###### Abstract  
This paper uses a neural network approach transformer of taxi driver behavior to predict the next destination with geographical factors. The problem of predicting the next destination is a well-studied application of human mobility, for reducing traffic congestion and optimizing the electronic dispatching system's performance. According to the Intelligent Transport System (ITS), this kind of task is usually modeled as a multi-class problem. We propose the novel model Deep Wide Spatial-Temporal-Based Transformer Networks (DWSTTNs). In our approach, the encoder and decoder are the transformer's primary units; with the help of Location-Based Social Networks (LBSN), we encode the geographical information based on visited semantic locations. In particular, we trained our model for the exact longitude and latitude coordinates to predict the next destination. The benefit in the real world of this kind of research is to reduce the customer waiting time for a ride and driver waiting time to pick up a customer. Taxi companies can also optimize their management to improve their company's service, while urban transport planner can use this information to better plan the urban traffic. We conducted extensive experiments on two real-word datasets, Porto and Manhattan, and the performance was improved compared to the previous models. intelligent transport system; geographical factors; location base social networks +
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many other exciting applications, for instance, targeted advertising based on destination, Location-Based Social Networks (LBSNs) [4], intelligent traveling schedule [5], and reducing travel costs, and energy consumption optimized scheduling strategies. Recently visited locations strongly influence the next location prediction of a trajectory [6]. Similarly, a trajectory's final destination prediction is strongly connected to existing neighboring locations [2]. In the literature, past traffic data prediction problems have been extensively studied in terms of travel time, taxi demand, and traffic flow volume. In traffic data prediction, the first applied time series prediction methods and machine learning methods include Support Vector Machine (SVM) and Autoregressive Integrated Moving Average (ARIMA) and its components [7], respectively. However, the drawback of such methods is they ignore the spatial correlation data. In recent years, deep learning, which has achieved great success in natural language process [8] and computer vision [9], for traffic prediction has been widely used [10]. Many researchers combine the Convolution Neural Network (CNN) with the Recurrent Neural Network (RNN) and to find temporal [11] and spatial correlations [9]. In traffic data prediction [12], the models merging CNN and RNN and their versions are also extensively used. In these combined methods, the spatial features are extracted by CNN first, then the temporal features by RNN, and finally the forecast is made. For taxi preallocation systems, we believe the passengers' destination information is critical. Without considering the passengers' destinations distribution, the taxi preallocation systems based solely on the forecasted taxi destination demand will suffer the following issues:  
* There are city management rules that limited traffic, for example Beijing's driving restriction policy [13]. Some drivers are allowed to driving in some specified regions. Suppose a region is assigned to a taxi driver where most passengers are going to a place. In that case, that place is restricted to taxi drivers. In such circumstances, he/she cannot take an order, resulting in a waste of resources. * To carry the passengers, some taxi drivers prefer passengers going to their familiar regions. Meanwhile, some taxi drivers are unwilling to take short trip orders for a small profit. If most passengers' destination are too close to the pick-up location or the region is outside of his/her operating regions, the driver may reject those requests.