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Bike sharing is a vital component of a modern multi-modal transportationsystem. However, its implementation can lead to bike supply-demand imbalancedue to fluctuating spatial and temporal demands. This study proposes acomprehensive framework to develop optimal dynamic bike rebalancing strategiesin a large bike sharing network. It consists of three components, including astation-level pick-up/drop-off prediction model, station clustering model, andcapacitated location-routing optimization model. For the first component, wepropose a powerful deep learning model called graph convolution neural networkmodel (GCNN) with data-driven graph filter (DDGF), which can automaticallylearn the hidden spatial-temporal correlations among stations to provide moreaccurate predictions; for the second component, we apply a graph clusteringalgorithm labeled the Community Detection algorithm to cluster stations thatlocate geographically close to each other and have a small net demand gap;last, a capacitated location-routing problem (CLRP) is solved to deal with thecombination of two types of decision variables: the locations of bikedistribution centers and the design of distribution routes for each cluster.

A Comprehensive Framework for Dynamic Bike Rebalancing in a Large Bike Sharing Network