# Characterizing Situations of Dock Overload in Bicycle Sharing Stations
## 2 Literature Review  
The analysis of urban data related to bicycle sharing systems has already been addressed in previous studies. Specifically, in this field, the main branches of research can be categorized as follows: (i) Grouping stations based on their usage profile [9; 10; 11; 12], (ii) Predicting future station occupancy levels [13; 14; 15; 16; 17], and (iii) Repositioning bicycles between the stations [18; 19; 20; 21; 22; 23].  
Branch (i) focuses on identifying groups of stations with different usage profiles by applying unsupervised machine learning techniques (e.g., clustering [9]). To characterize station usage, temporal features [9], spatial features [10], or a mix of the above [11] are considered. Instead of partitioning the set of stations into disjointed groups according to their common usage pattern, the methodology proposed in this study focuses on locating sets of nearby stations showing a critical or alternate usage profile (e.g., a station is overloaded, whereas the nearby station is almost empty). To the best of our knowledge, the information provided by OMPs, which is the core of the BELL methodology, cannot be obtained by any of the existing approaches.  
Branch (ii) aims at forecasting the occupancy level of a station in the near future (i.e., with a time horizon between 30 min and 2 h ahead) by applying supervised machine learning techniques (e.g., regression [13; 14; 15; 24], classification [16; 17]). Based on these predictions, a recommender system can be integrated into the mobile application of the provider to suggest the stations close to the user-specified point of interest with a sufficient number of free docks/available bicycles. Predictions are based not only on past occupancy levels but also on contextual information (e.g., meteorological data [24]). The main differences between the aforesaid works and the proposed approach are enumerated below: (i) Unlike the aforesaid approaches, this work does not address the problem of forecasting the station occupancy levels using supervised techniques. Conversely, it presents a methodology based on an unsupervised technique. (ii) In the prediction task, the aim is to forecast short-term variations in occupancy level (typically, between 30 min and 2 h ahead). Our work aims at identifying recurrent situations of imbalance in dock occupancy, which policymakers may consider for scheduling medium- and long-term maintenance actions (e.g., re-balance the number of bicycles in the stations, resize the existing stations, place new stations).  
Branch (iii) focuses on planning the re-balance of the bicycles in stations according to actual user demands (e.g., more bicycles close to parking areas and business centers or more free docks close to restaurants at lunchtime). The aim is to support providers in improving user experience. For example, in [12], the authors performed a stochastic characterization of demand to design fleet-management strategies dealing with flow asymmetries. The problem is complementary to the one addressed in this paper because detecting dock overload situations could trigger re-balance actions driven by optimization-based strategies such as [18; 22; 23].