## 2 Relevant Literature  
Urban trip distribution estimation methods are divided into three categories: field survey methods (the traditional approach), counting sensor methods, and methods based on location-oriented technologies [12; 13]. Though temporal and spatial patterns could be explored using the traditional method, such data sets are not always enough to measure microscopic travel behavior [14]. In the counting sensor method, urban traffic rates are estimated by sensors and traffic cameras, which are limited options due to high installation and maintenance costs [15]. In general, these models tend to have computational complexity. Methods relying on location-oriented technologies include mobile data, social networking inputs, Bluetooth tracking data, and locations registered by the Global Positioning System (GPS) system [16; 17; 18]. Data types related to the third category have been used in various studies. Some studies have used mobile phone data to discover the general spatial pattern of the population [19]. [PERSON] et al. [20] used mobile data and demographic data to explore the spatiotemporal pattern of population in Rome. Using large mobile data, [PERSON] et al. [21] and [PERSON] et al. [22] examined the spatiotemporal pattern of population movement in China. However, the problem with this type of study is that people do not desire to share their location because of their privacy [23]. In recent years, intelligent transportation systems have been including GPS-equipped taxis to alert and enhance transportation management. The route data, including location and speed, sent by moving vehicles are stored in a data center. Utilizing real-time data systems to manage traffic make it possible to monitor urban traffic and make predictions about probable congestions [24]. The data acquired from GPS-equipped taxis have been utilized in a few cases in current studies with geographical purposes. In the past few years, taxi GPS data have been studied as a suitable way to explore the patterns of movement of people in different studies [25; 26]. [PERSON] and [PERSON] [27] examined the urban movement using taxi-induced data in the Chinese city of Nanjing, which clearly distinguished between city centers and suburbs. [PERSON] et al. [28] speculated the purpose of traveling and identified the traveling patterns from the taxis trajectory data in Shanghai. The study approximates the Shanghai residents' journey. [PERSON] et al. [29] proposed a method to draw out passenger pick-up and drop-off locations, discovering the spatial movement pattern of the Nanjing population using taxi data. [PERSON] et al. [30] have spatially-temporally modeled the population of Bojnourd using data from the origin and destination of taxis and discovered the movement pattern of the population. The results of the study showed that the method used to model the population at the neighborhood level worked well. Some studies have also examined the movement pattern during the coronavirus pandemic. [PERSON] et al. [8] predict the spread of an epidemic using a framework based on machine learning and movement data and the spatiotemporal effects of an epidemiological model. The comparison of trained epidemic models with actual data showed the good performance of the model. In general, it can be acknowledged that in most of the researches, the movement pattern of people has been identified using taxi position data, and the parameters affecting the movement pattern of people have been paid less attention to predict the pick-up and drop-off locations. Recently, several methods have been developed to study the patterns of city movement by using taxi origin and destination data. Spatial data clustering is one of the most largely used methods, which is generally used for a group of similar objects. Exploratory analysis is a suitable method for displaying and describing spatial distributions, discovering spatial relationship patterns, and identifying spatial clusters. These methods show local and global spatial autocorrelation and spatial heterogeneity [31].