# COVID-19's Pandemic Effects on Bike Sharing Systems: A New Reality for Urban Mobility?
## 2 Materials and Methods
Due to limitations in data availability, we used a reduced number of explanatory variables. The main variable of interest was a dummy variable (Lockdown 23 March-4 May 2020) which captures the general quarantine lockdown and all the restrictions imposed due to the COVID-19 pandemic in Greece (it should be noted that on 4 May 2020 the Greek government announced the gradual de-escalation of restrictions that lasted until July 2020, including the partial lifting of movement restrictions and not the total lifting of restrictions). This variable takes the value of 1 during the quarantine lockdown period, i.e., the weeks between 23 March and 4 May 2020 and zero otherwise. Additionally, we included an alternative dummy variable (Lockdown 23 March-forward), which takes the value of 1 during the quarantine lockdown period and beyond, i.e., all the weeks after 23 March 2020 even after the easing of restrictions on 4 May, and zero all the weeks before that date. We assumed that quarantine lockdown was positively related to the performance of bike-sharing systems, increasing the number of rides during the quarantine period and beyond. Therefore, we expected a positive sign for both variables, implying that the quarantine lockdown led to an increased use of rental bicycles. Through this approach, an increase in the BS system would be proven to be a conscious choice of the users and not a random incident. In order to examine the trips' duration, we include the average duration of the rides (Duration) per week, expressed in minutes. Although the trips' duration could be associated with the number of rides, we do not have a priori expectation on the sign of this variable. Finally, taking into account the form of bike-sharing systems and renting infrastructure within each city, the number of rental stations (Stations) was included in the set of regressors. The automatic bike-sharing system of the municipality of Igoumenitsa consists of three rental stations, the municipality of Rhodes has seven rental stations while the municipality of Chania had four rental stations until January 2020 which were reduced to three from February 2020 onwards. Since BSS bicycles were rented and returned by the users to a rental station, we expected a positive effect of the number of available rental stations on the use of BSS bicycles. Our analysis consists of panel data that contains observations collected at a regular frequency across similar units (i.e., cities in Greece). The use of one panel data model instead of three separate time series models has the advantage of containing more information, variability, and more efficiency than pure time series data. This occurs because panel data models allow for heterogeneity across units (i.e., cities), namely, allowing for any or all the model parameters to vary across cities whereas time series models assume that the model parameters are common across cities. Furthermore, the idea of developing three discrete (each for a city examined in the framework of this paper) models was examined but not followed as the restriction measures applied were the same for all of Greece and for the same time period and concerned similar BS systems under the same administrator; thus, it was decided that one model should be developed covering all three cities. Table 1 presents the descriptive statistics and specifically the values of the observations as provided by the administrator. These values were examined and the marginal values were excluded by the model's developing process, although they represent real cases (for example, the minimum value regarding the average duration in minutes is recorded in one of the three BS systems and refers to a case in which the system failed to properly record the duration of the user's trip).