Operating a Bicycle Sharing System over some time without the operator's intervention causes serious imbalances, which prevents the rental of bikes at some stations and the return at others. To cope with such problems, user-based bicycle rebalancing approaches offer incentives to influence the users' behavior in an appropriate way. In this paper, an event-driven agent architecture is proposed, which uses Complex Event Processing to predict the future demand at the bike stations using live data about the users. The predicted demands are used to derive situation-aware incentives that are offered by the affected stations. Furthermore, it is shown how bike stations cooperate to prevent that they outbid each other.
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