AST-GIN: Attribute-Augmented Spatiotemporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting.

Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With accurate EV station availability prediction, suitable charging behaviors can be scheduled in advance to relieve range anxiety. Many existing deep learning methods have been proposed to address this issue; however, due to the complex road network structure and complex external factors, such as points of interest (POIs) and weather effects, many commonly used algorithms can only extract the historical usage information and do not consider the comprehensive influence of external factors. To enhance the prediction accuracy and interpretability, the Attribute-Augmented Spatiotemporal Graph Informer (AST-GIN) structure is proposed in this study by combining the Graph Convolutional Network (GCN) layer and the Informer layer to extract both the external and internal spatiotemporal dependence of relevant transportation data. The external factors are modeled as dynamic attributes by the attributeaugmented encoder for training. The AST-GIN model was tested on the data collected in Dundee City, and the experimental results showed the effectiveness of our model considering external factors' influence on various horizon settings compared with other baselines.

Missing Traffic Data Imputation with a Linear Generative Model Based on Probabilistic Principal Component Analysis.

Even with the ubiquitous sensing data in intelligent transportation systems, such as the mobile sensing of vehicle trajectories, traffic estimation is still faced with the data missing problem due to the detector faults or limited number of probe vehicles as mobile sensors. Such data missing issue poses an obstacle for many further explorations, e.g., the link-based traffic status modeling. Although many studies have focused on tackling this kind of problem, existing studies mainly focus on the situation in which data are missing at random and ignore the distinction between links of missing data. In the practical scenario, traffic speed data are always missing not at random (MNAR). The distinction for recovering missing data on different links has not been studied yet. In this paper, we propose a general linear model based on probabilistic principal component analysis (PPCA) for solving MNAR traffic speed data imputation. Furthermore, we propose a metric, i.e., Pearson score (p-score), for distinguishing links and investigate how the model performs on links with different p-score values. Experimental results show that the new model outperforms the typically used PPCA model, and missing data on links with higher p-score values can be better recovered.

Study on the Application of Modified Sn-Based Solder in Cable Intermediate Joints.

With the increasing use of underground cables, the quantity and quality of intermediate joints demanded are also increasing. The quality of the traditional crimping intermediate joint is easily affected by the actual process of the operator, which may lead to the heating of the crimping part of the wire core, affecting the insulation performance of the cable, and finally causing the joint to break. However, aluminothermic reactive technology has some problems, such as a high welding temperature and an uncontrollable reaction. In order to solve these problems, according to the brazing principle and microalloying method, the optimal content of In in Sn-1.5Cu-based solder was explored, and then the connection of the middle joint of a 10 kV cable was completed using a connecting die and electrical connection process. The contact resistance and tensile strength of the joint were tested to verify the feasibility of this method. The results show that the maximum conductivity of the solder with 3.8% and 5% In content can reach 3.236 × 106 S/m, and the highest wettability is 93.6%. Finally, the minimum contact resistance of the intermediate joint is 7.05 µΩ, which is 43% lower than that of the aluminothermic welded joint, and the tensile strength is close to that of the welded joint, with a maximum of 7174 N.