## 5 Conclusions  
This paper develops a railway dwell time estimation model using SVR, RF, and MLR methods. In the first phase, smart card-based passenger information is matched against the real-time train operation data from the S line of the Seoul Metro in Seoul, South Korea, for extracting boarding, alighting, on-bard passenger counts, and the observed dwell times for all trains at all stations. When the train departure times were missing, an assumption of constant braking time was adopted to estimate actual/observed dwell times, since the S line is autonomously operated with minimal variability in braking times.  
In the second phase, the paper develops dwell time estimation models utilizing the extracted information from the first phase. The SVR, RF and MLR-based models were developed for dwell time estimations while boarding, alighting, and onboard passenger counts were treated as independent variables and the dwell time was set as a dependent variable. In the comparative scenarios with less than 3, 5, and 10 s of errors between the estimations and the observed values, the SVR model performed the best, with an accuracy of 67.2% in the scenario with less than 5 s of error. Then the SVR model was enhanced by including additional independent variables, including arrival times and time difference of arrivals, at 2 consecutive stations for all trains at all stations. It was found that the enhanced SVR model improved the accuracy by 20% to an accuracy of 87.6% for the scenario of less than 5 s of error. In the case of less than 10 s of error, the improved SVR model performed at 97.2% accuracy.  
This research is unique in the sense that, firstly, it extracts boarding, alighting, and onboard passenger counts using data from real-life metro operations and smart card-based passenger information for all trains at all stations on an urban metro line. Secondly, this research develops dwell time estimation models with high performance accuracies that are validated by real-life data. The results of this paper are especially beneficial for autonomous railway operation, which requires construction and maintenance of dynamic railway timetables that require reliable dwell time real-time predictions. However, this estimation model may not work well if incidents such as rolling stock failure, or big events (i.e., sports games or exhibitions) occur near metro stations that may increase the demand dramatically and present unseen data to the proposed models. Enhancing the proposed estimation models to cover not only a general commute-based operation environment, but also the situations affected by such special events, are topics of future studies.  
In this study, all of the authors contributed to the writing of the manuscript. [PERSON] designed the overall modeling framework and performed the data analysis. [PERSON]-[PERSON] helped with the data analysis and drafted the initial version of the manuscript. [PERSON] supported data acquisition and contributed to the results interpretation. [PERSON]-[PERSON] and S.K. advised on the modeling process and coordinated the overall research. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant from R&D Program of the Korea Railroad Research Institute, Republic of Korea.  
The authors thank [PERSON] and [PERSON] for their help in data processing.  
The authors declare no conflict of interest.