## 1 Introduction
[21] developed a short-term passenger demand prediction method based on the LSTM to capture the temporal features, of which the results show that the deep learning models outperform the classic machine learning methods. In addition to temporal dependency, the spatial relationship pertains to the utilization of neighboring data to enhance the effectiveness of deep learning. By scanning a filter across the grid-structured data, the convolutional neural network (CNN) structure is developed to capture the neighborhood features [22; 23]. Nonetheless, with non-grid data structures, such as graphs, the graph convolutional network (GCN) has an advantage over CNN by considering the connectivity information between the vertices in the graph [24]. For instance, [PERSON] et al. [25] have proposed a spatiotemporal GCN model that does not use traditional conventional units but constructs a model on a graph instead to address the time series prediction problems. Since then, more studies have started to take into account both temporal and spatial dependencies for travel demand prediction. [PERSON] et al. [26] proposed a deep learning architecture to capture nonlinear spatiotemporal effects. [PERSON] et al. [27] modeled the periodicity of travel demand and the spatial correlation between metro stations with the incorporation of factors, such as weather and holidays, to perform the short-term prediction of passenger flows in the metro system. [PERSON] et al. [28] combined LSTM with spatiotemporal residual networks to predict the spatiotemporal travel flow across the city. [PERSON] et al. [29] proposed a graph-based deep learning approach by fusing the dynamic built environment influences and spatiotemporal dependency to predict short-term bus travel demand. [PERSON] et al. [30]adopted the spatial-temporal graph sequence with the attention network for bus passenger flow forecasting in Urumqi, China. [PERSON] et al. [31] developed a more scalable and robust bus network graph convolutional long short-term memory neural network model using data from the Laval bus network in Canada. These novel deep learning frameworks present great performance in capturing complicated spatiotemporal dependencies in travel demand prediction. Most existing graph-based models, including the GCNs, typically view edges as simple binary connections, which are either present or absent. However, the real-world relationships between the vertices are often much more nuanced and intricate [32]. Thus, a new graph convolution method, the graph diffusion convolution (GDC), is developed upon the foundation of GCN [33]. In the process of GDC, the attention of the model is initially focused on one node of interest, then gradually spreading to the node's neighboring vertices, thereby diffusing the attention away from the starting node of interest. Such a process leads to a more comprehensive and flexible understanding of the underlying graph structure [34]. [PERSON] et al. [35] have applied a diffusion convolutional recurrent neural network model by integrating the GDC into RNN, in order to predict the roadway traffic based on the traffic sensor data. The experiments show that the model outperforms the other baseline methods. Furthermore, based on the ride-hailing data from DiDi Chuxing, [PERSON] et al. [36] applied the same DCRNN approach to predict the evolution of urban traffic by capturing the spatial-temporal dependencies of the urban road network, which presents good performance even under extreme weather conditions. This work contributes to the current literature by transforming the bus network into a series of graphs and integrating diffusion convolution into a classic RNN prediction framework to capture the spatiotemporal relationships between different bus lines in a bus transit network. The diffusion convolution process captures the impacts of distance decay in a series of spatially correlated vertices in a network, thereby enhancing the performance of bus passenger flow prediction.