## 1 Introduction  
The rapid progress of urbanization and the fast growth of urban populations has led to a series of roadway transport problems such as severe traffic congestion [1]. The surge in traffic volume then seriously affects the normal operation of bus transit services. For bus transit operators, the real-time control strategies of bus operations, such as cross-line bus dispatching, has become one of the most effective means to reduce disturbance during operation [2]. The cross-line dispatching strategy involves allocating bus vehicles from low-demand bus lines to high-demand ones to match the peak bus travel demand in a bus transit network. The basis of this real-time strategy is the short-term prediction of passenger flows [3]. With the accurate prediction of passenger flows, especially in low-demand bus lines, the operations of bus transit services can be more effective and efficient, thereby enhancing the attractiveness of the whole public transit service [4, 5]. With more travelers shifting to the public transit system, it can ultimately mitigate traffic congestion, leading to the sustainable development of the city. With the development of the smart integrated circuit (IC) card system, bus transit operators are able to collect the bus travel demand in real-time [6]. Thus, a series of data-driven methodologies have been developed for travel demand prediction. Early studies paid more attention to time series analyses. For instance, [PERSON] et al. [7] utilized an autoregressive integrated moving average (ARIMA) model for short-term travel demand forecasting. [PERSON] et al. [8; 9] developed the theoretical foundation for travel demand prediction using the seasonal ARIMA process. [PERSON] et al. [10] proposed an improved ARIMA-based prediction method to forecast the spatial-temporal variation in passengers. In addition to the time series analyses, the Kalman filter, known for its robustness against the noise in data, has also been widely used for prediction tasks. For example, [PERSON] et al. [11] modified the traditional Kalman filter based on the error correction coefficient, historical deviation, and Bayesian combination to predict short-term passenger flows in the rail transit system. Nonetheless, these statistical methods normally assume that the changes in travel demand are linear, whereas the actual patterns may be nonlinear. To overcome this limitation, various machine learning models have been applied for travel demand forecasting. Classic machine learning methods, such as support vector regression [12] and K-nearest neighbor [13], have been widely used to transform time series problems into supervised learning problems, which achieve a high prediction accuracy. [PERSON] et al. [14] proposed to use random forest models to predict the number of passengers entering each metro station or boarding at each bus stop. [PERSON] et al. [15] developed a secondary decomposition integration method that combines empirical modal decomposition, sample entropy, and kernel extreme learning machines for the short-time prediction of bus route passenger flow. However, the shallow structure of the classic machine learning methods makes it difficult to handle the complex non-linearity in spatial and temporal patterns of travel demand [16]. In recent years, deep learning models have been widely applied to prediction problems in the transportation field. To deal with the temporal dependency, [PERSON] et al. [17] adopted a deep neural network to predict continuous travel time for transit signal priority. [PERSON] et al. [18] used a recurrent neural network for traffic speed prediction, while [PERSON] et al. [19] employed the long short-term memory (LSTM) model for travel time prediction. For travel demand prediction, [PERSON] et al. [20] proposed a deep architecture composed of deep belief networks (DBN) and multitask regression layers for short-term travel demand prediction. [PERSON] et al.