## 2 Literature Review  
In reviewing the literature on the use of LBSN data in transportation studies, [PERSON] and [PERSON], 2017 [4] noted the recent emergence of Big Data usage in transportation systems. Along with other scholars [5], [PERSON] and [PERSON] identified the reasons for this trend; i.e., that data from household surveys and roadside interviews with travelers have their disadvantages. One disadvantage is costliness which results in this type of data collection only being carried out every five--or even every ten--years. Another reason for using Big Data is the enormous increase in the amount of passively collected location data that are available. Research on the use of LBSN data for supply-demand transportation is advancing and ongoing [6], including trip generation and [7] trip distribution modeling [8], as well as trip destination selection [9]. It is clear, though, that research on the use of LBSN data for transportation system modeling is not yet comprehensive and in-depth. From the perspective of transportation planning, Big Data has the potential to describe the phenomena of interest in transport planning. As part of Big Data, LBSN data are defined as data from shared social network sites containing user locations [2]. Thus, it is necessary to study the collection method used in LBSN data gathering to obtain LBSN data from these sources. Transportation demand modeling continues to develop so that more precise and reliable estimation results are being obtained [10]. The trip production model is one of two parts of the trip generation model framework; the other part is the trip attraction model. Trip generation modeling has become a point of attention for scientists; both as a \"state of the art\" practice in relation to the meaning of the existence of the accessibility variable studied by [PERSON] et al., 2017 [11], and as state-of-practice, such as the significance of categorization of different types of land-use. The application of land-use as a variable in the Malaysian Trip Generation manual has been reviewed by [PERSON] et al., 2015 [12]. Similarly, the Trip Generation manual published by the Institute of Transportation Engineers (ITE) has also undergone review by [PERSON] and [PERSON], 2019 [13]. Trip production modeling is always performed based on data from the study area. Since the goal of this study is trip production modeling, it is necessary to know what kind of data are needed. It is important to know the variables derived from the parameters to understand appropriate labels of the data and the adequacy of the data for trip modeling production for specific travel purposes with a particular modeling approach. A zonal trip production model incorporates various factors, such as socio-demographic (population, household) and socio-economic (employment, income, vehicle ownership) variables, along with land use data from the trip-producing zone [14; 15]. Parameters in personal trip production modeling with an activity-based approach are based on various household-level factors. These factors include daily activity, household economic conditions (such as income and vehicle ownership), social characteristics of trip makers (such as age, gender, and family size), and land use of the trip production zone (including the value of land, residential density, and accessibility) [16]. With a tour-based approach, the parameters for trip production modeling can be based on household economic conditions (income, car ownership), household size and structure, travel time, and land-use in the zone [17]. [PERSON] et al., 2019 [4] state that the availability of machine learning is another reason for using LBSN data and Big Data in broad research terms, as well as data analytics [4]. Machine learning techniques aid scientists in extracting specific elements or labels from LBSN data, such as trip mode and trip purpose [18]. This enables researchers to meet the requirements of their studies more effectively.