Metro Emergency Passenger Flow Prediction on Transfer Learning and LSTM Model
Jingye Ma, Xin Zeng, Xiaoping Xue, Ranran Deng
Abstract
The metro transportation system will have emergency passenger flow for various reasons, resulting in passenger flow congestion, affecting efficiency and risks. In this paper, the LSTM network is applied to predict the normal passenger flow and emergency passenger flow of metro transportation based on transfer learning to solve the imbalanced data set problem when the amount of emergency samples is too small. The results show that under normal and emergency conditions, the average prediction error is less than 5%, which provides an alarm for the operating company to take preventive measures in advance. Compared with the strategy without transfer learning, it proves that the strategy proposed in this paper has advantages in predicting emergency conditions.