MA2GCN: Multi Adjacency relationship Attention Graph Convolutional Networks for Traffic Prediction using Trajectory data

The problem of traffic congestion not only causes a large amount of economic losses, but also seriously endangers the urban environment. Predicting traffic congestion has important practical significance. So far, most studies have been based on historical data from sensors placed on different roads to predict future traffic flow and speed, to analyze the traffic congestion conditions of a certain road segment. However, due to the fixed position of sensors, it is difficult to mine new information. On the other hand, vehicle trajectory data is more flexible and can extract traffic information as needed. Therefore, we proposed a new traffic congestion prediction model - Multi Adjacency relationship Attention Graph Convolutional Networks(MA2GCN). This model transformed vehicle trajectory data into graph structured data in grid form, and proposed a vehicle entry and exit matrix based on the mobility between different grids. At the same time, in order to improve the performance of the model, this paper also built a new adaptive adjacency matrix generation method and adjacency matrix attention module. This model mainly used gated temporal convolution and graph convolution to extract temporal and spatial information, respectively. Compared with multiple baselines, our model achieved the best performance on Shanghai taxi GPS trajectory dataset. The code is available at https://github.com/zachysun/Taxi_Traffic_Benchmark.

A Comparative Study on Application of Class-Imbalance Learning for Severity Prediction of Adverse Events Following Immunization

In collaboration with the Liaoning CDC, China, we propose a prediction system to predict the subsequent hospitalization of children with adverse reactions based on data on adverse events following immunization. We extracted multiple features from the data, and selected "hospitalization or not" as the target for classification. Since the data are imbalanced, we used various class-imbalance learning methods for training and improved the RUSBoost algorithm. Experimental results show that the improved RUSBoost has the highest Area Under the ROC Curve on the target among these algorithms. Additionally, we compared these class-imbalance learning methods with some common machine learning algorithms. We combined the improved RUSBoost with dynamic web resource development techniques to build an evaluation system with information entry and vaccination response prediction capabilities for relevant medical practitioners.