The disorderly emission and poor treatment effect of asphalt mixture fume in asphalt mixing stations have caused huge harm to the natural environment and human health. Based on the gravimetric method, self-designed and assembled fume generation-enrichment device that was suitable for the mixing state of asphalt mixture in the plant, and conducts research on the fume release rules during asphalt mixture mixing. Four kinds of petroleum asphalt were selected to consider the conditions of asphalt fume generation in the field mixing station. The mixing temperature, aggregate specific surface area and mixing frequency were determined as the key factors affecting the release of fume. The exposed area of the aggregate was calculated by the measured modified specific surface area calculation method, and the influence of each key factor on the smoke release of the plant-mixed asphalt mixture was analyzed. The results show that the self-designed and assembled fume generation-enrichment device has good stability and can ensure the accuracy of the experimental results. The magnitude of the light components in the fume under different mixing conditions is much lower than that of the heavy components, and the fume release after the base asphalt is modified by SBS is reduced. Mixing temperature is the most critical factor that determines the amount of fume release. The amount of fume release shows an obvious growth trend with the increase in temperature. Fume production has strong temperature sensitivity, and release varies significantly under different mixing temperatures. 160 ℃ and 170 ℃ are the jumping nodes of fume release. The amount of fume produced by the asphalt mixture is directly proportional to the specific surface area of the aggregate. The specific surface area of the aggregate directly affects the amount of fume released by determining the exposed area of the asphalt. The amount of fume release is positively related to the mixing frequency. Compared with the heavy components of the fume, the release amount of the light components of the fume is less affected by the mixing frequency. The research results can provide theoretical and technical support for the release and disposal of fume in mixing plants.

Pavement performance prediction is the basis for maintenance decisions. Predicting future pavement conditions accurately and efficiently helps determine the optimal maintenance time, select appropriate measures, and allocate rehabilitation funds effectively. However, limited to the instability and variability in pavement condition data collection, deterministic models are not always reliable for all pavement situations. On the other hand, probabilistic-based models are influenced by environmental factors that are challenging to quantify. Recognizing the limitations of the above two methods, this paper proposes a cumulative distribution-based technique for developing pavement performance prediction models. First, after comparing performance metrics such as pile-by-pile single-point, probability density, and cumulative distribution, it is evident that the cumulative distribution is the most reliable method for describing pavement conditions. A continuous distribution function is created from a limited set of discrete observed field pavement condition data using the sampling theorem. With cumulative distribution-based deterioration curves changing over time, it is possible to predict future pavement deterioration rates. A case study is presented at last. Analyses of the predicted curve and observed pavement performance indicate that the cumulative distribution-based technique is effective in modeling pavement performance and can provide reliable predictive results.

Curb detection and mapping are of great importance to ensure the safety and efficiency of intelligent vehicles. However, it remains challenging because shape estimation under noise and outliers is not well addressed in real traffic scenarios. In this paper, an efficient curb detection and mapping algorithm is proposed to achieve an accurate representation of curb shape. More specifically, an iterative Gaussian process regression (iGPR) is introduced, where each candidate point is verified multiple times. Then iGPR is employed in shape estimation of both road profile and curb, which serves as the backbone unit in curb candidate detection. During this process, the input 3D point cloud is segmented into road and obstacles, and potential curb points are selected by evaluating physically interpretable curb features. Finally, the proposed iGPR is validated and tested on two large-scale, complex urban datasets under real traffic scenarios. Experimental results show that the proposed iGPR achieves better performance than several state-of-the-art algorithms.

During the construction of the metro shield, the surrounding ground surface and buildings will settle due to the disturbance of sand and gravel during excavation. Therefore, it is essential to evaluate the safety and stability of shield tunneling. Aiming to address the ambiguity, randomness, and significance of each index in the process of evaluating safety and stability during shield tunneling, the study utilizes a method that combines game theory and extension cloud. Monitoring data related to ground subsidence, building settlement, embankment settlement, pipeline settlement, and segment deformation are selected as evaluation indices to develop a comprehensive extension cloud evaluation model for shield tunneling safety. The model is validated using real-world engineering cases. The results show that according to the combined weight results obtained by the game theory, analytic hierarchy process, and entropy weighting method, the safety and stability evaluation indexes of the shield tunnel interval in this case are building settlement, segment deformation, ground settlement, pipeline settlement, and embankment settlement in order of importance. The safety and stability of nineteen monitoring groups are evaluated using the comprehensive extension cloud model, including eight Level III, four Level II, and seven Level I. Among them, monitoring groups at Level II and Level I nearby need to adopt corresponding preventive measures. The comprehensive evaluation model developed in this study, based on game theory and extension cloud, can reflect the uncertain relationships among various indicators. It helps to avoid the one-sidedness of single weighting. Compared with fuzzy comprehensive evaluation, the calculation results are more aligned with objective reality. Based on the aforementioned research, a comprehensive evaluation method is proposed, which combines game theory and extension cloud model. This method offers a novel approach and concept for safety evaluation in shield construction. The research can provide scientific references for the safety and stability evaluation of shield tunneling in sandy and pebble strata in the Chengdu area.

Taking the carbonaceous slate of Zhuokeji tunnel on the Wenchuan-Ma,erkang expressway as the research subject, triaxial compression creep tests were conducted under various freeze-thaw cycles to analyze the creep strain characteristics and long-term strength of the carbonaceous slate. Assuming that the aging damage of rock under load follows the Weibull probability density distribution, the loaded damage variable is defined. The freeze-thaw damage variable is defined according to the phenomenological theory of damage mechanics. Considering the coupling effect of freeze-thaw and stress, a total damage variable for freeze-thaw and load is constructed. Based on the creep behavior of carbonaceous slate, the structure of the H-H｜N-N｜S creep model is determined. Based on this, the damage evolution is carried out, and a new creep damage model that can reflect the coupling of freeze-thaw and stress is obtained, which is extended to a three-dimensional stress state. The solution method for model parameters is provided, and the damage evolution law is analyzed. The creep characteristics of carbonaceous slate are identified using the established model. A traditional model is introduced for comparison, and the simulation comparison curve is analyzed to verify the feasibility and rationality of the new model.

Travel time distribution is an essential tool for measuring urban traffic performance, a subject that has been studied for decades. This paper conducts a comprehensive investigation of two types of travel time distributions using extensive license plate recognition data from Automatic Number Plate Recognition techniques on four signalized arterials in Guiyang, China. The travel time plane distributions presented in the overlay charts of observed travel times usually exhibit significant stratified data strips. When considering signal schemes, we observe that the cycle times of the first upstream and last downstream intersections are the determining factors for the data patterns of travel time plane distributions. We also investigate the characteristics of single or multiple peaks within various departure time windows. The results indicate that travel time statistical distributions are more likely to exhibit multiple states under short time windows. As for the shapes of travel time statistical distributions, skewness and kurtosis are used as descriptive statistics. The results show that the majority of statistical distributions are positively skewed and leptokurtic, and the skewness is highly correlated with the kurtosis. A stable skewness and kurtosis at a relatively lower level may be caused by lower travel time reliabilities.

Effective traffic management and congestion reduction heavily rely on accurate traffic flow prediction. Existing prediction methods, such as Markov, ARIMA, STANN, GLSTM, and DCRNN models, often face challenges because they rely on fixed spatial relationships, leading to limited long-term prediction accuracy. To address these shortcomings, this study proposes the Impedance-Spatio-Temporal Topological Network (Impedance-STTN) prediction model. The Impedance-STTN model integrates K-medoids clustering for data analysis, generating a real-time impedance matrix from impedance functions, traffic big data, and real-time flow data. This approach captures dynamic node relationships within the spatio-temporal network, enhancing prediction accuracy. Experimental results demonstrate the superior predictive performance of the Impedance-STTN model, achieving accuracies of 94.79%, 93.78%, and 93.11% in 5 min, 15 min, and 30 min predictions, respectively. These results outperform existing models, especially in long-term predictions. The findings underscore the model’s high accuracy and effectiveness across varying prediction durations, marking a significant advancement in traffic flow prediction. This suggests promising avenues for future research and practical applications.

The efficient, reliable, and sustainable nature of a transportation system is a prerequisite to support the development of urban agglomeration. This paper proposes network modeling and resilience assessment methods for public transportation in urban agglomerations. A multi-layer network is constructed. With the identification of the key nodes in a multi-modal transportation network (MMTN), a resilience assessment method is proposed that considers two phases: absorption and recovery after an attack. The Beijing-Tianjin-Hebei urban agglomeration network is taken as a case study. The results show that the attack on key nodes brings more influence to MMTN than random attacks. More attention is suggested to be paid to the larger hub-type stations in operation and management. The proposed method can be applied in different types of urban agglomerations and serve as technical support for reducing the disorder and imbalance of MMTN.

With the development of networked vehicles, location information-based transportation systems have proven to provide significant benefits. However, the exposure of vehicle location information also raises important privacy issues. Current typical methods for protecting vehicle location privacy protection methods such as anonymity and pseudonymity, still carry the risk of the vehicle being tracked, leading to data security issues. This paper proposes a kind of vehicle trajectory generation algorithm based on Generative Adversarial Networks (GAN). The algorithm utilizes vehicle movement trajectory data to train both the discriminator and generator models to generate virtual trajectory data that matches the distribution of real trajectory data. Therefore, virtual trajectory data can obscure vehicle information, addressing the privacy concerns associated with moving trajectory data and enhancing the security of applications. In this paper, the vehicle travel time of sample trajectory data and virtual trajectory data is used as indicators for statistical analysis. The experiment demonstrated that the cumulative probability distribution of travel time for the sample data and virtual data passed the Kolmogorov-Smirnov (K-S) test at permeabilities ranging from 10% to 100% and at significance levels of 0.01 and 0.05. Both datasets accepted the hypothesis that they originate from the same distribution. The reliability of the proposed method for generating virtual trajectories has been demonstrated.