Network-wide bike availability clustering using the college admission algorithm: A case study of San Francisco Bay area

The significant increase in the use of bike sharing systems (BSSs) causes imbalances in the distribution of bikes, creating logistical challenges and discouraging bike riders who find it difficult to pick up or drop off a bike at their desired location. We investigated this issue by finding the network-wide availability patterns and how these patterns evolve temporally using a novel supervised clustering algorithm based on the College Admission and the K-median algorithms. The proposed approach models the clustering problem as a matching problem between two disjoint sets of agents: centroids and data points. This new view of the clustering problem makes our algorithm a multi-objective algorithm where the impurity and distance in each cluster are minimized simultaneously. The proposed algorithm showed promising performance when applied to BSS data for the San Francisco Bay area. The resultant network-wide availability patterns were used to identify imbalances in the BSS. Using a spatial analysis of these imbalances, we propose potential solutions for decision makers and agencies to improve BSS operations and make it more stable.