# Forecasting Taxi Demands Using Generative Adversarial Networks with Multi-Source Data
## 9 Conclusions  
Accurate taxi demand forecasting can solve the traffic congestion problem caused by the supply-demand imbalance. Although many methods have been successfully employed to address the taxi demand prediction problem, most existing methods may not comprehensively consider various factors that influence the forecasting results. To fill the gap, we propose a deep learning-based model for forecasting taxi demands in the urban area by considering multi-source data.  
Various factors have been considered, including trips factors, temporal factors, spatial factors, weather conditions, and POI. Firstly, in the proposed model, significant factors are extracted from raw data and then analyzed to understand the influences of these factors on taxi demands. Pick-up locations of Taxi trips are derived from taxi GPS trajectory, combined with temporal factors, weather conditions, POI data, and road network data. All information is then integrated to explore the travel pattern of taxi demand and its related influences.  
Secondly, the extracted factors are prepared for use in the forecasting model. Normalization, transformation, and concatenation were employed. Thirdly, the Generative Adversarial Networks (GANs) structure is introduced, followed by a training process setting. The convolutional recurrent neural network model. In the proposed model, the LSTM network is adopted as the generator according to its stability. The convolutional neural network (CNN) is employed to distinguish whether the input data is real or generated by the generator. Finally, comprehensive experiments are performed on real-world datasets. The proposed model can automatically learn various characteristics to understand spatiotemporal patterns and enhance forecasting performance. For proving the xxx of predictive accuracy, the proposed model is validated and compared with several benchmark algorithms, including the ARIMA, XBoost, MLP, GAN_LSTM, and GAN_CNN on the real-world data of Wuhan city.  
The results show that our model outperforms the other prediction approaches in the measurements of MAPE, RMSE, MAE, and time-consuming.  
The evidence from findings proves that considering a deep learning-based approach and considering spatial-temporal, weather, road network correlations in models can significantly improve predictive accuracy. The results can assist policymakers in regulating the taxi industry to enhance the temporal taxi supply and the taxi lease rents (which vary by shift) for specific periods, which may improve passengers' degree of satisfaction and improve the transportation capacities of the taxi industry cities.  
For future work, there is a need to investigate the impact of other similar types of traffic demands such as online car-hailing services (for instance, didi and ober), which lead the taxi demands to be reduced. Moreover, the spatial correlation between city areas would be considered and then forecast taxi demand by graph-based deep neural networks.  
[PERSON] designed, developed the methodology, collected and analyzed the data; [PERSON] supervised the work and provided analysis tools; [PERSON] wrote the paper; [PERSON] performed data curation and visualization; T.L. project administration. All authors have read and agreed to the published version of the manuscript.  
This study was supported by Foundation of Nanyang Institute of Technology (grant no.: 510102), Projects of Henan Provincial Department of Science and Technology (no. 212102310297).  
The authors declare no conflict of interest.