# Characterizing Situations of Dock Overload in Bicycle Sharing Stations
## 3 Methodology
### Knowledge Exploration
Section 4.3 evaluates the impact of the system configuration parameters on the number of mined OMPs and on their corresponding intermittence and criticality values, while Section 4.4 reports performance evaluation in terms of execution time for the OMP-Miner algorithm. The main characteristics of the analyzed datasets are summarized in Section 4.1. The OMP-Miner algorithm was implemented by using the C language. The experiments were performed on a 2.67 GHz six-core Intel(R) Xeon(R) X5650 machine (Turin, Italy) with 32 Gb of main memory running Ubuntu 18.04 server with the 3.5.0-23-generic kernel. ### Reference Use Case Datasets

This section briefly presents the main characteristics of the two bike sharing systems considered as reference use case in this study and describes data that we have considered on the system usage. **The Bicing system in Barcelona. ** Bicing is the bicycle sharing system in Barcelona which consists of 377 stations distributed all over the city area. Stations have a fixed number of parkings, which vary from 15 to 39. A description of the service is given in [13]. To perform our analyses, the collection of measurements described in [13] have been taken into account. The acquired data (from a single operator) include 30 million records from the Bicing stations over a period of approximately a semester of service (i.e., between 15 May and 30 November 2008). Occupancy values were acquired every 5 min. **The Citi Bike system in New York. **_Citi Bike_ is the bicycle sharing system in New York which features thousands of bikes at 528 stations across New York and Jersey City. Bicycles are available 24/7, 365 days a year. More information about the system is given in [2]. To perform our analyzes, an ad hoc Web crawler was developed which downloaded and parsed the JSON data from the Citi Bike system feed to retrieve the historical occupancy data. Occupancy values were acquired every 5 min over a time period of approximately 13 months (i.e., between 23 October 2014 and 17 November 2015). **Characteristics of the collected data on the system usage. ** In both bicycle sharing systems, each station is characterized by the information on its name and geographic coordinates (latitude and longitude). Historical data on station occupancy can be collected by submitting periodical requests to the stations in the system and storing the corresponding responses. Specifically, for each station, we acquired the information on the number of free and occupied slots in different time instants within a given time window. ### OMP Characterization

In what follows, some OMPs are discussed as representative examples of the insights mined through our framework. Specifically, some top ranked OMPs with maximal intermittence and criticality values are discussed as reference cases. These OMPs represent dock overload conditions that could yield to disservices for end users in the usage of the bicycle sharing system. OMPs were extracted from the Bicing and Citi Bike datasets using a standard system configuration with _maxdist_ = 0.5 km, _full-th_ = 3, and _time period_ equal to _time slot size_ of 1 h.This configuration pinpoints a time-space granularity suitable to provide useful information to end users and system managers. For example, we set _maxdist_ = 0.5 km because bikers are (usually) more willing to move to physically closer stations if the expected destination is fully occupied. We set the time period equal to _time slot size_ = 1 h to determine more precisely sets of nearby stations that could lead to service disruption. Parameter _full-th_ has been set to 3 to represent situations when the station is (almost) full. The impact of the system parameters on the characteristics of the extracted OMPs is discussed in Section 4.3. **Example OMPs with maximal intermittence. ** The OMP-Miner algorithm generates as output a set of OMPs with various intermittence values.