A Stochastic Analysis of Bike Sharing Systems

• DOI: 10.48550/arxiv.1708.08052
• Publication Date: 2017-01-01
• Authors: Shuang Tao; Jamol Pender
• Keywords: Bike Sharing; Leverage (statistics); Central limit theorem

As more people move back into densely populated cities, bike sharing is emerging as an important mode of urban mobility. In a typical system, riders arrive at station and take if it available. After retrieving bike, they ride for while, then return to near their final destinations. Since space limited in each has finite capacity docks, which cannot hold bikes than its capacity. this paper, we study systems with stations having By appropriate scaling our stochastic model, prove central limit theorem empirical process the number $k$ bikes. The provides insight on variance, sample path dynamics large scale systems. We also leverage results estimate confidence intervals various performance measures such proportion empty stations, full circulation. These have potential inform operations design future