Short-term demand forecasting of shared bicycles based on long short-term memory neural network and climate characteristics

Abstract

Shared bicycle is an emerging industry in recent years. It is an important part of urban transportation system. Its shortterm demand forecasting is of great significance to the supply, management and allocation of shared bicycle resources. The data of shared bikes are crawled to analyse the impact of time and climate characteristics on the demand for shared bikes. The short-term demand of shared bicycles is predicted by long short-term memory neural network. The experimental results showed that the long short-term memory neural network is suitable for the prediction of shared bicycle demand, and the prediction results with climate characteristics are better than those with only time series. Applying this model to predict the short-term demand of shared bicycles can improve the configuration efficiency of shared bicycles. On this basis, it provides a basis for establishing accurate and effective shared bicycle configuration strategy.