## 3 Methodology
### Stated Choice Experiment Design  
The SCE was structured around three scenarios, each featuring three situational variations, resulting in nine stated choice tasks per respondent. In each situation, respondents had to select the alternative they personally found most attractive. As illustrated in Figure 2, the first scenario captured mode choice decisions for daytime travel, while the third scenario focused on nighttime travel (10:00 p.m.-5:00 a.m.). The second scenario involved an intermodal setting, featuring a combination of PT (bus and rail) and the RP service figs. However, in order to ensure comparability between the daytime and nighttime scenarios, each of which exclusively reflects unimodal choices, the intermodal scenario was excluded from the present analysis.  
A preliminary question assessed participants' ability to drive at night (e.g., considering factors such as regular alcohol consumption during nighttime activities or general fatigue), which served as a filter for displaying the bicycle or private car (highlighted in _blue_ in Figure 2). To reduce complexity and prevent participants from being overwhelmed by too  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Category** & **Day and Night** & **Daytime-only** & **Non RP Users** & **Germany** \\  & **RP Users** & **RP Users** & **(n = 271 162\%)** & **(2017 [37])** \\ \hline
**Sociodemographics** & & & & \\ Female & 50.0\% & 51.4\% & 51.7\% & 49\% \\ Average age [years] & 42.9 & 51.6 & 51.3 & 44.4 \\ Individual with mobility impairments & 14.4\% & 32.6\% & 10.5\% & 11\% \\ Employed & 58.9\% & 60.1\% & 64.3\% & 47\% \\ Student & 10.0\% & 2.1\% & 5.2\% & 2\% \\ Retired & 20.0\% & 30.4\% & 22.0\% & 21\% \\ Lives in Mannheim city & 90.0\% & 86.2\% & 84.8\% & – \\ \hline
**Availability of mobility tools** & & & & \\ Driver’s license & 58.9\% & 71.7\% & 84.2\% & 87\% \\ Avg. number of cars per household & 0.79 & 0.83 & 1.2 & 1.1 \\ RP use at least once per week & 46.7\% & 40.6\% & – & – \\ Public transit pass & 64.4\% & 48.6\% & 37.0\% & 18\% \\ Bicycle available & 64.4\% & 64.5\% & 75.1\% & 73\% \\ Car-sharing membership & 10.0\% & 8.7\% & 6.0\% & 3\% \\ Bike-sharing membership & 10.0\% & 10.1\% & 8.1\% & – \\ E-scooter-sharing membership & 17.8\% & 10.1\% & 8.9\% & – \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sociodemographic and mobility characteristics of RP (non-)user groups.  
Figure 2: Design of the stated choice experiment.  
many choices, taxis were excluded from the daytime scenarios. This decision was based on the assumption that taxis are more relevant at night due to the lower availability of other transport modes such as PT [23]. The _Ngene_ 1.2 software was used to design the SCE by optimizing utility functions within a D-efficient design, thereby minimizing the number of required respondents and the D-error [38]. Four question blocks were identified as the optimal setup. Travel times were constrained to reflect real-world conditions in the city of Mannheim, Germany. Typical urban trips with distances ranging from 2 to 5 km served as a reference for the scenario design. For better comprehensibility, Table 2 presents the attribute levels used in the experiment. The cost attributes in the scenarios were adjusted to account for realistic urban trip distances and fare discounts, such as those available for PT pass holders or individuals with mobility impairments.