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Bike-sharing is becoming increasingly popular as an urban traffic mode while increasing the affordability, flexibility, and reliability of interconnected public transportation systems (i.e., interconnected light rail, buses, micro-mobility, and ride-sharing modes of transportation). From the consumers perspective, 1) finding a bike station in convenient locations where demand usually occurs and 2) the availability of bikes at rush hours with a lesser probability 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 essential to understand a bike-share system's demand pattern to select the optimal locations and reallocate bikes to the right stations to increase the utilization rate and reduce the number of unserved customers (i.e., potential demand). The Capital Bikeshare in the Washington DC Metropolitan Area is one of the prominent bike-share systems in the USA - with more than 4,300 bikes available at 654 stations across seven jurisdictions. This study provides a systematic analysis of a bike-sharing system's Capital Bikeshare system usage pattern. Our study intends to create an optimization strategy formulated as a deterministic integer programming for reallocating bike stations daily and rebalancing the bike supply system. From an operational perspective, such a strategy will allow overnight preparations to answer the rush-hour morning demand and during special events in Washington D.C.