## 5 Results
### Online Taxi-Hailing Demand Forecasting  
Then, we forecast the online taxi-hailing demand in Bell Tower area based on the BPNN and XGB. We test the prediction effects of different indicators based on the BPNN and XGB. In the experiment, we add time factors, environmental factors, and taxi demand factors into models based on BPNN and XGB. Models with different impacting factors are shown in Table 6. Next, we use the grid search algorithm to adjust the hyperparameters of models based on BPNN and XGB. Moreover, the hyperparameters for the models are illustrated in Table 7. Furthermore, the results of models with different impacting factors are shown in Figures 5 and 6. Additionally, the factors of \"T\", \"E\" and \"TX\" are shown in Table 5. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Information** & **Indictors** & **Description** \\ \hline \multirow{3}{*}{T} & DW & Day of the week. DW is in (1, 7). \\  & HD & Hour of day. HD is in (1, 24). \\  & WON & Workday or non-workday. Workday: 0; Non-workday: 1. \\ \hline \multirow{3}{*}{E} & WS & Wind speed (m/s). \\  & TEM & Temperature (\({}^{\circ}\)C). \\ \cline{1-1}  & RHU & Relative humidity (\%). \\ \cline{1-1}  & TG & Sensible temperature (\({}^{\circ}\)C). \\ \cline{1-1}  & VIS & Horizontal visibility (m). \\ \hline TX & TX & Taxi demand (pcu). \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Predictive indicators of trip demand. Figure 2: An instance of One-Hot Encoder. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Model Input** & **Model Output** \\ \hline Model BPNN + T & Time data & Next week’s demand \\ Model BPNN + T + E & Time data + Environmental data & Next week’s demand \\ Model BPNN + T + TX & Time data + Environmental data + Taxi data & Next week’s demand \\ Model XGB + T & Time data & Next week’s demand \\ Model XGB + T + E & Time data + Environmental data & Next week’s demand \\ Model XGB + T + TX & Time data + Environmental data + Taxi data & Next week’s demand \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Predictive indicators of trip demand. Figure 4: The demand for taxi and online taxi-hailing in Bell Tower area. Figure 3: Research scope in the Bell Tower area. Figure 5: Online taxi-hailing demand prediction results of models based on BPNN. (**a**) Result of model “BPNN + T”; (**b**) Result of model “BPNN + T + E”; (**c**) Result of model “BPNN + T + E + TX”. Then we use RMSE, MAPE and R\({}^{2}\) to test the prediction effect of the models (Table 8). Table 8 shows the RMSE, MAPE and R\({}^{2}\) of six different models' test datasets in the Bell Tower area. Comparing the performance of predictions based on BPNN, our results show that the model \"BPNN + T + E + TX\" is the best-performing method for solving online taxi-hailing prediction problems. Moreover, among three predictions based on XGB, the model \"XGB + T + E + TX\" is the best-performing method for online taxi-hailing prediction problems. Next, we analyze the contributions of the different sources of information. From Table 8, we can find that including information about taxi demand (\"TX\") enhances the prediction effects based on BPNN and XGB. In the BPNN models, including information \"E\" leads to a MAPE reduction from 0.224 to 0.190, while it decreases RMSE from 28.576 to 23.921, and increases the R\({}^{2}\) from 0.819 to 0.845. Likewise, including information \"TX\" leads to a MAPE reduction from 0.190 to 0.132, and it increases the R\({}^{2}\) from 0.845 to 0.866. Meanwhile, in the XGB models, including information \"E\" leads to a MAPE reduction from 0.333 to 0.197 while it reduces RMSE from 26.296 to 21.206, and increases the R\({}^{2}\) from 0.833 to 0.857. Including information \"TX\" leads to a MAPE reduction from 0.197 to 0.139, and it  
Figure 6: Online taxi-hailing demand prediction results of models based on extreme gradient boosting (XGB). (**a**) Result of model “BPNN + T”; (**b**) Result of model “BPNN + T + E”; (**c**) Result of model “BPNN + T + E + TX”. increases the R\({}^{2}\) from 0.857 to 0.865.