  Bike sharing has become one of the major choices of transportation for
residents in metropolitan cities worldwide. A station-based bike sharing system
is usually operated in the way that a user picks up a bike from one station,
and drops it off at another. Bike stations are, however, not static, as the
bike stations are often reconfigured to accommodate changing demands or city
urbanization over time. One of the key operations is to evaluate candidate
locations and install new stations to expand the bike sharing station network.
Conventional practices have been studied to predict existing station usage,
while evaluating new stations is highly challenging due to the lack of the
historical bike usage.
  To fill this gap, in this work we propose a novel and efficient bike
station-level prediction algorithm called AtCoR, which can predict the bike
usage at both existing and new stations (candidate locations during
reconfiguration). In order to address the lack of historical data issues,
virtual historical usage of new stations is generated according to their
correlations with the surrounding existing stations, for AtCoR model
initialization. We have designed novel station-centered heatmaps which
characterize for each target station centered at the heatmap the trend that
riders travel between it and the station's neighboring regions, enabling the
model to capture the learnable features of the bike station network. The
captured features are further applied to the prediction of bike usage for new
stations. Our extensive experiment study on more than 23 million trips from
three major bike sharing systems in US, including New York City, Chicago and
Los Angeles, shows that AtCoR outperforms baselines and state-of-art models in
prediction of both existing and future stations.
