Using a deep learning method to detect buried pipes on ground penetrating radar (GPR) data is popular for its excellent performance potential. However, this needs a large amount of high-quality training data, which leads to time-consuming and labor-intensive data annotation work. Semi-supervised learning method provides a solution for the situation of small sample size. However, the original semi-supervised learning methods lack control over the quality of pseudo labels during model training. To address the problem, a new prior knowledge-guided semi-supervised deep learning method is proposed to improve the model performance under the small sample. In the method, a prior structure feature (PSF) is constructed to control the quality of pseudo label during semi-supervised learning. The structure response index (SRI) is designed to segment the structure subject out for eliminating disturbance from unstructured information. Then, the PSF is represented using histograms of oriented gradients (HOGs) and Fourier descriptors (FDs) to describe the structure’s edge orientation and shape. Based on the PSF, LightGBM is employed as a discriminator to screen out high-quality pseudo labels for model training. In the experiments, comparison experiments with some state-of-the-art supervised learning methods and semi-supervised learning methods were carried out to validate the method’s performance. The ablation studies on labeled dataset size, pseudo-label confidence threshold, and PSF were conducted to validate the effectiveness of the prior structural feature in controlling the pseudo label’s quality. The results show that the PSF is effective, and the proposed method outperforms the other methods.

Sensors layout plays an important role in proactive traffic safety management system for accidents detection and prevention. However, traffic accidents uncertainty and sensors heterogeneity are difficulty for sensors optimal layout on road network. To solve the problems, an optimal layout method of heterogeneous sensors is presented. In the method, a traffic accident risk distribution assessment model is established to reduce the uncertainty influence of traffic accidents and obtain the stable spatial distribution of accident risk on road network. Then heterogeneous sensors optimal layout model is established to maximize the coverage quality on road network traffic accident risk with the constraints of sensors types, cost, accidents detection error, and so on. A two-stage heuristic method is proposed by pruning search algorithm and AO-GI-MO algorithm to optimize sensors types, numbers and positions. Stability, convergence, consistency, reliability experiments are carried out to validate the proposed method. The results show the proposed method could deal with traffic accidents uncertainty efficiently and achieve the optimal layout of heterogeneous sensors on road network.

Pavement segregation reduces pavement strength and causes early pavement damages. As a convenient and nondestructive subsurface detection technology, Ground Penetrating Radar (GPR) is widely applied to detect segregation by manual interpretation. This induces labor-intensive and time-consuming. And then the segregation is interpreted automatically by image processing technology based on GPR B-scan images. However, this ignores the intrinsic physical properties of segregation. To address the problem, a new method for automatically detecting segregation is proposed to interpret segregation by combining visual features with intrinsic physical properties of segregations. In the method, GPR data preprocessing is carried out to remove the subgrade layer disturbance and enhance GPR signal. And then the candidate segregation locating is put forward by segregation representation index and graph cuts algorithm according to segregation visual and statistical characteristics. Segregation visual features and intrinsic physical properties are represented for recognition according to gray level co-occurrence matrix and dielectric properties. The extreme gradient boosting is employed as a classifier to recognize segregation. In experiments, experiment samples are collected by 3D GPR testing vehicle and coring experiments, and comparison experiments are carried out to validate the proposed method by analysis on feature selection, segregation localization and segregation recognition. The results show the proposed method has a better performance than the other methods, such as multi-grained cascade forest, deepLab v3+ and SegNet. As a result, the proposed method will provide a new reference for automatic interpretation GPR application, especially, on the combining visual features with intrinsic physical properties.

To improve the efficiency of detecting abnormal traffic incidents on the road network and reduce the false alarm rate, a real-time traffic anomaly detection framework based on a graph spatiotemporal pattern learning (GSTPL) network is proposed. In this framework, a traffic pattern search algorithm based on a fluctuation similarity measure is designed to screen traffic flow data with the same traffic pattern, and a traffic pattern graph tuple is constructed as the input of the network model to avoid the sample imbalance problem and the effect of single-sample randomness for traffic pattern learning. Then the GSTPL network is designed to extract, unsupervised, the traffic spatiotemporal pattern features and make a reasonable prediction of future traffic parameters as the basis for anomaly evaluation. To further restrain the effect of random fluctuations in traffic flow parameters, an abnormal state evaluation method is designed to calculate the anomaly state likelihood by prediction error distribution learning. The overall detection framework realizes stable prediction of network key node traffic parameters by using spatiotemporal pattern features to construct the traffic pattern graph tuple, and gives incident evaluation results in real time by combination with the detection data. The experiment uses I90 and I405 highway traffic data in Seattle, WA, from 2015. Through comparative analysis, the proposed incident detection method based on GSTPL has a higher detection rate and lower false alarm rate, can adaptively learn dynamic changes of the traffic pattern, and has strong adaptability and stability to different traffic environments.

Electrified roads represent an emerging transportation solution in the context of global energy transition. These systems enable vehicles equipped with roof-mounted pantographs to draw power from overhead contact lines while in motion, allowing continuous energy replenishment. The effectiveness of this energy transfer—namely, the quality of pantograph–catenary interaction—is significantly influenced by the pantograph’s equivalent mechanical parameters. This study develops a three-dimensional overhead catenary model and a five-mass pantograph model tailored to electrified roads. Under conditions of road surface irregularities, it investigates how variations in equivalent pantograph parameters affect key contact performance indicators. Simulation results are used to identify a new set of equivalent pantograph parameters that significantly improve the overall quality of pantograph–catenary interaction compared to the baseline configuration. Sensitivity analysis further reveals that, under road-induced excitation, pan-head stiffness is the most critical factor affecting contact performance, while pan-head damping, upper frame stiffness, and upper frame damping show minimal influence. By constructing a coupled dynamic model and conducting parameter optimization, this study elucidates the role of key pantograph parameters for electrified roads in determining contact performance. The findings provide a theoretical foundation for future equipment development and technological advancement.

Accurate bus passenger flow prediction is a critical prerequisite for optimizing resource allocation in intelligent bus systems and improving public transport service quality. However, due to dynamic fluctuations in passenger travel demand and the complex topological structure of bus networks, effectively capturing the inherent spatiotemporal dependencies of passenger flow remains a significant challenge. To address this issue, this paper proposes GAT-Informer, a hybrid deep learning model for short-term bus passenger flow prediction. Unlike conventional methods that mainly rely on fixed physical adjacency or single-view spatial correlations, the proposed model incorporates domain knowledge into graph construction through a four-dimensional associated-node mechanism. Specifically, four types of features, namely line connectivity, spatial proximity, OD correlation, and station functional similarity, are used to identify relevant associated nodes and construct a passenger flow interaction spatiotemporal graph that better reflects inter-station dependencies. Based on this graph, a Graph Attention Network (GAT) is introduced to adaptively learn spatial interaction features and differentiated influence weights among associated stations. The spatially enhanced features extracted by GAT are then fed into the Informer network, where probabilistic sparse self-attention and hierarchical timestamp encoding are employed to efficiently capture long-term temporal dependencies and periodic fluctuation patterns of passenger flow. Experimental results based on bus passenger flow data from Foshan City show that the proposed GAT-Informer model significantly outperforms benchmark models, including LSTM-Transformer, CNN-GRU, and STGCN, across different prediction horizons, validating its effectiveness and improved predictive performance in bus passenger flow prediction.