# Short-Term Travel Demand Prediction of Online Ride-Hailing Based on Multi-Factor GRU Model
## 1 Introduction  
In the era of big data, the use of big data in the transportation industry is becoming more extensive. Online ride-hailing has become popular across the country and is an important means of transportation for people because online ride-hailing is more convenient and flexible than bus and metro for passengers. The travel demand prediction of online ride-hailing has great significance for the development of online ride-hailing [1, 2, 3, 4]. Deep learning is a new research direction in the field of machine learning, which is now widely applied in the field of transportation. Through multi-layer processing, the initial \"low level\" features are gradually transformed into \"high level\" features, and the complex learning tasks can be completed with simple models. In 1994, [PERSON] et al. [5, 6] looked into this problem in depth, and found some fairly fundamental factors that make training recurrent neural networks (RNNs) very difficult. Then, in 1997, [PERSON] et al. [7] proposed a long short-term memory (LSTM) neural network, a special RNN, which solves the problem of long-term dependence of RNNs and can learn long-term dependence information. In recent years, in the field of transportation, many scholars have used the LSTM model to predict traffic flow. [PERSON] et al. [8] proposed a spatio-temporal traffic flow prediction method combining K-nearest neighbor (KNN) and LSTM. KNN is used to select the adjacent stations that have the closest relationship with the test stations to capture the spatial characteristics of the traffic flow. LSTM is used to mine the temporal variability of the traffic flow, and a two-layer LSTM network is used to predict the traffic flow of theselected stations. [PERSON] et al. [9] proposed a method to improve LSTM by capturing these high-impact traffic flow values using an attention mechanism. At the same time, some data beyond the normal range were smoothed to obtain better prediction results. The results showed that the prediction model has certain competitiveness in short-term traffic flow prediction. The gate recurrent unit (GRU) can also be seen as a variant of LSTM, as their underlying concepts are similar and produce equally impressive results in some cases. [PERSON] et al. [10] used the data of Didi and Conv-LSTM to forecast the travel demand of online ride-hailing. Reasonable prediction results can provide data support for vehicle scheduling and distribution, solve problems such as energy waste and traffic congestion caused by asymmetry between supply and demand, and maximize the benefits of passengers, drivers, and ride-hailing platforms. In 2014, [PERSON] et al. [11] proposed the GRU model, which consists of a two-layer RNN. [PERSON] et al. [12] used LSTM and GRU to forecast vehicle traffic flow with GPS sampling data in the road network. The results showed that the LSTM and GRU methods have better prediction performance than the existing autoregressive integrated moving average (ARIMA) methods. [PERSON] et al. [13] proposed a short-term traffic flow prediction model combining spatiotemporal analysis and GRU. The prediction results of the proposed model were compared with the actual traffic flow data to verify the effectiveness of the model. [PERSON] et al. [14] combined the transfer learning method to solve the problem of insufficient online traffic data, adopted the particle filter online training algorithm to reduce the training time complexity, and used the GRU model to achieve accurate prediction of satellite network traffic. Many factors, such as weather conditions, morning and evening rush hours, and holidays, can affect travel. Therefore, combining various factors to forecast the traffic flow can make the prediction result more accurate [15; 16; 17]. [PERSON] et al. [18] used GRU model to predict traffic flow and combined weather factors to make the prediction results more accurate. [PERSON] et al. [19] used different deep-learning models to forecast pedestrian travel in hazy weather. [PERSON] et al.