Predicting bike sharing demand using recurrent neural networks

Predicting bike sharing demand can help bike sharing companies to allocate bikes better and ensure a more sufficient circulation of bikes for customers. This paper proposes a real-time method for predicting bike renting and returning in different areas of a city during a future period based on historical data, weather data, and time data. We construct a network of bike trips from the data, use a community detection method on the network, and find two communities with the most demand for shared bikes. We use data of stations in the two communities as our dataset, and train an deep LSTM model with two layers to predict bike renting and returning, making use of the gating mechanism of long short term memory and the ability to process sequence data of recurrent neural network. We evaluate the model with the Root Mean Squared Error of data and show that the prediction of proposed model outperforms that of other deep learning models by comparing their RMSEs. (C) 2019 The Authors. Published by Elsevier B.V.