## 2 Related Work  
Bus passenger flow prediction has been a popular research topic in recent years. Generally, approaches to this topic can be divided into parametric and non-parametric methods. In parametric methods, the ARIMA model has been applied successfully [10]. A pioneering paper [11] introduced ARIMA into traffic prediction. Later, many variants of ARIMA were proposed by combining modes in passenger flow, especially in terms of time. Different seasonal autoregressive integrated moving average (SARIMA) models were tested, and the appropriate one was chosen to generate rail passenger traffic forecasts in [6]. The SARIMA time series model was chosen to forecast the airport terminal passenger flow in [7]. Other methods were further combined with ARIMA by some researchers. A hybrid model combining symbolic regression and ARIMA was proposed to enhance the forecasting accuracy [12]. Fused with a Kalman filter, the framework consisting of three sequential stages was designed to predict the passenger flow at bus stops [13]. These methods assumed that the data change only over time, so they relied heavily on the similarity of time-varying patterns between historical data and future forecast data, ignoring the role of external influences. It would be complex for these approaches to train a specific passenger flow forecasting model for every station in a certain line. Non-parametric models, represented by machine learning methods, were also utilized for predicting traffic characteristics. Machine learning methods have been gaining popularity due to their outstanding performance in mining the underlying patterns in traffic dynamics. The support vector machine (SVM)-based approach can map low-dimensional data to a high-dimensional space with kernel functions. The complexity of the computation depends on the number of support vectors rather than the dimensionality of the sample space, which avoids the \"dimensionality disaster\". Hybrid models connecting the classical ARIMA and SVM were built in [14], which performed better than the use of a single model. A model combining the advantages of Wavelet and SVM was presented to predict different kinds of passenger flows in the subway system in [15]. These SVM-based methods had satisfying passenger flow forecasting performance. A well-known machine learning paper [16] showed that machine learning methods dominate in terms of both accuracy and forecasting horizons. Methods based on deep learning were also applied to passenger flow prediction. [PERSON] et al. proposed a deep learning-based architecture integrating the domain knowledge in transportation modeling to explore short-term metro passenger flow prediction. [17]. The real-time information was taken into consideration in passenger flow prediction based on the LSTM [18]. An improved spatiotemporal long short-term memory model (Sp-LSTM) based on deep learning techniques and big data was developed to forecast short-term outbound passenger volume at urban rail stations in [19]. The XGBoost algorithm is one of the core algorithms in data science and machine learning. XGBoost is an improved CART algorithm. The results of the XGBoost algorithm in a Kaggle machine learning competition were introduced in [20]. [PERSON] explained why XGBoost wins every machine learning competition in his master's thesis [21]. [PERSON] et al. predicted short-term traffic flow using XGBoost and compared its accuracy with that of SVM [22]. [PERSON] et al. trained XGBoost to model express train preference using smart card and train log data and achieved notable accuracy [23]. Mass data are an important condition for the algorithm to function. The availability of big data sources such as smart card data and POI provide a perfect chance to produce new insights into transport demand modeling [24]. Smart card records, the transactions of passengers in the public transit network, are a valuable source of urban mobility data [25]. In order to ensure the prediction accuracy, it is vital to increase the dimensions of bus smart card data.