## 1 Introduction  
Modern society is facing serious traffic challenges, including traffic accidents, congestion and pollution, and accurate traffic prediction holds the key to alleviating these issues [1]. Normally, traffic forecasting is performed to assess the flow, speed and density of urban road networks, and rarely involves transfer flow between different transport tools. However, due to the constraints of urban network structures and running schedules, some commuters need to transfer between different transport modes to arrive at their final destinations [2]. Usually, commuters in metropolitan areas make reasonable plans before traveling. Especially for remote areas without direct access, effective and less delayed transit transfers can not only greatly enhance commuters' travel comfort but also help to alleviate urban traffic congestion by dispersing crowds more rapidly. Meanwhile, urban rail transit systems worldwide experienced a rapid expansion in scale and large increase in demand before the pandemic [3]. A future trend of urban transport progress is building comprehensive traffic systems with metros as the backbone and public transport as a form of assistance in the post-pandemic era [4]. Hence, determining how to conduct mathematical modelling and algorithm analysis on the collected historical transfer passenger flows of different modes of transport to establish a high-performance forecasting model, and finally estimate future transfer passenger flow, is crucial. Accordingly, transport operators should develop Sustainable Mobility as a Service (S-MaaS) solutions [5,6] that integrate different forms of transportation and services, with their supply components ultimately providing more effective transfer services for public transport. In urban transportation systems, metro and bus, two of the most convenient transport services, dominate public transportation. As of late 2019, China had 40 cities with rail transit, or metro as a narrow definition [4], and this trend is accelerating. Hence, there are a large amount of transfer services between buses and metro stations that are suitable as a target of our research. In this era of big data, a large number of traffic data forecasting techniques have been proposed. Concretely, the available traffic forecasting algorithms can be roughly divided into classical and machine learning (ML) methods, the former of which can be further subdivided into traditional linear methods and statistical learning methods. To be more specific, earlier research efforts focused on historical averages [4,7], smoothing techniques [7,8] and later autoregressive integrated moving averages (ARIMA) [9] and its variants. Moreover, the decomposition-based statistical learning approaches, specified as matrix factorization, can accomplish the forecasting and even imputation tasks through a low-rank matrix factorization (LRMF) capturing spatial-temporal dependencies [10]. Nevertheless, the overly strict assumptions required by these methods have hindered the utilization of large-scale traffic data modelling processes [8,11]. Currently, studies on predicting traffic value using the popular ML-based methods have made significant progress compared with non-ML models [12], such as support vector machine (SVM), decision tree (DT), extreme gradient boosting (XGBoost) and random forest (RF), which are frequently utilized for traffic prediction tasks due to their superior abilities to capture partial complex spatiotemporal correlations from small incomplete datasets. In fact, it has been proven that deep learning (DL) is an emerging technology due to its good fitting capability [13]. As a hot research topic, several existing DL models perform well in traffic prediction, including long short-term memory (LSTM) and convolutional-based neural networks [1]. In particular, LSTM, a variant of recurrent neural networks (RNN), is capable of alleviating the vanishing gradient and effectively tackling the network-level time series forecasting problem [13]. Nevertheless, we still face great challenges.