Comparing Different Neural Network Models on Subway Traffic Volume Forecast

Yanfei Jiang, Lian Wen, Shaoyang Zhang, Yongli Liu

Abstract: This paper compares four prevalent neural network models (SNN, DNN, RNN, LSTM) in forecasting daily subway traffic volumes. We employ both real subway data and an artificially generated data set, the latter allowing for the calculation of a theoretical limit to forecast accuracy. The results indicate that LSTM outperforms the other models in accuracy across both data sets, while RNN displays a potential overfitting tendency. Our research contributes to the field by providing a benchmark for model accuracy and revealing unique insights about the performances of these models. The findings may aid in the efficient management of subway systems.