## 1 Introduction  
Bus transport is a critical component of the transportation system. With the significant progress of urbanization, buses are becoming the leading force in public transportation. For example, Beijing has one of the most crowded bus networks at present. According to the statistics of Beijing Public Transport Corporation, in 2020, there were 1207 bus lines (including suburban lines) with a total length of 28,400 km. The volume of the average daily passenger in Beijing has far exceeded a person-time of 5 million, and the total annual passenger volume has reached a person-time of 1.9 billion [1]. Passengers' behavior can be understood by analyzing smart card data [2]. The large quantity of data collected by smart cards offers more detailed characteristics in the time and space dimension than any other types of data. To improve the bus service quality, an accurate and proactive passenger flow prediction approach is necessary [3, 4]. Availability of smart card data has offered more opportunities for the prediction work [5]. The prediction results can help the bus operators optimize resource scheduling and save operating costs as well as assist passengers in making better decisions by adjusting their travel paths and departure time. Furthermore, this approach is useful for the government to assess risk and guarantee public safety.  
There are two main fields of study in passenger flow prediction, namely time series models and machine learning methods. Most time series models are designed based on the autoregressive integrated moving average model (ARIMA) [6, 7, 8]. However, time series models only predict different states for a single target, such as the number of passengers at a specific bus stop at different times. When predicting multiple targets for the whole traffic network, this kind of method maintains various models for different objects. As for the machine learning methods, they convert the time series into a supervised learning problem, solved by machine learning algorithms [9].  
Passengers' chief travel destinations are closely related to daily work and life, such as work areas, residential quarters, markets and tourist attractions. The smart card data can be applied to analyze the passenger flow characteristics between different POI locations. From the above point of view, a PFP-XPOI model was investigated in this study for the prediction of bus passenger flow. The main contributions of this study are as follows:  
* A novel bus passenger flow prediction model is proposed. The model takes the predicting accuracy and the predicting efficiency into account. The model improves the dimensionality of bus IC card data by fusing POI, so that large-scale low-dimensional data have more feature representation, which ensures the accuracy of prediction. The XGBoost algorithm has the advantage of fast operation, contributing to reducing the total training time of the passenger flow prediction model for multiple lines to achieve the goal of efficient training.
* Extensive experiments were conducted on historical passenger flow datasets of Beijing. After preprocessing the original data and matching the POI data, the XGBoost algorithm can be used to build a unified prediction model for different stations of the bus line, which can effectively improve the training efficiency of the model. In addition, comparison with the existing methods verifies the practicability and effectiveness of the proposed model.  
In the following, Section 2 reviews literature on bus passenger flow prediction methods. Section 3 elaborates the proposed method in detail, covering data processing and modeling. The prediction results and relevant discussion are given in Section 4. Finally, Section 5 concludes this paper.