# Forecasting Taxi Demands Using Generative Adversarial Networks with Multi-Source Data
## 1 Introduction  
Taxis play a vital role in the modern urban transportation system as they comfortably and conveniently serve many urban passengers. According to the annual report of urban passenger transport operation [1], more than 216 million passengers used a taxi service in 2020 in Wuhan city. However, a critical challenge emerged that there is a significant mismatch between the supply of taxis and passengers' demands. For instance, passengers may not find taxis for an extended period in an area at a specific time. In contrast, taxi drivers may cruise roads without getting passengers in another area at the same time. Therefore, this may lead to several problems, such as increasing passengers' wait time and oil consumption and decreasing taxi incomes. To this end, it becomes significant to accurately predict fine-grained taxi demands in advance to guide taxi drivers to areas with high demands [2].  
There is a need for a deep understanding of the temporally varying taxi-passenger demand over various spatial areas that can guide and motivate drivers to be in the areas with a high potential of passenger demands and thus enhance the taxis' utilization rate [3].  
Recently, there has been much research investigating the correlation between taxi demands and related dependencies. However, taxi demand forecasting is still an open problem, which is mainly affected by several kinds of factors [4; 5; 6]:  
1. Temporal factors include weekdays, weekends, rush hours, holidays.
2. Spatial factors such as neighboring areas.
3. External factors including POI data, weather, traffic condition, etc.  
In this study, we try to study taxi demands and related factors extracted from multi-source data. The taxi demands, represented by taxi pick-up events, were analyzed usinga generative adversarial networks (GAN) to forecast taxi-passenger demand by considering significant factors, including temporal, spatial, and external factors. A novel deep learning-based approach is proposed for feature extraction and fine-grained taxi demands forecasting in urban areas.  
The main contributions of this article include the following:  
1. To provide a method for extracting features related to taxi demands from multi-source data following a detailed analytic for the relationship between taxi demands and significant factors.
2. The generative adversarial networks (GAN) model is adopted to perform taxi demand forecasting. In the GAN, the long short-term memory (LSTM) structure is selected in the generative network, whereas the Conventional network is used in the discriminator network to enable the GAN model to deal with fundamental factors and related patterns naturally.
3. The proposed forecasting model was evaluated using a real multi-source data set collected of Wuhan city, China. The experiments proved that the proposed model showed better results and outperformed other state-of-the-art methods.  
The rest of the paper is structured as follows: Section 2 presents a related review of the recent taxi demand prediction methods. Section 3 introduced the mathematical definition of taxi demand forecasting. Section 4 introduces the proposed methodology, followed by explaining the conducted experiment in detail in Section 5. The experimental results are discussed in Section 6. Section 7 concludes the implications and value of taxi demand forecasting findings and introduces the future work.