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Moment-based Availability Prediction for Bike-Sharing Systems

Research output: Contribution to journalArticle

Original languageEnglish
Number of pages38
JournalPerformance Evaluation
Early online date28 Sep 2017
DOIs
StateE-pub ahead of print - 28 Sep 2017

Abstract

We study the problem of predicting the future availability of bikes in a bikestation through the moment analysis of a PCTMC model with time-dependent
rates. Given a target station for prediction, the moments of the number of
available bikes in the station at a future time can be derived by a set of moment
equations with an initial set-up given by the snapshot of the current state of
all stations in the system. A directed contribution graph is constructed, and a
contribution propagation method is proposed to prune the PCTMC so that it
only contains stations which have signicant contribution to the journey
flows to
the target station. Once the moments have been derived, the underlying probability
distribution of the available number of bikes is reconstructed through the
maximum entropy approach. We illustrate our approach on Santander Cycles,
the bike-sharing system in London. The model is parameterised using historical
data from Santander Cycles. Experimental results show that our model outperforms
a time-inhomogeneous Markov queueing model with respect to several
performance metrics for bike availability prediction.

ID: 44197450