## 2 Related Work  
Over the years, numerous works have been dedicated to enhancing the accuracy of trip demand forecasting. The first application of the trip demand forecasting is predicting trip demand based on a four-step process considering spatiotemporal factors [1]. [PERSON] et al. predicted the spatial distribution of taxi demand by presenting a method [2]. Then, he proposed a learning model considering real-time data to forecast the taxi-passenger demand's spatiotemporal distribution [3]. Next, he proposed a combination forecasting model to forecast the taxi-passenger demand's spatiotemporal distribution [4]. [PERSON] et al. forecasted the location of hotspots and tested the heat of the hotspots by presenting an adaptive forecasting method [5]. Next, [PERSON] et al. proposed a time-series method to forecast the taxi demand by mining the regulation of taxi mobile app data [6]. [PERSON] et al. forecasted the taxi demand hotspots based on social media check-ins to reduce the imbalanced supply and demand of taxis [7]. [PERSON] et al. predicted the taxi demand through three forecasting methods, respectively, based on the Markov model, Lempel-Ziv-Welch model, and ANN model [8]. Besides the GPS data and environmental data, [PERSON] et al. also considered historical traffic behaviors as an important variable in the taxi demand forecasting problem, and they proposed an LSTM method to forecast taxi demand in several urban areas [9]. [PERSON] improved the hidden Markov chain model and proposed a D-model to forecast the taxi demand [10]. For exploring the relationship between taxi and subway, [PERSON] et al. took the interaction between taxi demand and subway demand into account to explore the impacts of the interaction on the accuracy of taxi demand and proposed a taxi demand prediction method based on a neural network model [11]. [PERSON] explored the impacts of tessellation on-demand prediction effects and proposed a combination algorithm of different tessellation strategies to predict taxi demand [12]. The research above considered the impacts of the GPS data and the environmental data on prediction accuracy, but they did not take real-world event information into account. To address this problem, [PERSON] et al. mined the real-world event information from unstructured data, and they applied the machine learning method to realize taxi demand forecasting [13]. [PERSON] et al. introduced the real-time demographic data into the taxi demand forecasting method and explored the impacts of demographic data on taxi demand forecasting accuracy by a stacked denoising autoencoder [14]. [PERSON] conducted a comparison of two deep neural networks for forecasting trip demand and found that DNNs perform better than other traditional machine learning methods [15]. [PERSON] et al. presented an ensemble method consisting of the LSTM model, GRU model, and extreme gradient boosting model (XGB) to forecast taxi demand [16]. [PERSON] proposed a sequence learning method considering the historical demand to forecast trip demand [17]. [PERSON] et al. presented a multi-view spatiotemporal network framework to simulate spatiotemporal relationships and forecasted the traffic demand [18]. [PERSON] analyzed taxi requests and proposed a Bayesian hierarchical semiparametric model to forecast taxi demand [20]. [PERSON] introduced the unstructured data into a deep learning method to forecast the trip demand [21]. However, the methods above ignored the destination of passengers. [PERSON] proposed a method to forecast the taxi demand between origin-destination pairs [22]. [PERSON] introduced real-world events into the prediction method and used the data to forecast traffic demand [23]. [PERSON] et al. explored the relationship between drop-off points and pick-up points and proposed a spatio-temporal LSTM model to forecast the taxi demand [25]. [PERSON] predicted taxi demand through the QUADRIVEN method based on human-generated data [26].