Short-Term Metro Passenger Flow Prediction Based on Random Forest and LSTM

Abstract

Rapid and accurate short-term passenger flow prediction plays an important and far-reaching role in passenger flow control and early warning. In fact, the short-term passenger flow presents the characteristics of non-linearity and randomness. Traditional machine learning algorithms can hardly meet current predictions. In this paper, the random forest(RF) is used to calculate the feature importance to filter the extracted features and remove the redundant features, and we apply the Long Short-Term Memory network(LSTM) algorithm model to predict the short-term passenger flow of the metro. First, we calculate the out-of-bag(OOB) error of the features by RF based on the characteristics of bootstrap sampling and regression tree, and calculate OOB error again after adding noise. According to the two OOB errors, the feature importance can be obtained through related formulas, and some features can be filtered. RF can effectively reduce redundant features to participate in calculation and improve operating efficiency. Second, we apply the LSTM model to predict passenger flow every 10 minutes for each station and use the important features selected by RF as the model inputs. LSTM has an excellent effect in dealing with problems that are highly related to time series, and it is very suitable for prediction on time series issues. The proposed model is evaluated with real metro card data, the prediction performance compares to single RF, LSTM, and other algorithm models. The experiment results show that the accuracy of the RF combined LSTM model algorithm is better than that of other existing models such as RF and LSTM model. It shows good prediction accuracy and has far-reaching significance in the field of passenger flow prediction.