# Characterizing Situations of Dock Overload in Bicycle Sharing Stations
## 3 Methodology
### Knowledge Exploration
** The distribution of the number of extracted OMPs for each intermittence and criticality range when varying the time slot size were also analyzed. Experiments were performed for time slots ranging from two to eight hours; as a representative example, Figure 10 reports the results achieved on the _Bicing_ dataset with the 4-h time slot. Considering a courser time granularity to analyze collected data as, for example, a larger time slot size, can provide a high-level view of the station overload conditions in the bicycle sharing system. This view can be useful for end users but expecially for system managers to identify the time frames when usage conditions are critical. For instance, results in Figure 10a point out that the number of OMPs with high intermittence value (between 50-59%) is significantly higher between 8.00 a.m. and 12:00 p.m.

Domain-experts can then focus on each selected time frame to locally analyze collected data with a finer time granularity (i.e., a time slot with lower size). This latter analysis can provide more detailed information on dock overload conditions on each selected time frame. In some cases, using time slots with a larger size could smooth local intermittence and criticality peaks of potential interest. For instance, few OMPs with intermittence in the range 70-79% are mined with a 4-h time slot (see Figure 10a). Instead, when considering 1-h time slots, around 50 patterns with intermittence between 70-79% are generated in the 10:00 a.m., 11:00 a.m., 12:00 p.m. time slots (see Figure 4a). **Day category. ** Experiments have been performed to analyze the impact of the day category on the hourly distribution of intermittence and criticality. We compared the OMPs extracted by considering the station occupancy log data related to workdays with respect to those mined by considering the weekends. Results are shown in Figure 11a-d.

Extracted OMPs show a significantly different trend in weekdays and weekends. More OMPs with higher criticality and intermittence values are mined in weekdays. These OMPs are mainly located in the time period from 7:00 a.m. to 2:00 p.m. In weekends, OMPs with high intermittence and criticality values (about 70-79%) are mainly related to the period from 12:00 a.m. and 1:00 a.m. and from 7:00 p.m. to 11:00 p.m. Moreover, OMPs with high intermittence values are also mined for the 2:00 p.m. time slot. These results highlight different usages of the bike sharing system of Barcelona during the days of the week. They support the need for different actions (such as bike rebalancing actions) depending on the type of day of the week we are considering. For example, bike rebalancing actions may be more

Figure 9: Barcelona. Impact of the maximum distance threshold on the characteristics of the mined OMPs. _full-th_ = 3. _time slot size_ = 1 h.

relevant in weekdays than in weekends, and they must be scheduled in different time periods based on the day category. Figure 11: Barcelona. Characteristics of the mined OMPs related to weekdays and weekends. _full-th_ = 3. Figure 10: _Bicing_ (Barcelona). Distribution of the number of OMPs and their corresponding levels of intermittence/criticality with a time slot granularity of 4 h. _maxdist_ = 0.5 km. _full-th_ = 3. _time slot size_ = 4 h.

### Algorithm Performance

We analyzed the performance of the OMP-Miner algorithm in terms of execution time. OMP-Miner requires time both for (critical and normal) o-itemset extraction and for the consequent generation of OMPs on top of the mined o-itemsets. The o-itemsets extraction is the most computationally expensive step. With the default parameter setting, the extraction time of o-itemsets is approximately 454 s for Bicing (Barcelona) and 825 s for Citi Bike (New York), while the time for OMP generation is a few milliseconds in both cases. We also analyzed how the system parameters impact on the execution time. Specifically, we focused our analysis on the maximum distance threshold _maxdist_, which can impact significantly on the number of mined OMPs, and thus on the execution time.