# Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
## 2 Literature Review on Short-Term Passenger Flow Prediction of URT  
In the past research, the research on the short-term passenger flow prediction of URT mainly focused on three aspects: the analysis of the spatial and temporal characteristics of passenger flow, the establishment of prediction models, the optimization of prediction models under different circumstances, and the selection of time granularity [14]. These three aspects are also the main problems of passenger flow prediction of URT. The research on the prediction of short-term passenger flow of URT previously only considered the temporal characteristics of passenger flow and predicted passenger flow on the basis of only collecting historical passenger flow data. [PERSON] et al. used the LSTM model for traffic passenger flow prediction and found that the long-term learning advantage of the LSTM model could not be reflected [15; 16]. [PERSON] et al. used the LSTM model to predict the short-term passenger flow of URT and pointed out that the LSTM model had a faster convergence speed and better stability [17; 18]. [PERSON] found that the LSTM model can capture the nonlinear characteristics of the time series passenger flow data of a single station in short-term passenger flow prediction [19]. [PERSON] et al. predicted the monthly passenger flow of Ampang Line in Malaysia by fitting the time series model, and the prediction result was good [20]. [PERSON] et al. used a Bluetooth detector to collect traffic flow data and compared several time series prediction methods based on this data and found that the ARIMA model performed best in predicting traffic demand [21]. [PERSON], [PERSON] and [PERSON] et al. have all predicted the short-term passenger flow of rail transit based on the ARIMA model, and the results also show that the model has good performance [22; 23; 24]. However, the advantages and disadvantages of a single model are prominent, and the applicability is different. A single model can only capture the temporal or spatial characteristics of the data and ignore the impact of historical cycle segments on the target time. The existing models pay little attention to multi-source external information such as weather and air conditions. When external factors change greatly, it has a great impact on the accuracy of passenger flow prediction, and the ability to capture the peak is not enough. With the development of URT and passenger travel demand increasing year by year, the passenger flow of URT shows many characteristics, such as strong nonlinearity, correlation, and periodicity. The method based on capturing the time series characteristics of passenger flow can no longer guarantee the accuracy of passenger flow prediction with strong randomness. And in recent years, due to the development of intelligent rail transit, the intelligent rail transit technology represented by driverless, virtual coupling, etc. needs more accurate passenger flow prediction data as decision support. Therefore, many scholars have established a combination model of passenger flow prediction with high prediction accuracy and wide application according to the actual situation. [PERSON] et al. proposed a machine learning-based ARMA-LSTM model for prediction. It was found that the prediction effect was significantly better than a single model, and the combined model had higher accuracy and better applicability in passenger flow prediction [25]. [PERSON] et al. proposed a short-term passenger flow prediction method based on the PSO-LSTM model, which better solved the problems such as the difficulty of accurate short-term passenger flow prediction [26]. [PERSON] proposed a method combining CNN and LSTM to forecast future traffic flow [27]. [PERSON] et al. proposed a novel Wave-LSTM model, based on combining the long short-term memory network (LSTM) and the wavelet.