# Characterizing Situations of Dock Overload in Bicycle Sharing Stations
## 3 Methodology
### Knowledge Exploration
Experiments were run by varying the _maxdist_ value while the standard configuration was adopted for the other parameters. The execution time, similarly to the number of mined OMPs, increases more than linearly with respect to the maximum distance threshold value. The time ranges from 3 min when _maxdist_ = 0.1 km up to 42 min when _maxdist_=0.6 km. The execution time increases to more than one hour when values of _maxdist_ greater than 0.6 km are used, i.e., when _maxdist_ is set to values that are considered not interesting in our application domain. Most of the execution time is spent on o-itemset generation, while even in the worst case the OMP generation requires a few seconds. ## 5 Discussion

The BELL methodology analyzes historical occupancy data acquired from bicycle sharing systems with the aim of identifying situations of imbalance in dock occupancy levels of bike stations. The proposed methodology relies on an itemset-based approach, which extracts recurrent patterns from historical data and provides domain experts with a set of interpretable patterns to explore. The extracted OMPs describe the context (i.e., city area and time slot) in which a set of stations is in a critical/intermittent dock overload condition. The discovered patterns represent (i) groups of nearby stations whose slots are almost all occupied at most points of time, and (ii) groups of nearby stations among which at least one of them (but not all of them) has a high level of occupancy at most points of time (possibly in an alternate fashion). The position of this paper differs to a large extent from previous works in the literature. Specifically, (i) previous works on clustering of the stations based on their usage profiles have been unable to identify intermittent dock overload situations; (ii) studies on forecasting future occupancy levels of the stations have applied supervised techniques, while the methodology presented in this paper relies on an unsupervised technique (i.e., itemset mining); (iii) previous approaches aimed at planning re-balancing actions are complementary to the proposed work because they can be applied to a subset of stations with intermittent dock occupancy levels. The results achieved by the BELL methodology on real bicycle sharing system data have shown potentially harmful dock overload situations in the stations of bike sharing systems. Specifically, we explored the applicability of the BELL methodology in two real case studies, the Barcelona and New York bicycle sharing systems. Notably, the achieved results show behaviors peculiar to each use case. For example, in New York, the mined OMPs highlight situations of imbalance mainly due to intermittent occupancy levels (i.e., intermittence value = 100%, criticality value = 0%). This implies that, although some areas were characterized by a strongly imbalanced bike distribution among stations in certain time slots, at least one station per area had a non-critical dock occupancy in the analyzed period. Hence, planning re-balancing actions could be sufficient to counteract situations of imbalance. Conversely, in Barcelona, situations of imbalance were usually characterized by a mix of critical and intermittent conditions. Hence, re-balancing actions may not be sufficient and long-term maintenance actions (e.g., station resizing) need to be put in place to counteract the issue. The takeaways from this study can be summarized as follows:* The use of data mining tools to analyze bicycle sharing system data has become more and more attractive. * Unsupervised approaches, like the BELL methodology presented in this study, characterize system usage in the medium and long-term. They identify contexts in which user experience could worsen due to recurrent system inefficiencies. * System users may take advantage of the data-driven approaches to system monitoring because potentially critical situations can be automatically detected and managed without the need for explicit notification.