# Smart Recommendations for Renting Bikes in Bike-Sharing Systems
## 1 Introduction
Dynamic reservation schemes can be used to address such problems, but may have a negative impact on the overall capacity of a BSS. BSSs are also often perceived from the perspective of the smart cities paradigm as part of a more sophisticated, heterogeneous, intelligent transportation solution--a system of systems that integrates different modes of transport (buses, trains, private and shared vehicles, on-demand transportation services, etc.) and enables dynamic collaborations among users, such as crowdshipping [4] or crowdsensing [5,6] scenarios. In a multimodal context [7,8], real-time recommendation systems may suggest any type of appropriate means of transport (not just bikes) that fits the particular needs and preferences of a user at a particular moment. In some cases, this may alleviate the quality of service problems within a BSS due to imbalances, as mentioned above. For example, if there are no available bikes in the area of a user, the system may recommend an on-demand shared taxi service, instead of walking to another BSS station. This perspective is very appealing, and research in this direction will pave the way towards better and greener transportation services in smart cities [9,10]; however, it does not explicitly account for the agglomeration problems that reduce the quality of service at the level of the BSS. Therefore, we argue that addressing the imbalance problem in BSSs is still a relevant endeavour, as it can impact positively on the overall service level of intelligent transportation systems within a smart city. In this work, we focus on the aforementioned problems within station-based BSSs. Our first contribution is a novel recommendation strategy based on a notion of cost that combines _distance_ (between a user's origin/destination and a rental/return station) with (an expectation of) _availability_ of slots or bikes. To account for the dynamic nature of the BSS environment as mentioned earlier, instead of recommending stations that have available bikes or slots at the time a search request is made, we use demand prediction to estimate the expected probability of finding an available bike or slot at the time the user will arrive at a recommended station. In particular, we model stations as queues, and apply techniques from queuing theory for probability estimation. The second contribution of this work is an extension of the aforementioned recommendation strategy that also addresses balancing problems by combining individual objectives with social welfare. For this purpose, the system not only tries to recommend stations with a low cost for the user, but also uses demand predictions to estimate the impact of renting or returning a bike at a given station on society (potential future users). Here, recommendations prioritise stations (for rental or return actions) that combine a low cost for the user with an improved overall impact on the distribution of bikes and slots in the BSS (and, thus, for potential future users). To evaluate the proposed recommendation strategies, we used a simulation tool for station-based bike-sharing systems: Bike3S ([[https://github.com/gia-urjc/Bike3S-Simulator](https://github.com/gia-urjc/Bike3S-Simulator)]([https://github.com/gia-urjc/Bike3S-Simulator](https://github.com/gia-urjc/Bike3S-Simulator))). This simulator performs semi-realistic simulations of the operation of a bike-sharing system in a given area of interest, and allows for evaluating and testing different management decisions and strategies. In our simulations, we used real data from the BiciMAD BSS, which is operating in Madrid (Spain). The rest of the paper is organised as follows: In Section 2 we discuss related works on bicycle-sharing systems. Section 3 outlines the general functioning of a station-based BSS, and introduces the basic notation used in the rest of the paper. Section 4 focuses on station recommendation strategies that are centred on BSS users. In particular, we sketch some simple, straightforward selection strategies, before introducing our more sophisticated proposal.