Bike-Share Demand Prediction using Attention based Sequence to Sequence and Conditional Variational AutoEncoder

Abstract

In recent years, bicycle sharing services (bike-shares) have been established worldwide. One important aspect of bike-share management is to periodically rebalance the positions of the available bikes. Because the bike demand varies by and over time, the number of bikes at each bike-port tends to become unbalanced. To efficiently rebalance a bike-share system, it is essential to predicting the number of bikes in each bike-port. In this paper, we propose a method to predicting bike demand and the number of bike pickups and drop offs at each bike-port every hour, up to 24 hours in advance. To predict demand, we used a time series generation model based on the Variational Autoencoders model and the Attention based Sequence to Sequence learning model. We named this method "Conditional Variational Autoencoders considering Partial Future data" (CVAE-PF). In the experiment, our proposed method showed higher prediction accuracy in root mean square error (RMSE) compared to conventional methods.