## 2 Literature Review  
As passenger demand forecasting, by nature, focuses on predicting the number of passengers within a given time interval and should thus be assessed within the context of a time series analysis. However, because of the complex and nonlinear relationships in a time series, traditional statistical techniques can often fall short [7]. Various statistical and machine learning approaches have been employed in previous studies to forecast passenger volumes, including regression analysis [8], Support Vector Machines (SVMs) [9], Artificial Neural Networks (ANNs) [10], Bayesian models [11], and Gradient Boosting techniques [12]. Several researchers focusing on short-term forecasting have contributed insights into passenger flow prediction using diverse methodologies. One of the earliest studies adopting a statistical approach was by [PERSON] and [PERSON], who employed the AutoRegressive Integrated Moving Average (ARIMA) model to predict highway traffic flow [13]. Among time series models, ARIMA and its variants are widely used. [PERSON] et al. developed the Seasonal ARIMA (SARIMA) model by incorporating seasonal components in traffic data [14]. Similarly, [PERSON] et al. utilized a three-month dataset to predict daily bus passenger flow and analyzed the performance of the model by combining weekday and weekend data [15]. However, in many cases, such statistical models often yield high error rates, particularly in nonlinear passenger flows with heavy irregular fluctuations [16]. Thus, to reduce error rates and achieve better forecast accuracy, Kalman filtering techniques were also wisely used alongside ARIMA models, eliminating the noisy data [17]. [PERSON] et al. further developed the Kalman filtering model by integrating it with the Bayesian method to better predict rail passenger flow [18]. In another study, time series data from train stations were analyzed using Facebook's Prophet model, demonstrating superior performance over ARIMA in certain contexts [19]. The limitations of linear models--especially when ample training data are available--have led to the adoption of machine learning (ML) approaches [20]. [PERSON] and [PERSON] developed a revised model based on the Least Squares Support Vector Machines (LSSVM) model to forecast passenger density in metro stations in Guangzhou [21]. In addition to traditional ML methods, deep learning (DL) models offer significant advantages in understanding nonlinear structures [22; 23]. In particular, DL models yield more successful results in forecasting both forward and backward time series features [24]. DL models are known for their powerful computational capabilities and are more skilled at managing complicated functions compared to standard machine learning algorithms [25]. By utilizing multilayer network architectures and non-sequential optimization techniques that do not follow a regular order, DL models can autonomously filter out meaningful features from data at diverse abstraction levels [26]. Deep learning models have demonstrated high levels of success not only in forecasting tasks but also in complex classification problems such as image recognition. This highlights their robustness and reliability across different data domains [27]. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), two fundamental tools of deep learning methods, exhibit high performance in catching spatial dependencies [28]. RNN models deal with input sequences step by step in temporal order, maintaining a state vector at each step that represents the temporal dynamics of past information within the hidden layers. In comparison to traditional models such as ANNs and CNNs, this network architecture is particularly sensitive to short-term dependencies owing to its memory-based mechanism. However, it may face limitations in modeling long-term dependencies [29]. To address difficulties such as information loss and gradient vanishing commonly encountered in classical RNNs, LSTM networks were developed.