## 2 Literature Review
With the availability of LBSN data and machine learning, LBSN data have the potential to be a new data source in estimating travel demand, in addition to conventional data sources. Machine learning has been used in this study to automatically retrieve data from the Twitter database. Data extracted from LBSN data have the potential to supplement data from household survey results [6]. Compared to data resulting from household surveys, which are structured and designed for a particular research purpose, data from LBSN are unstructured. Utilization of LBSN data for modeling transportation is still at the research level and has not yet arrived at the application stage. Therefore, further research on the use of LBSN data for transportation demand modeling is recommended by [PERSON] et al., 2019 [8]. Data for trip production modeling should include essential criteria such as the individual's name (user account name), location and time during activity, and relevant text statements related to themselves, trip departure locations, and trip-related situations. Among the currently available LBSN platforms that record user activity locations, Twitter is an excellent source for this type of data due to the characteristics of Twitter data; hence, this study has focused on using Twitter. Twitter data have geotagging and time-stamping besides the text messages; thus, they has the potential to identify individual movement and other related information [19]. For trip production modeling purposes, this study required geotagged tweet data, timestamping, and text messages. The availability of geotagged tweet information is determined by Twitter users who share, or do not share, their locations when activating their accounts before sending or receiving messages. Users can change location-sharing settings at any time [2]. Thus, Twitter data collection has the potential to provide spatio-temporal information in the study area, as well as information on trip-maker attributes. In the process of developing a trip-production model based on Twitter data, several questions arose. Firstly, how to retrieve data from the Twitter database so that, as much as possible, Twitter user locations and time of tweeting geotagged timestamp data, besides text data, could be obtained and could represent all zones within the study area. Previous researchers used Twitter data that was not directly pulled from the Twitter database, but from a data provider instead [20]. As previously indicated, it is not easy to pull data from the Twitter database and prepare them for analysis suitable for research purposes. For the purposes of trip production modeling, the location label is important to identify the requirements according to the purpose of the model, such as home or workplace. Besides the location label, the label of the trip maker and the zone related to the trip are also important. This differs from modeling based on conventional data where labels are received directly from respondents. In contrast, modeling based on Twitter data require that labeling needs are predicted using geotagged data, the timestamp of tweet, and the tweet content [21]  
Previous Twitter data retrieving applications have been discontinued by the providers, and Twitter company gives opportunity to researcher to utilize Twitter data as long as researcher's proposal comply with the company policy. Software packages such as Sense-Place2, which can also handle analytic support for situational awareness [22], are available for Twitter data retrieving. Other software packages are also available, such as Kwitty and Twitter2 PDF; however, these are no longer being maintained, as noted by [PERSON] and [PERSON], 2015 [23]. For the purposes of this study, it was found that the most reliable and most suitable Twitter data retrieval procedures used Application Programming Interfaces (API) to retrieve data [24]. To obtain data from the Twitter database, the platform system provides three types of API accounts: Enterprise, Premium, and Standard, each with its own limitations and advantages.