# Estimating Destination of Bus Trips Considering Trip Type Characteristics
## 6 Conclusions  
This study aimed to develop a methodology for estimating non-tagged alighting stop information gradually, by considering the characteristics of trip types and utilizing transportation card data. We classified four trip types based on the continuity and recurrence of trips and developed the methodology with a trip-chain-based estimation method that considers the characteristics of individual trip type, an estimation method based on the recurrent travel patterns and non-recurrent travel patterns for each person (card ID), and finally, route patterns for building 100% valid data. The methodology proposed in this study was applied gradually to transport card data of the Seoul metropolitan area, and the estimation accuracy was examined. The results of trip type classification indicated that in approximately 67.68% of the cases, travel information existed on a similar day as the alighting information-missing trip or the next day while the trip was determined to be temporally and spatially continuous from the previous trip. Therefore, preliminarily, it was valid to apply the trip-chain-based alighting stop estimation methodology. According to the alighting stop estimation results, the accuracy (less than 1 km) of the alighting stop estimation for all samples was approximately 85.00% based on a scenario where the alighting tag data in bus trips were 100% missing, and the usable valid tag rate was close to 100%. In short, the alighting stop estimation's accuracy and valid tag rate indicated significant improvements compared to those of previous studies.  
In some trips, the estimation of alighting stops was impossible based on only a single methodology, such as the trip-chain-based estimation and pattern-based estimation methodologies. In this study, we classified the trip types by analyzing the characteristics of transportation card data to build 100% complete data. Furthermore, this study differed from previous studies in that it built more complete bus trip data by applying the stage-by-stage alighting estimation model. However, on weekends, when trips tend to be less recurrent, the accuracy of estimating alighting stops was analyzed to be relatively lower compared to weekdays. This was also the case for non-continuous non-recurrent travel and route patterns for the same reason. Therefore, further studies are necessary to improve the accuracy of alighting stop estimation for weekend trips. It is also necessary to conduct additional studies to apply and review more various estimation methods such as AI-based alighting stop estimation methodology to improve the alighting estimation accuracy for non-recurrent travel. This should lead to an improvement of the current public transport OD methodology, and the discovery of public transportation policies and services.  
Conceptualization, S.L., S.C. and J.L.; methodology, S.L., S.C. and J.L.; formal analysis, S.L., S.C. and J.L.; validation, S.L., B.B. and D.N.; data curation, S.L., S.C. and J.L.; writing--original draft preparation, S.L., B.B. and D.N.; writing--review and editing, S.L., B.B. and D.N.; supervision, B.B., D.N. and S.C. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 21 TLRP-B148671-04). This paper is supported by Inha University.  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.