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Moment-based Probabilistic Prediction of Bike Availability for Bike-Sharing Systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationProceedings for the 13th International Conference on Quantitative Evaluation of SysTems (QEST 2016)
Subtitle of host publicationQuébec City, Québec, Canada August 23-25 2016
PublisherSpringer, Cham
Pages139-155
Number of pages16
ISBN (Electronic)978-3-319-43425-4
ISBN (Print)978-3-319-43424-7
DOIs
StatePublished - Aug 2016

Publication series

NameLecture Notes In Computer Science
PublisherSpringer, Cham
Volume9826
ISSN (Print)0302-9743

Abstract

We study the problem of future bike availability prediction of a bike station 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 with contribution propagation method is proposed to prune the PCTMC to make it only contain stations which have significant contribution to the journey ows to the target station. The underlying probability distribution of the available number of bikes is reconstructed through the maximum entropy approach based on the derived moments. The model is parametrized using historical data from Santander Cycles, the bike-sharing system in London. In the experiments, we show our
model outperforms the classic time-inhomogeneous queueing model on several performance metrics for bike availability prediction.

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