# Smart Recommendations for Renting Bikes in Bike-Sharing Systems
## 2 Related Work  
Bike-sharing systems' management decisions can be made with different temporal horizons. Long-term or _strategic_ decisions are mainly related to system design and deployment (location and size of stations, number of bikes, etc.) [11; 12; 13; 14], as well as the analysis of different exploitation policies for existing systems [15]. At an _operational_ level, decisions are made in order to maintain a good performance of the system, which usually means maximizing the number of users that rent bikes. Thus, actions are aimed at reducing imbalances produced at stations due to heterogeneous demand for bikes and empty slots. In this regard, _offline_ or static approaches are mid-term decisions oriented at repositioning bikes using vehicles (trucks)--typically at night, or during off-peak times--in such a way that bike distribution through stations is optimal for the expected demand. These systems estimate the best way of distributing not only the existing bikes, but also the routes taken by the trucks (see e.g., [16; 17; 18]). A natural evolution of such systems is their _online_ application during the actual operation of the fleet. The challenge is in combining the current state of the system with demand prediction, so as to propose short-term balancing actions. Many works propose solutions to the problem of dynamically repositioning bikes using trucks as efficiently as possible [19; 20; 21; 22]. It has become clear that optimal solutions to the optimization problem do not scale as the size of the BSS grows, so authors use heuristics to find solutions applicable in real-world systems. Furthermore, using trucks to rebalance a bike-sharing fleet has several disadvantages, including cost of operation, contribution to congestion and pollution, etc. Recently, several approaches have proposed crowdsourcing this task, so that users can contribute by taking or returning bikes at stations other than their target station. Typically, users receive some type of incentive (e.g., discounts) to compensate the extra (usually small) effort of modifying their initial choices. This paper focuses on dynamic rebalancing systems; thus, we describe related works in this area in further detail. [PERSON] et al. [19] studied the problem of obtaining the optimal policy to determine the best action that a repositioning vehicle can take in order to maximise the number of users that find a bike. Since this is an NP-hard problem, they proposed several heuristic solutions that, roughly speaking, are based on choosing the station with the most or least excess of bikes with regard to a given target state. They also employed a pricing strategy in which each station has a price that is paid when the bike is returned, regardless of the origin of the trip; users choose the destination station based on its current price and the user's walking and biking distances. This solution consists of optimizing station prices assuming certain user models. [PERSON] et al. [23] presented a dynamic rebalancing system that combines truck-based vehicle redistribution and price incentives for users. In this approach, users either find a bike at their origin stations or leave the system. Upon arrival at their target station, users decide whether or not to accept an incentive to return the bike at a neighbouring station. This decision is taken according to their \"perceived cost\" of the additional distance travelled (biking and walking) and the incentives received. Dynamic prices are calculated through an optimization problem that combines the quality of the resulting state and the cost of paying out the incentives. The key component of the system quality is the analysis of each station's utility, based on an optimal \"fill level\" (the available bikes at a station). [PERSON] et al. [24] proposed a method that combines trucks and price incentives. In this approach, the authors intentionally tried to make some stations even more imbalanced (hubs), so that bike redistribution could be carried out more efficiently with shorter truck trips.