Abstract
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.
Highlights
The relatively reliable schedule adherence of railway systems is one of the most attractive merits of the mode for the railway passengers [1]
This paper develops a railway dwell time estimation model using support vector regression (SVR), random forest (RF), and multiple linear regression (MLR) methods
Smart card-based passenger information is matched against the real-time train operation data from the Shinbundang line (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
Summary
The relatively reliable schedule adherence of railway systems is one of the most attractive merits of the mode for the railway passengers [1]. This paper utilizes data from real-life operations of trains in the Seoul Metro, and the smart card-based passenger information, for estimating dwell times for each station of a metro line. The initial dataset for analysis was prepared according to the method suggested by Hong et al [10] based on the smart card passenger information and archived real-time train operation data This dataset includes the number of trains operating and their current status with respect to their nearest station as one of. It is noted that the time associated with boarding, alighting, and transfer refers to the time when the passenger checks in or out with the gates at stations In this paper, both archived real-time train operation and smart card-based passenger data from the Shinbundang line (S Line) on 31st October 2017 was used.
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