## 1 Introduction  
With the rapid growth of the city, urban planners face many challenges. The unmanned development of cities and populations has imposed many problems on planners at various levels. Understanding urban growth and its related factors and their impact on the city should be considered by urban planners. Urban growth is directly related to transportation and, consequently, urban traffic. Part of urban planning is a transportation planning and urban traffic [1, 2]. Lack of attention to urban planning and transportation planning leads to a lack of sustainable development [3]. Urban planning and its context are also continuous, permanent, and dynamic and cannot be dealt with in a cross-sectional, temporary, and static manner. On the other hand, understanding the pattern of transport requires understanding the movement behavior of individuals [4]. Therefore, there is a need to study the spatiotemporal growth of the urban environment and the movement behavior of individuals to achieve sustainable urban development. In an urban environment, an important part of human activity is the movement behavior of individuals, and a thorough analysis can lead to the extraction of a movement pattern. With the rapid advancement in IT and communication technologies, research about human movement in recent years has risen [5]. The discovery of movement patterns helps recognize the spatial and temporal structure of cities [6]. Understanding urban populations' movement and temporal patterns are useful in areas such as urban planning, urban management, and urban traffic modeling and forecasting [7]. Additionally, given the prevalence of the coronavirus, spatiotemporal population modeling can be used to predict epidemics. A population with a high rate of displacement can spread the disease more rapidly. In areas with high population movements, the spread of the disease is higher because the arrivals and departures are high, and residents have to travel longer distances to do their jobs [8].  
We live in a data-rich world where analyzing this big data can solve many of our problems [9]. Therefore, big data of intra-city travel can be useful in many spatial analysis. These studies have great importance in various applications such as traffic control, land use planning, and other applications [10; 11], especially in the densely populated and developing cities of Iran, which face a variety of problems. One of these developing cities facing a high growth rate is Bojnourd, the capital of the Northern Khorasan province. Few studies in Iran have explored the patterns of population movement, which necessitates this research. In addition, in past studies, less attention has been paid to predict pick-up and drop-off locations based on the parameters affecting the movement pattern. Therefore, this research has explored the movement patterns of Bojnourd's inhabitants, using taxis' origin and destination data.  
This paper initially identifies the movement patterns by data pre-processing and then segmenting the studied area into smaller pieces; [PERSON]'s I will be applied to evaluate spatial autocorrelation, and hot spot analysis will be used to investigate the spatial patterns of pick-up and drop-off locations. Then, the process of modeling the parameters influencing the population movement patterns continues to further predict taxis' pick-up and drop-off locations distribution.  
This study begins with a brief introduction justifying the necessity of the research, its implementation, and then the scope of the study areas. The following parts present the research methodology, findings, discussion, and conclusion.