A Semi-Conv-Transformer Model for Inflow Prediction of Newly Expanding Subway Lines

Due to the rapid development of urban rail transit and the expansion of new lines, accurately predicting the passenger flow of newly expanding subway lines has become increasingly important. The lack of historical data and long forecasting times have led to insufficient accuracy in previous studies when directly predicting inflow at new line stations. To address these challenges, this study proposes a method to decompose inflow features into trend features and scale features, and introduces a model named Semi-Conv-Transformer, based on semi-supervised learning and deep learning neural networks, for prediction of newly expanding subway lines. This innovative approach divides the inflow prediction of newly expanding subway lines into three parts: 1) enhancing the dataset using semi-supervised learning for data augmentation, 2) predicting trend features and scale features with Conv-Transformer deep learning model, and 3) combining the trend features and scale features of station passenger flow to obtain the required inflow data. The proposed method was tested on a new subway line operated in Nanning, China. In this experiment, the model achieved better experimental performance than previous methods and achieved higher accuracy.