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

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Speaker: Cheng Feng. See abstract below.

What
  • Lab Lunch
When Jun 21, 2016
from 01:00 PM to 02:00 PM
Where Mini Forum 2
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Abstract:


I will talk about 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 are 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 flows 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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