# Forecasting Taxi Demands Using Generative Adversarial Networks with Multi-Source Data
## 7 Discussions  
In this study, we try to investigate taxi demands and related factors extracted from multi-source data. A generative adversarial network (GAN) is adopted to forecast taxi-passenger demand by considering significant factors, including temporal, spatial, and external factors. Using GANs, the experiments showed that overall performance is very good compared to similar methods. Comparing this study to the study by [2; 5], we highlight several similarities and dissimilarities. Regarding their methodology and results, in particular, this study has several differences and innovations. Firstly, a comprehensive experiment was conducted by GANs using multi-source data collected for the city of Wuhan in China, which covers all variables associated with taxi demand. The obtained variables include time, Is rush-time, Is weekend, Is holiday, Region, temperate, Weather, Wind, Humidity, and POI counts. In contrast, key variables were not included in the methods of [PERSON] et al. 's study [2] and [PERSON] et al. 's study [5], such as the POI counts, Is rush-time, Is weekend, Is holiday, Region, temperate, etc. These variables undoubtedly play a vital role in forecasting the taxi-passenger demand, and some of these variables were significant according to the results of this study. Not surprising that these factors are considered significant in forecasting taxi-passenger demand. It conforms to the findings of a previous study [16; 18; 29]. Secondly, [PERSON] et al. 's study [2] adopted the Convolutional Neural Network (CNN) to study contributing factors to taxi demand, but this study adopted a generative adversarial networks model. Three kinds of GANs have been adopted, namely the GAN_LSTM_CNN model, GAN_LSTM model, and GAN_CNN model. The results comparison indicated that the GAN_LSTM_CNN model outperformed the GAN_LSTM model and the GAN_CNN model in MAE, RMSE, and sMAPE introduced in Figures 18-20. It shows that the GAN_LSTM_CNN model can present a more comprehensive view of the impact of significant factors on taxi demands. Moreover, this study presents an analysis of the Training Loss Function among the GAN_LSTM_CNN model, GAN_LSTM model, and GAN_CNN model. It is important for considering computing consumed time during the Training process. The discriminator in GAN_LSTM_CNN shows that the model may learn better and faster, which can improve the efficiency and effectiveness of taxi demand prediction. Finally, although the training time consumed by GAN_LSTM is considerably higher than those in GAN_CNN and GAN_LSTM_CNN, even though the trainable parameters are the lowest number. In addition, GAN_LSTM_CNN results in higher time consumption during training and testing processes than in GAN_CNN. This is reasonable as the training parameters in GAN_LSTM_CNN are 22 times bigger than those in GAN_CNN. The findings of this study indicate that our hypothesis is correct. The results highlighted the vital role which deep learning-based methods can play in improving the accuracy of taxi demand forecasting. And this can help policy-makers regulating the taxi industry to enhance the temporal taxi supply during specific periods, which in turn can increase taxi profit. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Parameters** & **Training (for Each Epoch)** & **Testing (for Each Batch)** \\ \hline GAN\_LSTM & 8320 & 233.3 s & 1.5 s \\ GAN\_CNN & 443,003 & 50.9 s & 1.2 s \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of Time Efficiency. Although the used dataset contains various vital variables, there are three limitations found during this study. The first is that more datasets can be collected for a more extended period, such as a year or two years. It may enhance the results and may provide a more accurate understating of taxi demands. The second limitation is the dataset used in this study is a historical dataset, not a real-time dataset.