## 5 Conclusions  
Based on IC card data of Beijing buses, this study addressed the bus passenger flow prediction problem via fusing POI data by using the XGBoost algorithm. The proposed  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**On-Board Passenger** & & \\
**Prediction Models in** & **RMSE** & **MAE** & **R-Squared** \\
**Line 685** & & & \\ \hline PFP-XPOI & 4.92 & 4.53 & 0.890 \\ XGBoost & 5.76 & 4.74 & 0.849 \\ LSTM & 5.32 & 4.66 & 0.871 \\ SVM & 6.09 & 4.90 & 0.831 \\ Historical Average & 5.53 & 4.92 & 0.861 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation values of different models for on-board passenger prediction in line 685.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**On-Board Passenger** & & & \\
**Prediction Models in** & **RMSE** & **MAE** & **R-Squared** \\
**Line 56008** & & & \\ \hline PFP-XPOI & 7.84 & 7.32 & 0.912 \\ XGBoost & 8.79 & 8.16 & 0.889 \\ LSTM & 8.69 & 8.12 & 0.892 \\ SVM & 8.89 & 8.25 & 0.887 \\ Historical Average & 8.96 & 8.34 & 0.885 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation values of different models for on-board passenger prediction in line 56008.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**On-Board Passenger** & & & \\
**Prediction Models in** & **RMSE** & **MAE** & **R-Squared** \\
**Line 56008** & & & \\ \hline PFP-XPOI & 7.43 & 6.98 & 0.931 \\ XGBoost & 8.06 & 7.52 & 0.919 \\ LSTM & 7.49 & 7.13 & 0.929 \\ SVM & 7.96 & 7.48 & 0.921 \\ Historical Average & 8.12 & 7.65 & 0.917 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation values of different models for alighting passenger prediction in line 56008.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**On-Board Passenger** & & & \\
**Prediction Models in** & **RMSE** & **MAE** & **R-Squared** \\
**Line 56008** & & & \\ \hline PFP-XPOI & 7.84 & 7.32 & 0.912 \\ XGBoost & 8.79 & 8.16 & 0.889 \\ LSTM & 8.69 & 8.12 & 0.892 \\ SVM & 8.89 & 8.25 & 0.887 \\ Historical Average & 8.96 & 8.34 & 0.885 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation values of different models for on-board passenger prediction in line 56008.  
method takes advantage of the accuracy ensured by POI generated from bus operation data and the efficiency guaranteed by the XGBoost algorithm. Through the XGBoost algorithm, the big data of the bus card can be merged with the POI data. After calculating the experimental data, we chose \"300 m\" as the query radius because the prediction outcome is the most accurate. Due to some new added features, the PFP-XPOI model can improve the dimension of smart card data by fusing the POI data. By comparison and verification, it was proved that the proposed model has higher accuracy and runs faster.  
This work may be further strengthened in other fields. The modeling of multiple buses arriving and leaving a single bus station would require more in-depth analysis. In the future, we will explore the applications of the proposed method in intelligent transportation system comprehensively.  
Data curation, W.L. and Y.R.; Formal analysis, Q.O.; Investigation, W.L. and Q.O.; Methodology, W.L.; Supervision, Y.L.; Writing--original draft, W.L.; Writing--review and editing, W.L. and Y.R. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China (61872036).  
The data is available through a partnership with BPTC and is not publicly available.  
The authors declare no conflict of interest.