Short-term Passenger Flow Prediction of Subway based on Neural Network LSTM Algorithm

Abstract

The present study investigates the application of the Long Short-Term Memory (LSTM) neural network algorithm for short-term passenger flow forecasting in subway systems. The dataset, sourced from Hangzhou subway stations, records passenger flow at 10-minute intervals over 30 days, distinguishing between weekdays and weekends. The LSTM model was trained using 80% of the available data, while the remaining 20% was reserved for testing purposes. Results showed the model achieved a mean squared error (MSE) of 19.33 on training data, indicating a good fit. However, the test data MSE was significantly higher at 460.78, suggesting overfitting. This implies the model captures training data patterns well but struggles with generalizing to new data. The findings underline the potential of LSTM networks in handling long-term dependencies and gradient vanishing issues inherent in traditional Recurrent Neural Networks (RNNs). Despite these advancements, the study highlights the need for model refinement to address overfitting and improve generalizability. Future research should focus on data augmentation, model enhancement, and regularization techniques to develop more robust prediction models. Accurate short-term predictions of passenger flow play a crucial role in optimizing train scheduling, resource allocation, and overall service quality within urban rail transit systems.