  Bike sharing is a vital component of a modern multi-modal transportation
system. However, its implementation can lead to bike supply-demand imbalance
due to fluctuating spatial and temporal demands. This study proposes a
comprehensive framework to develop optimal dynamic bike rebalancing strategies
in a large bike sharing network. It consists of three components, including a
station-level pick-up/drop-off prediction model, station clustering model, and
capacitated location-routing optimization model. For the first component, we
propose a powerful deep learning model called graph convolution neural network
model (GCNN) with data-driven graph filter (DDGF), which can automatically
learn the hidden spatial-temporal correlations among stations to provide more
accurate predictions; for the second component, we apply a graph clustering
algorithm labeled the Community Detection algorithm to cluster stations that
locate geographically close to each other and have a small net demand gap;
last, a capacitated location-routing problem (CLRP) is solved to deal with the
combination of two types of decision variables: the locations of bike
distribution centers and the design of distribution routes for each cluster.
