Traffic prediction in a bike-sharing system based on hierarchical time series

Abstract

Bike-sharing systems are widely deployed in many major cities, providing a convenient transportation mode for citizens’ commutes. As the rents/returns of bikes at different stations in different periods are unbalanced, the bikes in a system need to be rebalanced frequently. Real-time monitoring cannot tackle this problem well as it takes too much time to reallocate the bikes after an imbalance has occurred. We propose a hierarchical prediction model to predict the number of bikes that will be rent from/returned to each station cluster in a future time interval so that bike reallocation can be executed in advance. We first propose a bipartite clustering algorithm to cluster bike stations into groups, formulating a 2-level hierarchy of stations. Then a H̲i̲erarchical T̲ime S̲eries (HiTS) prediction model based on an Input-output Hidden Markov Model (IO-HMM) and Gaussian Processes (GPs) is proposed to predict the check-out, from which the check-in of each cluster can be easily inferred. We evaluate our model on a real bike-sharing system in New York City (NYC), named Citi Bike, confirming our model’s advantage beyond baseline approaches, especially for anomalous periods, i.e. those under rare weather conditions.

Date of Award	2016
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology