## 1 Introduction  
High-speed railway (HSR) networks are significantly expanding on a global level. The length of HSRs was about 44,000 km in 2017, increasing by more than 30% to about 59,000 km in 2022 [1]. The HSR network in Korea has also expanded to about 893 km since it opened in 2004, as shown in Figure 1[2], and the number of passengers has increased from 37,394 thousand persons in 2009 to 89,420 thousand persons in 2019 [3]. HSRs are used for operations of at least 250 km/h by UIC [4], but in Korea they have a maximum speed of over 300 km/h. In response to increasing demand, projects to build several HSR lines have been undertaken by the Korean Government. The impacts of these projects need to be evaluated using preliminary feasibility studies to secure part of the national budget, and the impacts are quantified based on long-term demand-forecasting results. The standard procedure for long-term demand forecasting in preliminary feasibility studies is included in a guideline published by the Korea Development Institute, which is a national research institute [5]. According to the guideline, the long-term demand for  
Figure 1: The HSR network in Korea [2]. HSR is predicted based on the premise that the current trip patterns will be maintained in the future. Therefore, the calibration process to simulate the current trip patterns is one of the most important steps in the long-term demand forecasting procedure. However, the guideline does not provide a specific methodology for calibration; therefore, analysts apply a trial-and-error approach to correct the error between the observed volume and estimated volume using a trip assignment model. This approach causes a large amount of time loss in long-term demand-forecasting analysis. Therefore, it is necessary to develop a time-efficient long-term demand-forecasting algorithm. A variety of machine learning (ML)-related algorithms have been used to improve accuracy and time efficiency. In railway demand forecasting, ML is mainly focused on short-term forecasting. Short-term demand forecasting estimates the daily/hourly demand and establishes train operation plans. It aims to improve service quality and to reduce operating costs by adjusting operation plans. The most recent research on short-term passenger flow prediction has used the long short-term memory (LSTM)-based model or a combined model to capture the spatiotemporal features of historical passenger flow data in Chinese urban railway networks [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. [PERSON] et al. proposed a novel deep-learning-based passenger flow (inflow and outflow) volume prediction approach using graph convolutional neural networks to capture spatiotemporal dependencies [17]. [PERSON] et al. proposed a sequence-to-sequence model with an embedded attention mechanism to predict the alighting passenger volume at each Singapore metro station in the near future given the boarding passenger volume at each station in the last few short-term periods [18]. [PERSON] et al. predicted the weekly periodicity and nonlinearity characteristics of short-term (5 min) ridership using a support vector machine (SVM) combined online model at two stations of the Nanjing Metro [19]. [PERSON] et al. forecasted short-term passenger flows in the urban rail network of Paris with incomplete data based on dynamic Bayesian networks [20]. [PERSON] et al. proposed a novel hybrid model Wavelet-SVM to predict different kinds of passenger flows (high frequency and low frequency) in the Beijing subway [21]. [PERSON] and [PERSON] developed a hybrid EMD-BPN forecasting approach that combines empirical mode decomposition (EMD) and backpropagation neural networks (BPNs) to predict short-term passenger flow in Taipei's rapid transit [22]. In addition, several research works have predicted abnormal or irregular congested passenger flow [23; 24; 25; 26], and a few scholars have established models to predict short-term HSR demand.