Short-Term Demand Forecasting of Shared Bicycles Based on Long Short-Term Memory Neural Network Model

Abstract

Shared bicycles have strong liquidity and high randomness. In order to more accurately predict the short term demand for shared bicycles, the long short-term memory (LSTM) neural network model was used as the tool to predict, on the basis of crawling the weather characteristics data of bicycles shared by Citi Bike in New York City, and analyzing the influence of time factor and meteorological factors on the demand for bicycles. On the purpose of verify our method, the traditional RNN and back propagation (BP) neural network were compared with LSTM neural network. The experimental results show that the main factors affecting the demand for shared bicycles including temperature, holidays, seasons and morning and evening peak time periods. Compared with traditional BP neural network and cyclic neural network RNN algorithm, LSTM has high robustness and strong generalization ability. The prediction result curve is consistent with the real result curve, the prediction accuracy is the highest with 0.860 and the root mean square error is the smallest with 0.090. It can be seen that the LSTM model can be used to predict the short-term demand for shared bicycles.