Short-Term Demand Prediction of Shared Bikes Based on LSTM Network

Abstract

Shared transportation is widely used in current urban traffic. As a representative mode of transport, shared bikes have strong mobility and timeliness, so it is particularly critical to accurately predict the number of bikes used in an area every hour. In this paper, London bike-sharing data are selected as a data set to primarily analyze the impact of meteorological elements and time factors on bike-sharing demand. At the same time, it is important to use LSTM neural network models and popular machine learning models to predict demand for shared bikes at an hourly level. Through data analysis and visualization, the major elements affecting the bike-sharing demand are found to include humidity, peak hours, temperature, and other elements. The root mean squared error of the LSTM model is 314.17, the R2 score is as high as 0.922, and the error is small in comparison to other machine learning models. Through the evaluation indicators, it can be seen that the LSTM model has the smallest error between the prediction results and the true values of the compared machine learning methods, and the change trend of the model prediction result curve is basically consistent with the actual result curve.