# Characterizing Situations of Dock Overload in Bicycle Sharing Stations
## 3 Methodology
### Knowledge Exploration
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**OMP IDentifier (ID)** & **OMP** & **Time Slot** & \
\begin{tabular}{c} **Crit.** \\ **\%** \\ \end{tabular} &
\begin{tabular}{c} **Intern.** \\ **\%** \\ \end{tabular} \\ \hline
1 &
\begin{tabular}{c} [Aerquas de l’Argentera, \\ Avinguda del Marques Argentina) \\ \end{tabular} & [10:00 a.m., 11:00 a.m.] & 37.96 & 19.23 \\ \hline
2 &
\begin{tabular}{c} [Gran Via, Rocafort] \\ \end{tabular} & [11:00 a.m., 12:00 a.m.] & 35.94 & 19.91 \\ \hline
3 &
\begin{tabular}{c} [Gran Via, Rocafort] \\ \end{tabular} & [10:00 a.m., 11:00 a.m.] & 34.48 & 19.84 \\ \hline
4 &
\begin{tabular}{c} [Marquas de l’Argentera \\ Avinguda del Marques Argentina) \\ \end{tabular} & [11:00 a.m., 12:00 a.m.] & 33.52 & 21.15 \\ \hline
5 &
\begin{tabular}{c} [Parala lel, Pl. Jean Genet] \\ \end{tabular} & [1:00 a.m., 2:00 a.m.] & 32.64 & 25.42 \\ \hline
6 &
\begin{tabular}{c} [Parala lel, Sant Oleguer, Pl. Jean Genet] \\ \end{tabular} & [1:00 a.m., 2:00 a.m.] & 23.41 & 41.91 \\ \hline
7 &
\begin{tabular}{c} [Marquas de l’Argentera, \\ Avinguda del Marques Argentina, \\ Pl. Comercial] \\ \end{tabular} & [10:00 p.m., 11:00 p.m.] & 22.99 & 37.16 \\ \hline
8 &
\begin{tabular}{c} [Marquas de l’Argentera \\ Avinguda del Marques Argentina, \\ Pl. Comercial] \\ \end{tabular} & [12:00 p.m., 1:00 a.m.] & 22.48 & 32.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Bicing (Barcelona). Groups of stations with maximal criticality in different hourly time slots. Geographical distribution of significant intermittent and critical OMPs.Each OMP represents a group of geo-referenced stations. To support the management of the bicycle sharing system, maps can be used to highlight the city areas associated with OMPs (i.e., groups of stations) with high intermittence and criticality values. Notice that OMPs can be easily visualized on a map because they represent groups of _nearby_ stations. The extraction and visualization of OMPs including distant stations is prevented by enforcing the spatial constraint in the OMP-Miner algorithm (see Algorithm 2, line 5). For example, Figure 5(a),b show two heat maps (The heat maps have been generated by using the service provided by [PERSON] et al. [31].) of the areas of Barcelona identified by the OMPs in hourly time slot (between 11:00 a.m. and 12:00 a.m.). OMPs in this time slot represent significant intermittent and critical situations according to the results in Figure 4. In Figure 5(a),b, the color intensity of areas increases with the density of occurrence of OMPs and their intermittence and criticality values, respectively. The higher the color intensity, the more severe the disservice to end users. Figure 5(a) shows that intermittent situations are mainly localized in the city center in four distinct areas. The area with the highest intensity is centered in _Placa Catalunya_, while the other two large areas are centered in _History Museum of Catalonia_ and _La Vila Olimpica del Polbenou_ and a small area is in _Pla de Miquel Tarradell_. Figure 4: _Bicing_ (Barcelona). Hourly distribution of the number of OMP and their corresponding levels of intermittence/criticality. _maxdist_ = 0.5 km. _time slot size_ = 1 h. _full-th_ = 3. Figure 5: _Citi Bike_ (New York). Hourly distribution of the number of OMPs and their corresponding levels of intermittence and criticality. _maxdist_ = 0.5 km. _time slot size_ = 1 h. _full-th_ = 3. Instead, based on Figure 6b, critical situations are more spread over the geographical areas. The larger area in Figure 6b covers all the three main areas in Figure 6a. Moreover, three additional areas show up, two of them located on the top of the map (in the _Torre Glories_ and _El Maresme Forum_ areas) and one on the bottom (_Drassanes_ area). We also exploited heat maps to analyze the geographical distribution of OMPs mined in hourly time slot (between 11:00 a.m. and 12:00 a.m.) in New York (see Figure 7a,b). Compared to Barcelona, more areas in New York are characterized by OMPs with high intermittence and criticality values.