## 4 Results
### Variables Determining the Probability of Replacing Car Journeys with the MEVO Bicycle  
The logistic regression models were then constructed to verify the effect of independent variables on the distribution of users substituting commuting by car with the MEVO bicycle. A sequence of two hundred models were assessed in Statistica 13.1. software with all the possible different combinations of independent variables. The Wald Chi-square test was used to assess the significance of these models. The pseudo R-squared values were calculated to determine the explanatory power of the models, and the Akaike information criterion (AIC) was used to compare the relative quality of the models and to help us investigate if any variables which were omitted could have been included in the model, providing further added value. Logistic regression has been widely used to analyze the determinants of transport behavior and is the generally accepted method in similar research (e.g., [65; 66; 67; 68]). A binary variable showing whether someone substituted a car or sustainable means of transport was chosen as the dependent variable. The respondents declaring that they substituted public transport (road or rail), their private bike, or commuting on foot with the MEVO bike system were aggregated together. Thus, a value of one for the dependent variable indicated that the trip made substituted a car trip. A value of zero meant that it did not or that it substituted another type of trip, including public transport trips. The independent variables were divided into two groups: factors and covariates. The factors included the following:  
- Reason behind traveling;  
- Distance between the place of residence and the place of work;  
- Most common use of the MEVO system;  
- Gender;  
- Education;  
- Socioeconomic status;  
- Owning a driver's license;- Disposable income. The covariates included the following:  
- Year of birth;  
- Number of people in the household;  
- Number of cars in the household;  
- Number of bikes in the household. The results of logistic regression are presented in Table 9. Only the best model with all the significant variables is presented. Only a few of all the variables turned out to be statistically significant. These included the age, number of people in the household, number of cars in the household, distance from work, and gender. The results prove that for the analyzed group, the younger participants substituted their cars with MEVO rather than substituting public transport. With each year of age, people were relatively 7% more likely to opt for the substitution of transport modes. Moreover, a higher number of people in a household were correlated with a 45% chance (c.p.) of someone substituting a car with MEVO, possibly due to the lower relative availability of cars within the household as perceived by the respondent. On the other hand, a higher number of cars was correlated with a statistical increase of substituting public transport with MEVO, indicating the well-known fact that car owners tend to use their cars, and the bike-sharing system played a mostly recreational role for them. Each additional car in the household was correlated with a decrease in likelihood by a further 34%. Moreover, people living far away from their places of work more often substituted public transport with MEVO rather than people living within a 3-km radius of their place of work who, in comparison with the average respondent, commonly substituted a car. In this case, they probably were not that dependent on cars in their commutes and thus could allow themselves to choose MEVO despite its initial problems with availability. Finally, women substituted cars with MEVO 28% more often than men. Interestingly enough, the reason behind the choice of MEVO systems as a mode of transport was not correlated with the willingness to swap the car for a bike.