Discrete wavelet transform application for bike sharing system check-in/out demand prediction

Abstract

The rebalancing of bikes and demand prediction at the station level plays a fundamental role in the regular operation and maintenance of bike-sharing systems (BSSs). In this paper, a novel model which incorporates discrete wavelet transform (DWT), autoregressive integrated moving average (ARIMA), and long-short term memory neural network (LSTM NN), is proposed for BSS station-level check-in/out demand prediction. This study adopts the wavelet analysis method to denoise the raw BSS demand series firstly. Then, DWT is developed to decompose the denoised sequence into three high-frequency components (i.e. details) and one low-frequency component (i.e. approximation). ARIMA and LSTM are employed to forecast the detailed components and one approximation component, respectively. The predicted results of each model are reconstructed into the final outputs by DWT. An experiment on a real-world trip dataset showed that the proposed approach consistently outperforms the standard ARIMA model and LSTM model.