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Quantitative approaches for estimating user demand provide a powerful tool for engineering designers. We hypothesized that estimating binomial distribution parameters n (user population size) and p (user population product affinity) from historical user data can predict demand in new situations for distributed product service systems. Distributed product service systems allow individuals to use shared products at different geographic locations as opposed to owning them. This approach is demonstrated on a major bike-sharing system (BSS) expansion. BSSs position rental bikes around a city in docks at prescribed locations. BSS operators must predict the rider demand when sizing new docking stations, but current demand estimation methods may not be suitable for distributed systems. The main contribution of this paper is the development and application of a revealed preference demand estimation method for …