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This paper studies the revenue changes of the platform after the free-floating bike sharing platform adopts the monthly strategy, and obtains the best monthly subscription pricing to maximize the platform revenue. Through the comparison of user utility, we divide different types of users, depict the platform requirements, and thus find the platform revenue. We found that when the platform adopts the monthly strategy, if the monthly subscription price is too low, platform's revenue will be reduced. If the price is too high, the revenue will not change. Only in the case of a suitable monthly subscription price, platform revenue will increase. In addition, we find that the growth rate of platform revenue with the optimal pricing of monthly strategy is related to the purchase cost of bike. The lower the purchase cost, the higher the platform revenue growth rate.

The opportunistic networks and the bike-sharing systems have been attracting much research attention in these years. In this paper, the cycling trips and the buffers of bike stations are utilized to relay the large volume of data. The Markov chain is exploited to formulate and estimate the delivery ratio with the mathematical lattice model. Then, the probability-driven opportunistic forwarding (POF) scheme, which calculates the delivery potentials from the possible delivery paths, is proposed. The relay evaluations, which consider the proper sequence of cycling trips, are made to support the data forwarding decisions. Moreover, the strategies, i.e., the discard notification and the backward elimination, are exploited to drop the redundant replications of data as many as possible. The extensive simulation results show that the proposed scheme outperforms several benchmark algorithms, e.g., prophet, spray & wait, and JDER, in terms of the delivery ratio, the delivery latency, and the expected hop count. INDEX TERMS Opportunistic network, bike-sharing system, cycling trips, delivery potential.

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