Dwell time is a critical factor in constructing and adjusting railway timetables for efficient and accurate operation of railways. This paper develops dwell time estimation models for a Shinbundang line (S line) in Seoul, South Korea using support vector regression (SVR), multiple linear regression (MLR), and random forest (RF) techniques utilizing archived real-time metro operation data along with smart card-based passenger information. In the first phase of this research, the collected data are processed to extract boarding and alighting passenger counts and observed dwell times of each train at all stations of the S line under the current operational environment. In the second phase, we develop SVR, MLR, and RF-based dwell time estimation models. It is found that the SVR-based model successfully estimates the dwell times within 10 s of differences for 84.4% of observed data. The results of this paper are especially beneficial for autonomous railway operations that need constructing and maintaining dynamic railway timetables that require reliable dwell time predictions in real-time.

Overcrowding in confined spaces, like inside trains, is a social issue that, in the long-term, could lead to accidents. To alleviate congestion in trains, a reduction in train headways is planned, made possible by technological advances such as the introduction of advanced autonomous trains (e.g., 60-s headway). However, simply reducing a train’s headway may be challenging because this does not consider the travel behavior of passengers who engage in the disruptive act of excessively boarding trains, thereby causing delays in their departure. Furthermore, the behavior of passengers may vary depending on an individual’s latent preferences, however, there is insufficient research that reflects these latent preferences. In this study, a survey was conducted with 971 urban rail passengers in the Seoul Metropolitan Area of Korea to identify their latent preferences using latent class analysis, resulting in a four-type classification: a congestion avoidance type, a time-sensitive type, a subway-preference type, and a type that does not prefer public transportation. Based on this, willingness to wait (WTW; i.e., waiting for the next less crowded train to reduce passenger discomfort) according to their latent preferences, was calculated. WTW was found to increase as the headway of the train was reduced. Choice modeling was conducted based on variables, including socioeconomic variables, to suggest effects related to WTW. The results indicated demand dispersion effects according to the headway reduction at peak times for various types of people. This study’s findings could be utilized to offer less crowded and safer train operations.

In Korea, decisions for high-speed railway (HSR) construction are made based on long-term demand forecasting. A calibration process that simulates current trip patterns is an important step in long-term demand forecasting. However, a trial-and-error approach based on iterative parameter adjustment is used for calibration, resulting in time inefficiency. In addition, the all-or-nothing-based optimal strategy algorithm (OSA) used in HSR trip assignment has limited accuracy because it assigns all trips from a zone with multiple accessible stations to only one station. Therefore, this study aimed to develop a backpropagation-based algorithm to optimize trip assignment probability from a zone to multiple accessible HSR stations. In this algorithm, the difference between the estimated volume calculated from the trip assignment probability and observed volumes was defined as loss, and the trip assignment probability was optimized by repeatedly updating in the direction of the reduced loss. The error rate of the backpropagation-based algorithm was compared with that of the OSA using KTDB data; the backpropagation-based algorithm had lower errors than the OSA for most major HSR stations. It was especially superior when applied to areas with multiple HSR stations, such as the Seoul metropolitan area. This algorithm will improve the accuracy and time efficiency of long-term HSR demand forecasting.