# Mode Choice Effects on Bike Sharing Systems
## 5 Results  
The 2184 observations (choice tasks) were analyzed by estimating multinomial logit models (MNL) [44] in R [45; 46], whereby BSS was chosen as reference alternative when specifying the equations for estimation. Firstly, an initial MNL was estimated by including exclusively effects of attributes from the choice experiment (see Table 2; for a documentation of this work see Ref. [47]). With reference to previous studies on mode choice [18; 19; 20; 21] effects of socio-demographics (age, gender, education, student status, car availability, PT season ticket availability), home municipality, and season (winter vs. autumn) were expected. Consequently, as recommended in methodological literature [29; 44] the initial model was sequentially built up by including these effects as alternative-specific attributes in maximum \(J\)-1 alternatives (one alternative as reference category), testing the hypotheses, and comparing the models (restricted vs. unrestricted) to omit parameters without significant effects and/or substantial improvement in the model fit. Further, it was assumed that the effects of travel time and travel costs depend on household income and the distance of the trip, and this is why corresponding continuous interactions were specified [18; 19]; however,these interactions neither had a significant effect nor made a substantial improvement of the model and thus are not presented. All analytical steps along with estimated models can be made available on request. Table 5 provides an overview on model fit between the initial model, which exclusively included effects from attributes of the choice experiment, and the extended, final model, which is presented below. The likelihood ratio-test indicates a significantly better fit for the final model, which is supported by the increase in adjusted Rho-square, and by the decrease in AIC and BIC (for evaluation of model fit indices and model comparison, please review methodological literature, Refs. [29,44]). Employing the estimated parameters in transport demand models at a later stage of the project requires a distinction between mandatory and leisure trips. Mandatory trips are those with destinations for purposes such as education, work, business, or home. Leisure trips are those with destinations such as shopping, private activities and tasks, or any leisure activities. Usually, people have more degrees of freedom in destination choice for leisure than for mandatory activities. Therefore, the trip purpose was included as alternative-specific attribute in the final model on the total sample. In addition, segregated models were estimated on a subsample for mandatory and a subsample for leisure trips. The results of all three models, the overall (total) model on all observations, and the segregated model on mandatory and leisure trips, are presented in Table 6.  
All parameters for the final model show the expected sign and reasonable differences in parameter values. For all modes (BS, PMT, and PT), the estimated parameters for travel time and time for access and egress show a negative effect. Hereby the negative effect is stronger for access and egress, which was expected as ride-times in or on a vehicle are often considered less negatively than waiting times or access and egress-times [19]. Travel costs demonstrate a negatively associated utility for all modes. In addition, parking costs and fuel costs for PMT are negative, too. For BS, the data do not support any significant difference in utility for the street type; however, with reference category arterial road, the cycleway has a higher positive estimated utility (\(\beta\) = 0.195, t-value = 1.339) and the side street has a negative utility (\(\beta\) = \(-\)0.013, t-value = 0.089). This negative utility of side streets can be explained with a detour-association in comparison to the probably more direct and thus shorter route on an arterial road.