Short-Term Inbound Passenger Flow Prediction of Urban Rail Transit Based on RF-BiLSTM.

Short-term passenger flow prediction of urban rail transit based on historical data mining can guide the work of station organization, train scheduling, and passenger flow induction effectively and dynamically. We propose the RFBiLSTM prediction model, which combines the random forest algorithm (RF) and bi-directional long short-term memory neural network (BiLSTM). Firstly, the time series features of the flow are obtained by clustering algorithm and correlation analysis. Secondly, RF is used to obtain the importance of the features. Finally, we compare the prediction performance of 7 models and investigate the impact of feature selection on deep learning models. Through case analysis, the prediction accuracy of RF is the highest when using a single model for prediction, and the MAPE is 0.102. When using the combined prediction model, the MAPE of RF-BiLSTM is 0.074. The accuracy of RF-BiLSTM is better compared with the results of a single model. The performance of BiLSTM is improved by 46.6% after the features are optimized using feature selection methods. The findings demonstrate the suitability of the combined prediction model RF-BiLSTM for predicting shortterm inbound passenger flow. [ABSTRACT FROM AUTHOR]