Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis

Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems (ITS). According to previous studies, it is found that the prediction effect of a single model is not good for datasets with large changes in passenger flow characteristics and the deep learning model with added influencing factors has better prediction accuracy. In order to provide persuasive passenger flow forecast data for ITS, a deep learning model considering the influencing factors is proposed in this paper. In view of the lack of objective analysis on the selection of influencing factors by predecessors, this paper uses analytic hierarchy processes (AHP) and one-way ANOVA analysis to scientifically select the factor of time characteristics, which classifies and gives weight to the hourly passenger flow through Duncan test. Then, combining the time weight, BILSTM based model considering the hourly travel characteristics factors is proposed. The model performance is verified through the inbound passenger flow of Ningbo rail transit. The proposed model is compared with many current mainstream deep learning algorithms, the effectiveness of the BILSTM model considering influencing factors is validated. Through comparison and analysis with various evaluation indicators and other deep learning models, the results show that the R2 score of the BILSTM model considering influencing factors reaches 0.968, and the MAE value of the BILSTM model without adding influencing factors decreases by 45.61%.