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Bike-sharing is becoming increasingly popular as an urban traffic mode whileincreasing the affordability, flexibility, and reliability of interconnectedpublic transportation systems (i.e., interconnected light rail, buses,micro-mobility, and ride-sharing modes of transportation). From the consumersperspective, 1) finding a bike station in convenient locations where demandusually occurs and 2) the availability of bikes at rush hours with a lesserprobability of encountering empty docks (for fixed-station bike-share systems)are two key concerns. Some stations are more likely to be empty or full,reflecting an imbalance in bike supply and demand. Accordingly, it is essentialto understand a bike-share system's demand pattern to select the optimallocations and reallocate bikes to the right stations to increase theutilization rate and reduce the number of unserved customers (i.e., potentialdemand). The Capital Bikeshare in the Washington DC Metropolitan Area is one ofthe prominent bike-share systems in the USA - with more than 4,300 bikesavailable at 654 stations across seven jurisdictions. This study provides asystematic analysis of a bike-sharing system's Capital Bikeshare system usagepattern. Our study intends to create an optimization strategy formulated as adeterministic integer programming for reallocating bike stations daily andrebalancing the bike supply system. From an operational perspective, such astrategy will allow overnight preparations to answer the rush-hour morningdemand and during special events in Washington D.C.

Station Reallocation and Rebalancing Strategy for Bike-Sharing Systems: A Case Study of Washington DC