## 6 Conclusions  
Twitter data, from streaming and archive, can be retrieved using a Spyder Python-based program. Twitter archive data were obtained by using API Research, which was rich in textual data, but lacking in geotagged data, compared to the Twitter streaming data obtained when using API Standard v2. Using Twitter data extraction made it possible to obtain location and text data from Twitter users continuously, although this was limited in accordance with regulations set by Twitter. Twitter data retrieval for home-based work (HBW) trip production modeling needs unique algorithms that are based on the data needed. For HBW trip production modeling, Twitter geotagged data are important, not only for estimating location and time of trip departure and/or destination, but also for estimating labels of the trip maker's attributes and location, such as home or elsewhere. Since calculating an individual trip requires two coordinate locations, a minimal number of individual geotagged data from within the study area are needed. Twitter data retrieving using API Research does not capture enough geotagged data; thus, it is necessary to use both API Research and API Standard v2. This can be performed by first completing streaming data retrieval using API Standard v2 and obtaining a list of Twitter users within a particular study area. Archive data retrieval can then be applied, using API Research. Retrieving streaming data using multi-reference locations representing residential area within a particular zone gives more captured data than using one reference location. Intermittently running the program for an interval time, based on data occurrence, such as 20-30 min, is recommended. Instead of using data from both categories of data collection, data streaming data, and data archive, data streaming must be made with a time reference for any data identified as having same tweet content. For any tweet data identified as having the same tweet content and timestamp, but different location coordinates, thus showing inconsistent coordinates, the central location needs to be determined. For the most efficient data preprocessing, it is better to ensure there is enough data collection by using streaming data retrieving. For updating a data collection, it is recommended to retrieve data using streaming data. The Twitter data retrieving technique presented in this paper can be applied to Twitter data retrieval not only in the city of Serang, but also in other regions of Indonesia and any place in the World, according to Twitter's current data access policy and procedures, particularly for academic research. The explanation of Twitter data retrieving techniques in this paper has the potential to be widely recognized among scholars and provide researchers with more opportunities for using Twitter data in transportation demand modeling. As long as Twitter maintains an open data policy regarding its database, and Twitter data collection for research purposes is not considered a violation of personal data protection laws or the like, further research on data mining for transportation demand modeling remains widely accessible. Through the results presented in Section 3, we have demonstrated the potential and applicability of mining LBSN data for trip production modeling and its relevance to broader fields such as passenger transportation demand modeling. Our findings provide guidance for transportation system researchers and data scientists in further studies utilizing LBSN data. This study contributes to the literature of advanced research on transportation demand modeling in terms of LBSN data. The Twitter data retrieval technique used in this research is the initial part involved in modeling the production of user journeys using machine learning. Data retrieval results still need to be processed to be readable by the program developed for modeling. In order for the program to run, the following components are needed: a modeling program based on machine learning, a computer environment, and collaboration with programmers.