# Smart Recommendations for Renting Bikes in Bike-Sharing Systems
## 5 Evaluation of User-Centred Recommendation
### Simulation Results  
The main aim of the recommendation strategies is to allow an efficient usage of a BSS in terms of the time the users spend to go from their origin location to their destination, as well as the unsuccessful rental and return attempts. Table 3 compares the performance of the different user-centred recommendation strategies. The measurements we present are the following:  
* #a: number of users who dropped out (abandoned) the system, and percentage with regards to users that finished;
* #fh: number of failed user rental attempts and percentage over all user rental attempts;
* #fr: number of failed user return attempts and percentage over all user return attempts;
* _tt_: average total time of users in the system; this is based on the time a user requires to go from their origin to a bike rental station, to cycle from there to a station to return the bike and, finally, to walk to their final destination. The value is averaged over all users who were able to rent a bike (i.e., who did not abandon the attempt);
* _AET_: average station empty time; this is the time for which a station has been empty (without available bikes) and, thus, would potentially have been denying service. The value is the average over all 169 stations for the whole simulation period. The first row of Table 3 specifies a hypothetical optimal situation, where every user can obtain a bike at the station closest to their origin and return it at the station closest to their destination (i.e., the station capacities for rental and return are assumed to be unlimited). In this case, the average travel time would be 19.21 min. This travel time can obviously not be obtained in a real scenario. Analysing the standard strategies (the first four strategies in Table 3), there are different conclusions:  
_SD_ has the worst behaviour. Here, users go to the closest station, and many users will abandon their search because there are no bikes available. Furthermore, the travel time is quite high, because after an unsuccessful rental or return attempt, users may try at other stations and, thus, increase the time that they spend in the system. In comparison, _ISD_ has a much better performance, because the probability of finding an available bike or slot is much higher if a user only goes to the stations that have available resources at the moment of the recommendation request. _DR_ combines the distance and availability of resources. In this way, it obtains a better dropout rate and, at the same time, a shorter travel time than _SD_ and _ISD_. It should also be noted that, here, users would slightly prefer to rent/return bikes where there are more bikes or slots (within closed stations). This means, here, users implicitly improve the distribution of the bikes (and slots) among the stations. This can be seen in the reduction of the average empty time of stations. The _DistanceExpectedResources_ strategy (_DER_) clearly outperforms the standard strategies. Compared to _DR_, _DER_ not only includes the bikes/slots in the analysis of appropriate stations that are available at the precise moment when a user issues a recommendation request, but also the expected changes up to the expected arrival of the user. In this case, the dropout rate can be reduced from 3.76% to 1.98%, whereas the average travel time remains nearly the same (21.62 versus 21.54 min). It should be noted that, with _DER_, the failed rental and return attempts are both 0. This is because with the specified experimental setting, the estimation of available bikes/slots is exact because (1) users always go to