## 5 Conclusions  
RP services, which allow passengers with similar destinations to share rides, present significant opportunities to enhance urban mobility by complementing existing PT systems and offering viable alternatives to private car use. Understanding usage patterns, particularly temporal variations between day and night, is essential for optimizing these services. This study emphasizes the importance of temporal differentiation in RP preferences, using the case of the local RP service fips, which is integrated into the PT network in Mannheim, Germany. In 2024, a survey was conducted that included both a stated choice experiment and revealed preference questions, yielding a sample of 566 valid responses. To analyze differences in mode choices between daytime and nighttime contexts, a nested logit model was estimated based on the stated choice data. The findings underscore that nighttime mobility exhibits distinct behavioral patterns, including a decreased sensitivity to travel time and cost and a greater reliance on private cars, particularly among women, most likely due to safety concerns and the limited availability of PT. Moreover, the reduced sensitivity to travel time observed in nighttime contexts suggests a potential for a more efficient and spatially optimized stop network during these periods to improve vehicle utilization and reduce operational costs. An analysis of the revealed preference data further shows that the RP service during nighttime hours mainly attracts younger users, while a substantial portion of respondents reported not being active during weekend nights. Nonetheless, the actual utilization of RP services at night remains low. This appears to be driven less by a lack of demand and more by structural limitations, such as low public awareness and insufficient service availability, as reflected in the frequency of rejected booking attempts during nighttime. This implies that low usage is not merely the result of low demand, but also of structural barriers on both the supply and information sides. Overcoming these barriers through targeted information campaigns and a strategic expansion of nighttime service capacity could substantially promote more sustainable mobility during nighttime. Overall, the results align with prior research highlighting the increasing importance of flexible mobility solutions when conventional PT is less accessible or perceived as unsafe. However, it should be noted that the survey sample is not representative of the population, which limits the generalizability of these results. Moreover, due to the recruitment strategy, particularly via the social media channels of the local PT provider rnv, the sample may overrepresent PT-affine individuals, potentially leading to a self-selection bias that influences mode preferences. In addition, the intermodal scenario, originally included for daytime settings, was deliberately omitted from the nighttime experiment to isolate time-of-day effects. This limits the findings to unimodal choices during nighttime. Future research should deepen the understanding of how nighttime-specific conditions, such as safety concerns, limited service availability, and reduced PT coverage, influence travel decisions across diverse population groups. In particular, studies using more representative samples and including intermodal scenarios could help to better capture the full range of mobility needs and evaluate the role of RP services as a complement to PT under real-world nighttime constraints. Although safety concerns were rarely reported explicitly by non-users in this study, future research should incorporate qualitative interviews to better capture implicit safety-related barriers, particularly among women, that may not be fully reflected in standardized survey responses. From a modeling perspective, integrating the estimated discrete choice models into agent-based models (ABMs) enables the simulation of individual travel decisions and their system-level impacts under varying nighttime and off-peak conditions.