Bike-sharing systems have become an established reality in cities all across the world and are a key component of the Smart City paradigm. However, the unbalanced traffic patterns during rush hours can completely empty some stations, while filling others, and the service becomes unavailable for further users. The traditional approach to solve this problem is to use rebalancing trucks, which take bikes from full stations and deposit them at empty ones, reducing the likelihood of system outages. Another paradigm that is gaining steam is gamification, i.e., incentivizing users to fix the system by influencing their behavior with rewards and prizes. In this work, we combine the two efforts and show that a joint optimization considering both rebalancing and incentives results in a higher service quality for a lower cost than using simple rebalancing. We use simulations based on the New York CitiBike usage data to validate our model and analyze several schemes to optimize the bike-sharing system.

中文翻译：

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结合动态再平衡和用户激励的共享单车优化框架

共享单车系统已成为世界各地城市的既定现实，是智慧城市范式的关键组成部分。但是，高峰时段的不平衡交通模式可能会完全空置一些车站，而另一些车站则会填满，从而使更多用户无法使用该服务。解决这个问题的传统方法是使用再平衡卡车，从满站取走自行车并将其存放在空站，从而减少系统中断的可能性。另一个正在流行的范例是游戏化，即通过奖励和奖品影响他们的行为来激励用户修复系统。在这项工作中，我们将这两项努力结合起来，并表明与使用简单的再平衡相比，考虑再平衡和激励的联合优化可以以更低的成本获得更高的服务质量。