## 1 Introduction  
To address the evolving needs and challenges in urban and rural traffic systems, new mobility services have been developed in recent years. Characterized by flexibility and convenience, a range of services have been established, ranging from bike- and carsharing to ride-hailing and ride-pooling (RP) [1]. While the former involves the individual booking and independent use of a bicycle or private car, ride-hailing entails booking a vehicle with a driver for a specific route without additional passengers. RP builds on this concept by allowing other passengers to share the ride [2]. Based on a predefined algorithm, this app-based service pools passengers with similar destinations along a shared route into a single vehicle. In this way, individual travel costs, total mileage driven, and traffic congestion can be reduced overall, contributing to a more sustainable public transportation (PT) system [3; 4; 5]. Proponents highlight RP's potential to reduce vehicle miles traveled and address first- and last-mile connectivity issues, thereby increasing the accessibility and efficiency of the overall mobility system. However, critics caution that RP may cannibalize existing PT services by shifting demand from line-based PT to more flexible, on-demand options, potentially increasing traffic volumes and emissions [6; 7; 8]. Previous studies have examined a wide range of influencing factors, including sociodemographic, mobility-related, and psychological determinants [9].  
However, one dimension that has received limited attention is the temporal variation in RP usage, particularly during nighttime hours. In this study, nighttime is defined as the period between 10:00 p.m. and 5:00 a.m. It can be assumed that travel behavior differs significantly between daytime and nighttime due to increased safety concerns and the reduced availability of alternative transport modes [3; 10; 11; 12]. This gap in research is particularly relevant as RP services may offer substantial potential during nighttime, both in improving accessibility and in filling service gaps left by traditional PT. To better understand these dynamics, this study examines the case of the RP service figs in Mannheim, Germany, which complements the existing local PT network. The analysis is based on survey data collected from Mannheim residents during January and February 2024. This is the first study in Germany that combines stated preference and revealed preference data to empirically analyze temporal differences in RP behavior, thereby contributing novel insights to the field of time-sensitive mobility research. While the methodological framework builds on established discrete choice modeling techniques, the main objective of this study lies in generating empirically grounded insights into temporal variation in ride-pooling behavior. The integrated use of stated and revealed preference data strengthens the robustness of the analysis and provides a more comprehensive understanding of user behavior in a relatively underexplored research context. The findings aim to illuminate the varying behavior of passengers at different times of the day, providing valuable insights for RP operators and urban planners to enhance the effectiveness and sustainability of RP services.  
This paper is structured as follows: Section 2 provides a comprehensive literature review on the impact of nighttime conditions on travel mode choice and mobility behavior. Section 3 outlines the methodological framework of the study, including the design and implementation of the stated choice experiment (SCE), the characteristics of the RP service figs, the data collection process, and the statistical modeling approach. Section 4 presents and discusses the empirical results. Finally, Section 5 summarizes the main findings, reflects on their implications for transport policy and planning, and suggests directions for future research.