Rental Prediction in Bicycle-Sharing System Using Recurrent Neural Network

Abstract

As the rapid development of smart city and Internet of Things (IoT), related research issues have attracted much attention from industry and academia around the world, and Bicycle-Sharing System (BSS) is one of the thriving applications of smart transportation system. BSS is a system that allows users to rent the bicycle from any automatic rental station. If there're some stations that don't have enough bicycles or free places, then it is usually handled by dedicated vehicles to rebalance the bicycles. Thus, predicting the rental (i.e. the number of renting or returning bicycles) from users in the future is important to improve the service quality. This research uses Recurrent Neural Network (RNN) to predict the rental from users. The RNN consists of three parts: period, closeness, and general. Each of them represents the historical records in different time intervals in the past time respectively. After inputting the historical rental data into RNN and the training process, we can predict the bicycle rental in the coming day by inputting the rental records of the past time into RNN. Finally, we compare the effectiveness among this and the method of Poisson by real YouBike data and prove that our model outperforms it.

Keywords

• Bicycle-sharing system
• rental prediction
• recurrent neural network
• deep learning