## 2 Literature Review
Compared to traditional deep learning models such as ANNs, CNNs, and RNNs, Long Short-Term Memory (LSTM) networks have demonstrated superior performance, particularly in sequential datasets like time series, due to their ability to capture long-term dependencies more effectively [30]. For instance, a study demonstrated that, compared to RNN, which was limited by vanishing gradient issues, and SVR, which exhibited overgeneralization, the LSTM model yielded superior predictive accuracy across all evaluated performance metrics [31]. LSTM networks are more adept at learning long-term dependencies and have been shown to surpass models like ARIMA, SARIMA, and Space-Time ARIMA (STARIMA) in studies performed on metro systems [32]. [PERSON] et al. revealed that the LSTM model represents stochastic and nonlinear structures more effectively than the SARIMA model in their study utilizing APC data [33]. In addition, in a study utilizing smartcard data for passenger flow forecasting, the LSTM model was compared with traditional models such as MLP, SVM, and RBF, and was shown to outperform them in terms of prediction accuracy [34]. In recent years, LSTM models have been widely adopted for passenger flow forecasting due to their capability to capture sudden fluctuations and elaborate patterns in time series data [35]. As a result, deep learning models have gained broad application in time series forecasting. For instance, [PERSON] et al. employed the LSTM model for short-term passenger flow prediction in rail systems, underlining its faster convergence and greater stability [36; 37]. Similarly, [PERSON] and [PERSON] reported that a station-based LSTM time series model was capable of effectively capturing nonlinear dynamics in passenger flow forecasting [38]. [PERSON] et al. further integrated two exogenous factors--weekdays and weekends--into the LSTM deep learning model and demonstrated that including these variables considerably enhances prediction accuracy [39]. Moreover, in a comparative analysis of hourly PM2.5 levels using LSTM, GRU, and CNN-LSTM, the LSTM model outperformed the more complex CNN-LSTM structure in terms of prediction accuracy [40]. [PERSON] et al. demonstrated that the LSTM model outdoes methods such as Random Forest (RF), SVM, K-Nearest Neighbors (KNN), and Gradient Boosting (GB) in a comparative analysis using six years of data from the Wuhan-Guangzhou high-speed railway [41]. [PERSON] et al. further enhanced the LSTM model using Nadam and SGD optimization algorithms, reaching improved results in ridership forecasting [42]. Similarly to the LSTM's capacity to learn long-term dependencies and lessen the vanishing gradient problem, the Gated Recurrent Unit (GRU) model was developed, offering a more simplified architecture [43]. Some studies have indicated that the GRU is more effective in capturing sudden fluctuations, especially short-term increases and decreases [44]. A comparative analysis of LSTM, GRU, and RNN for electricity load forecasting found that GRU achieved the best overall performance [45]. However, other research suggests that the GRU does not hold absolute superiority over the LSTM; rather, it performs better only under specific data structures and scenarios [46]. While the simpler structure of the GRU facilitates faster processing, the LSTM, with its greater number of parameters, is better suited for dealing with large-scale and complex problems [47]. For instance, [PERSON] et al. (2024) compared these models across various demand patterns--such as weekdays, weekends, and holidays--and found that both architectures effectively adapt to temporal variability, highlighting their practical potential in short-term forecasting applications [48]. This study assumes that meteorological conditions may play a significant role in forecasting passenger flow. However, the existing literature provides limited research that directly examines the impact of weather-related variables on ridership in public transportation systems.