# Characterizing Situations of Dock Overload in Bicycle Sharing Stations
## 1 Introduction  
In recent years, municipalities have fostered alternative ways of public transportation in order to reduce pollution and traffic congestion [1; 2; 3; 4; 5]. Bicycle sharing systems [6; 7] are a notable example of eco-friendly transportation systems, where citizens can rent bicycles on a short-term basis. Bikes are retrieved from stations spread throughout the city and each station has a maximum capacity as it is equipped with a fixed number of docks. Citizens can rent a bicycle parked at any station and return it to any other station with free docks. However, to achieve a satisfactory user experience, system managers should carefully monitor the level of occupancy of the stations. For example, if a station is frequently overloaded at peak hours, then a re-balancing action should be scheduled in order to move some of the parked bicycles to any station located in the neighborhood. In case the problem is more severe, managers may decide to expand the station to fit the increasing demand. Stations aregeo-referenced and equipped with sensors to constantly monitor their level of occupancy. Each station tracks the occupancy levels of its docks, thus providing geo-referenced time series data. These data acquired from stations can be collected and stored in a unique repository and analyzed by means of machine learning and data analytics techniques. Automating the process of analysis of the acquired occupancy level data is particularly appealing to computerize the planning of maintenance activities as well as giving targeted recommendations to the system users [8]. This work presents a novel exploratory data-driven methodology, named _B_ike Station OvErLoad AnalJzer (BELL), which analyzes the occupancy levels of the stations of a bicycle sharing system. The aim is to identify situations of dock overload in multiple stations which could lead to either service disruption or low customer satisfaction. For example, when all the docks in a station are occupied, users have to move to a nearby station to park their bike. By gathering insightful information regarding occupancy levels of multiple stations, domain experts can effectively apply targeted actions in order to avoid and/or limit the unpleasant situations described above. For instance, the mobile application of the system may recommend alternative nearby stations with free docks. Furthermore, the maintenance service may re-balance the number of bikes in each station thus avoiding overloaded conditions. For this reason, the proposed methodology would allow us to improve user experience in using the service. In the BELL methodology occupancy level data acquired from the geo-referenced stations are analyzed to discover a new type of pattern, called Occupancy Monitoring Pattern (OMP). OMPs describe in a concise way situations of imbalance in the occupancy levels of spatially correlated stations. Specifically, OMPs model two complementary dock overload situations: (i) Situations in which a set of stations are overloaded in an alternate fashion (hereafter denoted as intermittent situations); and (ii) Situations in which the docks of a set of stations are frequently overloaded at the same time (hereafter denoted as critical situations). To consider the spatial correlation between the occupancy level of different stations, spatial constraints can be enforced to represent groups of nearby stations in OMPs (i.e., stations within a limited geographical distance). Intermittent and critical situations are treated separately because they cause disservices with varying degrees of severity for end users. Specifically, intermittent situations indicate an imbalance in station usage which could be addressed by proposing alternative nearby stations to end users or by periodically repositioning the bicycles in the neighborhood. Conversely, critical situations indicate that a given area is temporarily inaccessible for parking bikes because all the stations in the area are in a dock overload situation.