conditions. The study conducted six high-frequency data collections over one year on the designated road sections. EPCI evaluations were carried out on each lane in different driving directions, summarizing eight types of pavement distress, including alligator cracking, block cracking, longitudinal and transverse cracking, potholes, longitudinal and transverse crack repairs, and block repairs. The development trends of EPCI and the distribution of pavement distress were analyzed. By comparing EPCI data, it was found that EPCI values in the driving lane fluctuated more stably than those in the overtaking and slow lanes, which was attributed to differences in maintenance intensity. The overall PCI data of the pavement during the COVID-19 pandemic showed that reduced maintenance activities are more conducive to analyzing the pavement’s deterioration patterns. By examining the distressed area in each lane over time, it was observed that the slow lane had the highest distribution of alligator and block cracking, while longitudinal and transverse cracking were most prevalent in the overtaking and driving lanes. Further analysis of the relationship between distressed area and EPCI suggests that controlling the distressed area to around 500 square meters per kilometer per lane can maintain the EPCI score at approximately 80. This level of maintenance is considered the most economical while ensuring satisfactory pavement performance. Full article

The parameterization of vegetation indices (VIs) is crucial for sustainable irrigation and horticulture management, specifically for urban green infrastructure (GI) management. However, the constraints of roadside traffic, motor and industrially related pollution, and potential public vandalism compromise the efficacy of conventional in situ monitoring systems. The shortcomings of prevalent satellites, UAVs, and manual/automated sensor measurements and monitoring systems have already been reviewed. This research proposes a novel urban GI monitoring system based on an integration of gas exchange and various VIs obtained from computer vision algorithms applied to data acquired from three novel sources: (1) Integrated gas sensor data using nine different volatile organic compounds using an electronic nose (E-nose), designed on a PCB for stable performance under variable environmental conditions; (2) Plant growth parameters including effective leaf area index (LAIe), infrared index (Ig), canopy temperature depression (CTD) and tree water stress index (TWSI); (3) Meteorological data for all measurement campaigns based on wind velocity, air temperature, rainfall, air pressure, and air humidity conditions. To account for spatial and temporal data acquisition variability, the integrated cameras and the E-nose were mounted on a vehicle roof to acquire information from 172 Elm trees planted across the Royal Parade, Melbourne. Results showed strong correlations among air contaminants, ambient conditions, and plant growth status, which can be modelled and optimized for better smart irrigation and environmental monitoring based on real-time data. Full article

With the development of cities, traffic congestion has become a common problem, which seriously affects the efficiency of motorway transport. This study proposed an improved ML-YOLO video data extraction model based on You Only Look Once (YOLOv8n) combined with the Deep Simple Online and real-time tracking (DeepSORT) algorithm, to classify the obtained Traffic Performance Index (TPI) into different congestion levels by extracting traffic flow parameters in real-time and combining with the K-means clustering algorithm. The Long Short-Term Memory Dropout (LSTM-Dropout) model and the emergency lane opening model were used to implement the road congestion warning successfully. The practicality and stability of the model were also verified by calculating the relative error between the predicted traffic flow parameters and the extracted parameters through the LSTM time series model. According to the model results, emergency lanes are opened when the motorway traffic TPI exceeds 0.17 and closed when below 0.17. This study provided a reasonable theoretical basis for motorway traffic managers to decide whether or not to open the emergency lane, effectively relieved motorway road congestion, improved efficiency of road traffic, and had important practical value and significance in reality. Full article

Fiber reinforcement is a promising solution to several problems, however, the impact of fiber characteristics on the mechanical behavior and reinforcement mechanisms of asphalt mixtures remains unclear. Therefore, two distinct forms of basalt fiber—chopped basalt fiber (CBF) and flocculent basalt fiber (FBF)—were employed. A comprehensive experimental program was conducted, encompassing macroscopic and microscopic analyses through semi-circular bending tests integrated with digital image correlation, four-point bending fatigue tests, and dynamic modulus tests. Results indicate that both fiber types significantly improve crack resistance, with FBF demonstrating superior performance. Compared with the ordinary mixture, the flexibility index and fracture energy of the FBF-reinforced asphalt mixture increased by 59.7% and 30.6%, respectively. Fibers exert a crack-bridging effect, delaying the transition of the crack propagation stage by 1.25–2.21 s and reducing the crack propagation rate by 39.6–55.4%. Although fatigue life decreased with increasing strain levels, basalt fibers substantially enhanced fatigue resistance, with FBF-reinforced asphalt mixture achieving 20–40% higher N_f,50 values than CBF. Dynamic modulus tests revealed that fibers reduce modulus at low temperatures while increasing it at high temperatures, with more pronounced reinforcement effects observed in high-frequency regions. These findings underscore the importance of fiber morphology in optimizing asphalt mixture design and provide a theoretical basis for optimizing fiber-reinforced pavement materials to achieve long-term durability under complex environmental and traffic load conditions. Full article

The current global economic challenges and resource scarcity necessitate the development of cost-effective and sustainable pavement solutions. This study investigates the performance of asphalt mixtures modified with Low-Density Polyethylene (LDPE) and Styrene–Butadiene–Styrene (SBS) as binder modifiers, and Hydrated Lime (Ca(OH)₂) and Reclaimed Asphalt Pavement (RAP) as aggregate replacements. The research aims to optimize the combination of these materials for enhancing the durability, sustainability, and mechanical properties of asphalt mixtures under various climatic and traffic conditions. Asphalt mixtures were modified with 5% LDPE and 2–6% SBS (by bitumen weight), with 2% Hydrated Lime and 15% RAP added to the mix. The performance of these mixtures was evaluated using the Simple Performance Tester (SPT), focusing on rutting, cracking, and fatigue resistance at varying temperatures and loading frequencies. The NCHRP 09-29 Master Solver was employed to generate master curves for input into the AASHTOWare Mechanistic-Empirical Pavement Design Guide (MEPDG), allowing for an in-depth analysis of the modified mixes under different traffic and climatic conditions. Results indicated that the mix containing 5% LDPE, 2% SBS, 2% Hydrated Lime, and 15% RAP achieved the best performance, reducing rutting, fatigue cracking, and the International Roughness Index (IRI), and improving overall pavement durability. The combination of these modifiers showed enhanced moisture resistance, high-temperature rutting resistance, and improved dynamic modulus. Notably, the study revealed that in warm climates, thicker pavements with this optimal mix exhibited reduced permanent deformation and better fatigue resistance, while in cold climates, the inclusion of 2% SBS further improved the mix’s low-temperature performance. The findings suggest that the incorporation of LDPE, SBS, Hydrated Lime, and RAP offers a sustainable and cost-effective solution for improving the mechanical properties and lifespan of asphalt pavements. Full article

Particulate matter (PM) poses serious health risks, including respiratory and cardiovascular diseases, and is classified as a carcinogen by the World Health Organization and International Agency for Research on Cancer. Roadside air pollution, which is strongly affected by traffic emissions, is a major contributor to urban air quality deterioration. This study investigated the feasibility of establishing a low-cost, Internet of Things (IoT)-based, high-density monitoring network for roadside PM₁₀ and PM_2.5 to support safer and more sustainable road environments. We developed low-cost IoT sensing devices, deployed them at three urban roadside sites with different environmental conditions, and compared their performances with those of nearby public monitoring stations. One-minute resolution data were analyzed using Pearson correlation, cross-correlation, dynamic time warping, Z-score, and the roulette index. The IoT sensor data were strongly correlated with public station data, confirming its reliability as a complementary observation method. Notable site-specific patterns were sharp concentration increases with traffic at an intersection and distinct diurnal and weekly cycles at residential and rooftop sites. These findings demonstrate that low-cost IoT sensing can complement sparse public networks by providing microscale air quality information. This approach offers a practical foundation for smart city development and intelligent roadside environmental management. Full article

Detecting anomalies in network traffic is a central task in cybersecurity and digital infrastructure management. Traditional approaches rely on statistical models, rule-based systems, or machine learning techniques to identify deviations from expected patterns, but often face limitations in generalization across domains. This study proposes a cross-domain data enrichment framework that integrates behavioral embeddings with network traffic features through adversarial autoencoders. Each network traffic record is paired with the most similar behavioral profile embedding from user web activity data (Charles dataset) using cosine similarity, thereby providing contextual enrichment for anomaly detection. The proposed system comprises (i) behavioral profile clustering via autoencoder embeddings and (ii) cross-domain latent alignment through adversarial autoencoders, with a discriminator to enable feature fusion. A Deep Feedforward Neural Network trained on the enriched feature space achieves 97.17% accuracy, 96.95% precision, 97.34% recall, and 97.14% F1-score, with stable cross-validation performance (99.79% average accuracy across folds). Behavioral clustering quality is supported by a silhouette score of 0.86 and a Davies–Bouldin index of 0.57. To assess robustness and transferability, the framework was evaluated on the UNSW-NB15 and the CIC-IDS2017 datasets, where results confirmed consistent performance and reliability when compared to traffic-only baselines. This supports the feasibility of cross-domain alignment and shows that adversarial training enables stable feature integration without evidence of overfitting or memorization. Full article

The rapid evolution of 5G New Radio networks has introduced a wide range of services with diverse requirements, complicating their coexistence within the shared radio spectrum and posing challenges in traffic scheduling and resource allocation. This study aims to analyze and categorize the methods, approaches, and techniques proposed to ensure efficient joint and dynamic packet scheduling and resource allocation among heterogeneous services—namely eMBB, URLLC, and mMTC—in 5G and beyond, with a focus on Quality of Service and user satisfaction. This scoping review draws from publications indexed in IEEE Xplore and Scopus and synthesizes the most relevant evidence related to packet scheduling across heterogeneous services, highlighting key approaches, core performance metrics, and emerging trends. Following the PRISMA-ScR methodology, 48 out of an initial 140 articles were included for explicitly addressing coexistence, scheduling, and resource allocation. The findings reveal a research emphasis on eMBB and URLLC coexistence, while integration with mMTC remains underexplored. Moreover, the evidence suggests that hybrid and deep learning-based approaches are particularly promising for tackling coexistence and resource management challenges in future mobile networks. Full article

Bridges play an increasingly indispensable role in endorsing the economic and social development of societies by linking highways and facilitating the mobility of people and goods. Concurrently, they are susceptible to high traffic volumes and an intricate service environment over their lifespans, resulting in undergoing a progressive deterioration process. Hence, efficient measures of maintenance, repair, and rehabilitation planning are critical to boost the performance condition, safety, and structural integrity of bridges while evading less costly interventions. To this end, this research paper furnishes a mixed review method, comprising systematic literature and scientometric reviews, for the meticulous examination and analysis of the existing research work in relation with maintenance fund allocation models of bridges (BriMai_all). With that in mind, Scopus and Web of Science databases are harnessed collectively to retrieve peer-reviewed journal articles on the subject, culminating in 380 indexed journal articles over the study period (1990–2025). In this respect, VOSviewer and Bibliometrix R package are utilized to create a visualization network of the literature database, covering keyword co-occurrence analysis, country co-authorship analysis, institution co-authorship analysis, journal co-citation analysis, journal co-citation, core journal analysis, and temporal trends. Subsequently, a rigorous systematic literature review is rendered to synthesize the adopted tools and prominent trends of the relevant state of the art. Particularly, the conducted multi-dimensional review examines the six dominant methodical paradigms of bridge maintenance management: (1) multi-criteria decision making, (2) life cycle assessment, (3) digital twins, (4) inspection planning, (5) artificial intelligence, and (6) optimization. It can be argued that this research paper could assist asset managers with a practical guide and a protocol to plan maintenance expenditures and implement sustainable practices for bridges under deterioration. Full article

Communications in intelligent transportation systems (ITS) face explosive data growth from applications such as autonomous driving, remote diagnostics, and real-time monitoring, imposing severe challenges on limited spectrum, bandwidth, and latency. Reliable semantic image reconstruction under noisy channel conditions is critical for ITS perception tasks, since noise directly impacts the recognition of both static infrastructure and dynamic obstacles. Unlike traditional approaches that aim to transmit all image data with equal fidelity, effective ITS communication requires prioritizing task-relevant dynamic elements such as vehicles and pedestrians while filtering out largely static background features such as buildings, road signs, and vegetation. To address this, we propose an Offline Knowledge Base and Attention-Driven Semantic Communication (OKBASC) framework for image-based applications in ITS scenarios. The proposed framework performs offline semantic segmentation to build a compact knowledge base of semantic masks, focusing on dynamic task-relevant regions such as vehicles, pedestrians, and traffic signals. At runtime, precomputed masks are adaptively fused with input images via sparse attention to generate semantic-aware representations that selectively preserve essential information while suppressing redundant background. Moreover, we introduce a further Bi-Level Routing Attention (BRA) module that hierarchically refines semantic features through global channel selection and local spatial attention, resulting in improved discriminability and compression efficiency. Experiments on the VOC2012 and nuPlan datasets under varying SNR levels show that OKBASC achieves higher semantic reconstruction quality than baseline methods, both quantitatively via the Structural Similarity Index Metric (SSIM) and qualitatively via visual comparisons. These results highlight the value of OKBASC as a communication-layer enabler that provides reliable perceptual inputs for downstream ITS applications, including cooperative perception, real-time traffic safety, and incident detection. Full article

This study evaluated the benefits of pavement preservation treatments across two climatic zones using data from the National Center for Asphalt Technology (NCAT) Pavement Preservation Group Study. Longitudinal data analysis was conducted to quantify pavement performance over time. Results indicate that in the freeze zone, treatments significantly improved pavement smoothness, as evidenced by reductions in the progression of the International Roughness Index (IRI), whereas similar trends were not observed in the no-freeze region, highlighting the need for further research to quantify the benefits in these zones. Life cycle cost analysis (LCCA) showed that selected preservation treatments reduced user costs by 54–57% due to lower excess fuel consumption, particularly in high-traffic corridors. These treatments also contributed to reductions in greenhouse gas (GHG) emissions by decreasing fuel use. Despite these findings, comprehensive, high-quality data are needed to fully evaluate the economic and environmental benefits of preservation treatments at the project level and to improve decision-making in pavement management strategies. Full article

As traffic load continuously rises and climatic conditions increasingly vary, the performance of conventional base asphalt can no longer satisfy the needs of modern road engineering in low-temperature cracking resistance, high-temperature stability, and long-term durability. Therefore, the development of novel and efficient asphalt modifiers holds significant engineering value and practical importance. In this study, modified asphalt was prepared using varying dosages of ZM modifier (direct-injection asphalt mixture modified polymer additive). A series of experiments was executed to assess its influence on asphalt properties. First, fundamental property tests were implemented to determine the regulating effect of the ZM modifier on basic physical performances, like the softening point and penetration of the base asphalt. Penetration tests at different temperatures were performed to calculate the penetration index, thereby assessing the material’s temperature sensitivity. Subsequently, focusing on temperature as a key factor, tests on temperature sweep, and multiple stress creep recovery (MSCR) were implemented to delve into the deformation resistance and creep recovery behavior of the modified asphalt under high-temperature conditions. In addition, bending beam rheometer (BBR) experiments were introduced to attain stiffness modulus and creep rate indices, which were applied to appraise the low-temperature rheological performance. Aside from Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR) was utilized to explore the mechanism by which the ZM modifier influences the asphalt’s functional group composition and microstructure. Our findings reveal that the ZM modifier significantly increases the asphalt’s softening point and penetration index, reduces penetration and temperature sensitivity, and enhances high-temperature stability. Under high-temperature conditions, the ZM modifier adjusts the viscoelastic balance of asphalt, hence enhancing its resistance to flow deformation and its capacity for creep recovery. In low-temperature environments, the modifier increases the stiffness modulus of asphalt and improves its crack resistance. FTIR analyses reveal that the ZM modifier does not introduce new functional groups, indicating a physical modification process. However, by enhancing the cross-linked structure and increasing the hydrocarbon content within the asphalt, it strengthens the adhesion between the asphalt and aggregates. Overall, the asphalt’s performance improvement positively relates to the dosage of the ZM modifier, providing both theoretical basis and experimental support for its application in road engineering. Full article

Road traffic accidents remain a major global public health concern, where complex urban driving environments significantly elevate drivers’ visual load and accident risks. Unlike existing research that adopts a macro perspective by considering multiple factors such as the driver, vehicle, and road, this study focuses on the driver’s visual load, a key safety factor, and its direct source—the driver’s visual environment. We have developed an interpretable framework combining computer vision and machine learning to quantify how road scene features influence oculomotor behavior and scene-induced visual load, establishing a complete and interpretable link between scene features, eye movement behavior, and visual load. Using the DR(eye)VE dataset, visual attention demand is established through occlusion experiments and confirmed to correlate with eye-tracking metrics. K-means clustering is applied to classify visual load levels based on discriminative oculomotor features, while semantic segmentation extracts quantifiable road scene features such as the Green Visibility Index, Sky Visibility Index and Street Canyon Enclosure. Among multiple machine learning models (Random Forest, Ada-Boost, XGBoost, and SVM), XGBoost demonstrates optimal performance in visual load detection. SHAP analysis reveals critical thresholds: the probability of high visual load increases when pole density exceeds 0.08%, signage surpasses 0.55%, or buildings account for more than 14%; while blink duration/rate decrease when street enclosure exceeds 38% or road congestion goes beyond 25%, indicating elevated visual load. The proposed framework provides actionable insights for urban design and driver assistance systems, advancing traffic safety through data-driven optimization of road environments. Full article

High-definition (HD) maps are essential for safe and reliable autonomous driving, but their growing size and the need for real-time updates pose significant challenges for in-vehicle storage and communication efficiency. This study proposes a lightweight and scalable HD map provisioning system based on Geohash spatial indexing and Smart Roadside Units (Smart RSUs). The system divides HD map data into Geohash-based spatial blocks and enables vehicles to request only the map segments corresponding to their current location, reducing storage burden and communication load. To validate the system’s effectiveness, we constructed a simulation environment where multiple vehicle clients simultaneously request map data from a Smart RSU. Experimental results showed that the proposed Geohash-based approach achieved an average response time (RTT) of 1244.82 ms—approximately 296.3% faster than the conventional GPS-based spatial query method—and improved database query performance by 1072.6%. Additionally, we demonstrate the system’s scalability by adjusting Geohash levels according to road density, using finer blocks in urban areas and coarser blocks in rural areas. The hierarchical nature of Geohash also enables consistent integration of blocks with different resolutions. These results confirm that the proposed method provides an efficient and real-time HD map delivery framework suitable for dynamic and dense traffic environments. Full article

The anti-slip performance of road surfaces directly affects traffic safety, yet existing evaluation methods based on texture features often suffer from limited interpretability and low accuracy. To overcome these limitations, a portable 3D laser surface analyzer was used to acquire road texture data, while a dynamic friction coefficient tester provided friction measurements. A multi-view fractal dimension index was developed to comprehensively describe the complexity of texture across spatial, cross-sectional, and depth dimensions. Combined with road surface temperature, this index was integrated into an XGBoost-based prediction model to evaluate friction at driving speeds of 10 km/h and 70 km/h. Comparative analysis with linear regression, decision tree, support vector machine, random forest, and backpropagation (BP) neural network models confirmed the superior predictive performance of the proposed approach. The model achieved backpropagation (R²) values of 0.80 and 0.82, with root mean square errors (RMSEs) of 0.05 and 0.04, respectively. Feature importance analysis indicated that fractal characteristics from multiple texture perspectives, together with temperature, significantly influence anti-slip performance. The results demonstrate the feasibility of using non-contact texture-based methods to replace traditional contact-based friction testing. Compared with traditional statistical indices and alternative machine learning algorithms, the proposed model achieved improvements in R² (up to 0.82) and reduced RMSE (as low as 0.04). This study provides a robust indicator system and predictive model to advance road surface safety assessment technologies. Full article