Effects of Daytime vs. Nighttime on Travel Mode Choice and Use Patterns: Insights from a Ride-Pooling Survey in Germany
Source abstract
Abstract
Ride-pooling (RP) services, in which passengers with similar destinations share a ride, offer considerable potential for enhancing urban mobility by bridging gaps in public transportation (PT) networks and providing a convenient alternative to private car use. For the effective design and operation of such services, a detailed understanding of user preferences and usage patterns is essential. This study investigates differences in RP preferences and usage between day and night (with nighttime defined as 10:00 p.m. to 5:00 a.m.), drawing on both a stated choice experiment (SCE) and revealed preference data collected in Mannheim, Germany. The focus lies on the local RP service fips, which is integrated into the PT system. The SCE, conducted in 2024 with 566 participants, was analyzed using a nested logit model. The analysis of the SCE reveals that nighttime preferences for RP are characterized by reduced sensitivity to travel time and cost, creating an opportunity for RP operators to optimize stop network designs during nighttime hours by increasing pooling rates. In addition, it indicates a greater likelihood of private car usage at night, especially among women, likely due to safety concerns and limited PT availability. The analysis of revealed preference data provides a complementary perspective. It shows that the RP nighttime service primarily attracts younger users, while many respondents report not being active on weekend nights. However, the combination of low public awareness and limited service availability, evidenced by rejected booking requests, suggests that existing demand is not being fully captured. This implies that low usage is not merely the result of low demand, but also of structural barriers on both the supply and information side. Overcoming these barriers through targeted information campaigns and expansion of nighttime service capacity could substantially enhance sustainable urban travel options during nighttime.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Transportation and Mobility Innovations · Urban Transport and Accessibility · Transportation Planning and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute for Transport Studies, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
Funding
Rhein-Neckar-Verkehr GmbH · KIT-Publication Fund of the Karlsruhe Institute of Technology
References (46)
Show source-provided references
1. User characteristics and spatial correlates of ride-pooling demand–Evidence from Berlin and Munich10.1016/j.jtrangeo.2023.103596 ↗
2. On ride-pooling and traffic congestion10.1016/j.trb.2020.10.003 ↗
3. Spatiotemporal variability of ride-pooling potential–Half a year New York City experiment10.1016/j.jtrangeo.2023.103767 ↗
4. Understanding the effects of taxi ride-sharing—A case study of Singapore10.1016/j.compenvurbsys.2018.01.006 ↗
5. Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas10.1007/s11116-016-9729-z ↗
6. Mobility and the sharing economy: Potential to facilitate the first-and last-mile public transit connections10.2148/benv.42.4.573 ↗
7. Araldo, A., de Palma, A., Arib, S., Gauthier, V., Sere, R., Chaabouni, Y., Kharouaa, O., and Ari, A. (2020). Pooling for first and last mile: Integrating carpooling and transit. arXiv.
8. Kostorz-Weiss, N., Engelhardt, R., Wilkes, G., Dandl, F., Heilig, M., Zwick, F., Fraedrich, E., Bogenberger, K., Vortisch, P., and Kagerbauer, M. (2024). Assessing the Role of Ride-Pooling in Urban Transportation Systems Using a Large-Scale Integrated Supply and Demand Model-Investigations Based on Empirical Data from Europe’s Largest Ride-Pooling Service, Under review.10.2139/ssrn.4849663 ↗
9. Sharing vehicles or sharing rides-Psychological factors influencing the acceptance of carsharing and ridepooling in Germany10.1016/j.enpol.2022.112874 ↗
10. Dynamic ride sharing using traditional taxis and shared autonomous taxis: A case study of NYC10.1016/j.trc.2018.10.007 ↗
11. Factors Influencing Young Peoples’ Perceptions of Personal Safety on Public Transport10.5038/2375-0901.16.1.1 ↗
12. Women’s perceived risk of sexual harassment in a Bus Rapid Transit (BRT) system: The case of Barranquilla, Colombia10.1016/j.jth.2019.100598 ↗
13. Ride-pooling demand prediction: A spatiotemporal assessment in Germany10.1016/j.jtrangeo.2022.103307 ↗
14. Schaller, B. (2025, March 27). The New Automobility: Lyft, Uber and the Future of American Cities. Available online: https://trid.trb.org/View/1527868.
15. Anzenhofer, F., Fleckenstein, D., Klein, R., and Steinhardt, C. (2023). Analyzing the Impact of Demand Management in Rural Shared Mobility-on-Demand Systems. SRN Electron. J., 4682056.10.2139/ssrn.4682056 ↗
16. Examining factors influencing the adoption of solo, pooling and autonomous ride-hailing services in Australia10.1016/j.trc.2021.103524 ↗
17. Pricing and equilibrium in on-demand ride-pooling markets10.1016/j.trb.2020.07.001 ↗
18. The shareability potential of ride-pooling under alternative spatial demand patterns
19. Freitagnachts unterwegs im ÖPNV10.1007/s00548-022-00774-6 ↗
20. Young drivers’ night-time mobility preferences and attitude toward alcohol consumption: A Hybrid Choice Model10.1016/j.aap.2015.07.002 ↗
21. Exploring the Role of Transportation in Fostering Social Exclusion: The Use of GIS to Support Qualitative Data10.1007/s11067-007-9031-x ↗
22. Yu, S., Han, X., Liu, L., Liu, G., Cheng, M., Ke, Y., and Li, L. (2024). Exploring usage pattern variation of free-floating bike-sharing from a night travel perspective. Sci. Rep., 14.10.1038/s41598-024-66564-2 ↗
23. An empirical analysis of bike sharing usage and rebalancing: Evidence from Barcelona and Seville10.1016/j.tra.2016.12.007 ↗
24. How people get to work at night. A discrete choice model approach towards the influence of nighttime on the choice of transport mode for commuting to work10.1016/j.jtrangeo.2022.103418 ↗
25. Exploring safety concerns in the night-time city: Revitalising the evening economy10.3828/tpr.71.1.rnk3336381678247 ↗
26. Using behavioral economics to optimize safer undergraduate late-night transportation10.1002/jaba.1029 ↗
27. Commuting at night: How time of day affects commuter perceptions10.1016/j.tbs.2024.100750 ↗
28. Day-to-Day Choice to Commute or Not by Bicycle10.3141/2230-02 ↗
29. Kostorz, N., Fraedrich, E., and Kagerbauer, M. (2021). Usage and user characteristics—insights from Moia, Europe’s largest ridepooling service. Sustainability, 13.10.3390/su13020958 ↗
30. Ridepooling als ÖPNV-Ergänzung: Der Moia-Nachtservice während der Corona-Pandemie
31. Revealed demand patterns of people with disabilities in on-demand ridepooling10.59490/ejtir.2025.25.2.7742 ↗
32. What drives the utility of shared transport services for urban travellers? A stated preference survey in German cities In
33. Factors Influencing Willingness to Pool in Ride-Hailing Trips10.1177/0361198120915886 ↗
34. Rhein-Neckar-Verkehr GmbH (2025, March 27). Fips—Personen-Shuttle. Rhein-Neckar-Verkehr GmbH. Available online: https://www.rnv-online.de/english/.
35. Institute for Transport Studies (Karlsruhe Institute of Technology) (2025, March 27). Accompanying Research fips. Institute for Transport Studies (Karlsruhe Institute of Technology). Available online: https://www.ifv.kit.edu/english/21_1650.php.
36. Goerguelue, M.E., Barthelmes, L., and Kagerbauer, M. (2025, March 27). Accompanying Research on Automated Minibusses in Monheim am Rhein: Report on Results. Karlsruhe Institute of Technology. Available online: https://publikationen.bibliothek.kit.edu/1000170289.
37. Bundesministerium für Verkehr und Digitale Infrastruktur, infas, Deutsches Zentrum für Luft- und Raumfahrt, Institut für Verkehrsplanung und Transportsysteme, infas360. Mobilität in Tabellen (MiT 2017) (2025, March 27). Bundesministerium für Verkehr und Digitale Infrastruktur. Available online: https://mobilitaet-in-tabellen.bast.de/.
38. ChoiceMetrics (2025, March 27). Ngene 1.2: User Manual & Reference Guide. Available online: https://dl.icdst.org/pdfs/files4/a4dfa4b81043c641ec83525d32e8e33f.pdf.
39. Zarembka, P. (1974). Conditional logit analysis of qualitative choice behavior. Frontiers in Econometrics, Academic Press.
40. Train, K.E. (2003). Discrete Choice Methods with Simulation, Cambridge University Press.10.1017/CBO9780511753930 ↗
41. Hermaputi, R.L., and Hua, C. (2024). Decoding Jakarta Women’s Non-Working Travel-Mode Choice: Insights from Interpretable Machine-Learning Models. Sustainability, 16.10.3390/su16198454 ↗
42. A Comparative Study of Machine Learning Classifiers for Modeling Travel Mode Choice10.1016/j.eswa.2017.01.057 ↗
43. A prediction and behavioural analysis of machine learning methods for modelling travel mode choice10.1016/j.trc.2023.104318 ↗
44. Koppelman, F., and Bhat, C. (2006). A Self Instructing Course in Mode Choice Modelling: Multinomial and Nested Logit Models.
45. Apollo: A Flexible, Powerful Cust. Free. Package Choice Model Estim. Application10.1016/j.jocm.2019.100170 ↗
46. Estimating the Dimension of a Model10.1214/aos/1176344136 ↗