Forecasting the Short-Term Metro Ridership With Seasonal and Trend Decomposition Using Loess and LSTM Neural Networks

Abstract

Forecasting the short-term metro ridership is an important issue for operation management of metro systems. However, it cannot be solved well by the single long short-term memory (LSTM) neural network alone for the irregular fluctuation caused by various factors. This paper proposes a hybrid algorithm (STL-LSTM) which combines the addition mode of Seasonal-Trend decomposition based on Loess (STL) and the LSTM neural network to mitigate the influences of irregular fluctuation and improve the performance of short-term metro ridership prediction. First, the original series is decomposed into three sub-series by the addition mode of STL. Then, the LSTM neural network is employed to predict each decomposed series. Finally, all the predicted outputs are merged as the overall output. The results show that the STL-LSTM model can achieve higher accuracy than the single LSTM model, support vector regression (SVR), and the EMD-LSTM model which combines the empirical mode decomposition and the LSTM neural network.

Keywords

• Short-term metro ridership prediction
• seasonal-trend decomposition based on loess (STL)
• long short-term memory (LSTM) neural network