## 2 Related Work  
A variety of issues in public transport, such as transfer service [14], still lack in-depth investigation, meaning that few studies have analyzed and forecasted transfer passenger flow among different transport tools. Recently, varied studies on the prediction of the flow, speed and density of single-traffic vehicles have achieved great success [4; 9], which are helpful for transfer passenger flow prediction. Herein, we recall the related applications of various forecast techniques on traffic time series, including traffic data imputation and forecasting. Restricted by the backwardness of data processing, early parametric prediction models (e.g., historical average [7] and Kalman filter [3]) can forecast stable traffic flow well, but fail to deal with the increasing amount of unsteady and non-linear traffic data [15]. Because of this, the availability of traffic data has provided more opportunities for forecasting efforts, and a wide variety of statistical learning methods, including principal component analysis (PCA)-based [11] and decomposition-based [9] algorithms, have been adopted to model traffic data. For example, [PERSON] et al. [16] proposed a robust PCA (RPCA) model for traffic data prediction. [PERSON] et al. [17] introduced three online non-negative matrix factorization (ONMF) methods for traffic crowd flow forecasting. Additionally, it is well known that high-quality traffic data offers a guarantee for highly accurate prediction results [10], and some scholars have studied spatiotemporal data assuming a lack of data entries. [PERSON] et al. [18] put forward a probabilistic PCA (PPCA)-based model to repair missing traffic values. [PERSON] et al. [19] created an imputation model (CIM) for missing traffic congestion data using joint MF. Further, [PERSON] et al. [10] introduced a compensated residual MF with spatial-temporal regularization (ST-CRMF) for traffic time series repair and multi-interval forecasting. Moreover, [PERSON] et al. [11] established a Bayesian temporal factorization (BTF) framework for both missing traffic data imputation and multi-step rolling prediction tasks. Except for the aforementioned approaches, ML and its subclass DL have aroused great academic and industrial interest in the past decades [20]. Of these, traditional ML prediction models, such as SVM, RF, XGBoost [21], wavelet transform (WT) [22] and Bayesian networks [23], have achieved favorable forecasting performance in intelligent transportation systems (ITS). For example, [PERSON] et al. [24] presented an adaptive multi-kernel SVM (AMSVM) with spatiotemporal correlation for short-term traffic flow prediction. After acquiring point-of-interest (POI) data around bus stops, [PERSON] et al. [25] employed an XGBoost to forecast the passenger flows of each bus line. In addition, due to the complexity of ITS, it is difficult for a single ML model to output reliable prediction results, and the hybrid methods connecting multiple algorithms enable the uncertainty and nonlinear characteristics of traffic data to be handled more effectively. [PERSON] et al. [7] built a hybrid Wavelet-SVM model for short-time passenger flow forecasting in Beijing metro. [PERSON] et al. [9] introduced a time-series-decomposition-based forecasting model with transfer learning for rail short-term passenger flow in holiday periods. Complying with further technological innovations, studies on traffic prediction with DL, such as using LSTM-based models to exploit the spatiotemporal properties of traffic data, have clearly outperformed the traditional methods. In fact, LSTM can capture and preserve the long- and short-term nonlinear traffic dynamics in an effective manner, and thus it is widely adopted in transportation fields. [PERSON] et al. [26] first introduced an LSTM network for short-term travel speed prediction. To promote prediction accuracy, [PERSON] et al. [27] further constructed a cascaded LSTM network considering spatiotemporal correlation for short-term traffic prediction.