# Characterizing Situations of Dock Overload in Bicycle Sharing Stations
## 3 Methodology
### Data Collection, Modeling and Enrichment  
To monitor the usage of the bicycle sharing system, the occupancy levels of all the stations are acquired at different points of time and stored into an Occupancy level dataset. Collected data are then enriched with additional spatial and temporal information needed to support the subsequent data analysis phase. **Data collection and modeling. ** Given a time window \(TW\) and a set \(TS=\{t_{1},\ldots,t_{n}\}\) of points of time in \(TW\), for each station \(s_{i}\) in the system, the number of free parkings at each time \(t_{i}\in TW\) is acquired and collected in a unique repository named Occupancy level dataset (\(\mathcal{D}\)). \(\mathcal{D}\) is modeled as a relational dataset [25]. A more formal definition follows. **Definition 1** (Occupancy level dataset). : _Let \(TW\) be an arbitrary time window and let \(TS\) be a set of sampling time points in \(TW\). Let \(\mathcal{S}\) be a set of attributes, where each attribute \(s_{j}\in\mathcal{S}\) represents a different station in the bicycle sharing system. Let (\(s_{j}\), \(o_{i}^{j}\)) be an arbitrary pair denoting the occupancy level \(o_{i}^{j}\) of station \(s_{j}\in\mathcal{S}\) at a given timestamp \(t_{i}\in TS\). The record \(R_{i}\) indicates the occupancy levels of all the stations in \(\mathcal{S}\) at time \(t_{i}\), i.e., it is a set of pairs \(\{(s_{j},\,o_{i}^{j})\}\), \(\forall\ j\mid s_{j}\in\mathcal{S}\). Each record is logically identified by a Record IDentifier (RID). An occupancy level dataset \(\mathcal{D}\) associated with time period \(TW\) is defined as \(\cup_{i}\mid t_{i}\in TS_{i}\)._  
Station occupancy values are categorized into two different classes to indicate the occupancy level of the station. Specifically, the measurements indicating the number of free parkings at a station are labeled as follows: (i) Overloaded, if the number of freely available parkings is below a given occupancy threshold _full-th_, or (ii) Normal, if the number of freely available parkings is equal to or above _full-th_. The occupancy level threshold _full-th_ is an absolute value specified by the domain expert. Label Overloaded is used to denote stations with a critical occupancy level, such that end users may not find free docks for parking. Instead, label Normal is used to denote station conditions that should not cause a disservice to end users. Table 1 shows an example of an occupancy level dataset. The dataset stores the occupancy levels of three arbitrary stations (\(s_{1}\), \(s_{2}\), \(s_{3}\)) at seven points of time (\(t_{1}\)-\(t_{7}\)). The dataset contains seven records logically identified with a RID (RID\({}_{1}\)-RID\({}_{7}\)). Each record includes the occupancy levels of the three stations at a given point of time. Figure 1: The _B_ike Station Ov_ErLoad Ana_Lyzer (BELL) architecture. Notice that this study will not address the complementary problem of detecting sets of underutilized stations. However, since our proposed methodology is general, it can be straightforwardly adapted to deal with this complementary problem. **Data enrichment with temporal information. ** The analysis of station occupancy levels at different time granularities allows system managers to investigate how overload conditions evolve over time, and to identify overload conditions that frequently happen in specific time periods. To support this analysis, the occupancy level data have been enriched with a temporal information with a coarser granularity. In dataset \(\mathcal{D}\), each record includes the occupancy levels of all the stations acquired at a different point of time \(t_{i}\in TS\). Each record is enriched with an additional attribute specifying the corresponding time period \(TP\) for the point of time \(t_{i}\). In the example dataset in Table 1, records are associated with three different time periods denoted as \(TP_{1}\), \(TP_{2}\), and \(TP_{3}\). The granularity of the time period can be defined based on the target analysis.