# Smart Recommendations for Renting Bikes in Bike-Sharing Systems
## 2 Related Work
The objective was to minimize the number of imbalanced stations, which the authors solved heuristically. Prices (which include incentives) were established for O-D station pairs. [PERSON] et al. [25] presented a mechanism that incentivizes the users in the bike repositioning process; they employed optimal pricing policies using regret minimization in online learning, and investigated the incentive compatibility of their mechanism. This approach was evaluated through simulations based on historical data from the Boston Hubway system, as well as on data collected via a survey study with MVGmeinRad in Mainz, Germany. [PERSON] and [PERSON] [26] proposed a hybrid approach for a free-floating bike-sharing system in which user discounts (60-100%) are applied when the imbalance is low (less than 15%), or trucks are used in case of high imbalances. The decision is updated once for each of five predefined time slots during the day. Imbalances are measured as the difference between the current and a desired fleet distribution. The latter is calculated for the entire day from historical data. [PERSON] and [PERSON] [27] proposed a repositioning strategy that assumes that there is a number of voluntary riders during each time period. Trips (including bike repositioning by volunteers) are considered for time periods of 30 min. The authors used integer programming to solve two optimization problems: first (1) the \"ideal bike inventory\" for each station and time period (with unlimited voluntary riders), and then (2) the voluntary rider flow (O-D pairs), so as to obtain an inventory as close to the \"ideal\" as possible. [PERSON] et al. [28] analysed Bike Angels--the incentive program of New York City's Citi Bike system. Angels are users who earn points and rewards for helping to rebalance the system. The authors analysed the Bike Angels program and proposed several offline and online incentivizing policies, one of which has been adopted by Citi Bike. [PERSON] [29] designed a raffle-based incentive system in which users can gain tickets for taking bikes from surplus stations and returning them at shortage stations. According to [30], this type of incentive is more effective than micro-incentives (e.g., small discounts). [PERSON] and [PERSON] [31] studied homogeneous BSSs and calculated the optimal fleet size to minimise the proportion of problematic stations (empty or full). They concluded that simply returning bikes to non-saturated stations does not produce a sufficient impact on the system behaviour. However, the mere incentive of suggesting users to return bikes at the \"worst\" among (only) two stations (chosen at random) improves the system dramatically, even if only some of the users follow the recommendations. [PERSON] et al. [32] proposed a solution that combines rebalancing (with trucks) and incentivising users with rewards and prizes, as this combination has a lower cost than using trucks alone. Using a finite Markov birth-death process, their approach was to minimise the average expected time for which stations are empty or full. The authors were only interested in the overall effect of incentives on the system, rather than the effects of specific types of incentives. [PERSON] and [PERSON] [33] proposed a bidirectional incentive model where two types of users are considered--namely, commuters and leisure travellers. The users' travel behaviours are characterised as _peak_, _flat peak_, or _inverse peak_ travel. The goal of this system is to persuade commuters to move from _peak_ to _flat peak_ behaviour, and leisure travellers from _flat peak_ to _inverse peak_. This change in behaviour is incentivised by charging more to commuters or rewarding leisure users, respectively. Subsidies from the government are also included in their strategies. User behaviour models are also relevant to static rebalancing approaches [22]. In [22], the behaviour of users when arriving at empty stations was simulated, i.e., whether they leave the system or walk to another station.