Ridesplitting services have gained great popularity in recent years. Past research has stressed that passengers, social matching, and trip characteristics are users’ top concerns when choosing such services. The present study sought to uncover potential users’ preferences toward ridesplitting in terms of the three aspects. We conducted a survey study and leveraged logit and multivariate ordinal regression models to analyze the data. Our results show that most respondents preferred splitting a ride with others that they’ve known and that they cared about passenger’s characteristics. Their expectations of trip length and time were quantitatively revealed. Sociodemographic factors exerted impact in such a way that users of certain groups were more conservative toward ridesplitting services. To better deploy ridesplitting and increase its adoption among users, we would recommend that future services consider users’ preferences and sociodemographic information when matching different people. An accurately estimated travel time also appears to be desired.
AmirkiaeeS. Y.EvangelopoulosN. (2018). Why do people rideshare? An experimental study. Transportation Research Part F: Traffic Psychology and Behaviour, 55, 9–24.
2.
BreretonM.RoeP.FothM.BunkerJ. M.BuysL. (2009). Designing participation in agile ridesharing with mobile social software. Proceedings of the 21st Annual Conference of the Australian Computer-Human Interaction Special Interest Group on Design: Open 24/7 - OZCHI ’09, 257. Melbourne, Australia: ACM Press.
3.
BuliungR. N.SoltysK.HabelC.LanyonR. (2009). Driving Factors behind Successful Carpool Formation and Use. Transportation Research Record: Journal of the Transportation Research Board, 2118(1), 31–38. https://doi.org/10.3141/2118-05
4.
ChenX.(Michael) ZahiriM.ZhangS. (2017). Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach. Transportation Research Part C: Emerging Technologies, 76, 51–70. https://doi.org/10.1016/j.trc.2016.12.018
5.
ChenX.ZhengH.WangZ.ChenX. (2018). Exploring impacts of on-demand ridesplitting on mobility via real-world ridesourcing data and questionnaires. Transportation.
6.
de Souza SilvaL. A.de AndradeM. O.Alves MaiaM. L. (2018). How does the ride-hailing systems demand affect individual transport regulation?Research in Transportation Economics, 69, 600–606.
7.
DeakinE.FrickK. T.ShivelyK. M. (2010). Markets for Dynamic Ridesharing? Case of Berkeley, California. Transportation Research Record, 131–137.
8.
HirkR.HornikK.VanaL. (2019). Multivariate ordinal regression models: An analysis of corporate credit ratings. Statistical Methods & Applications, 28(3), 507–539. https://doi.org/10.1007/s10260-018-00437-7
9.
HirkR.HornikK.VanaL. (2020). Mvord: An R Package for Fitting Multivariate Ordinal Regression Models. Journal of Statistical Software, 93(4). https://doi.org/10.18637/jss.v093.i04
KönigA.GrippenkovenJ. (2020). Travellers’ willingness to share rides in autonomous mobility on demand systems depending on travel distance and detour. Travel Behaviour and Society, 21, 188–202.
13.
LiW.PuZ.LiY.BanX. J. (2019). Characterization of ridesplitting based on observed data: A case study of Chengdu, China. Transportation Research Part C: Emerging Technologies, 100, 330–353.
14.
Lyft Usage Statistics. (2018). Retrieved from Lyft by the Numbers: Users & Drivers Statistics, Growth & Fun Facts website:https://muchneeded.com/lyft-statistics/
15.
MachadoC.de Salles HueN.BerssanetiF.QuintanilhaJ. (2018). An Overview of Shared Mobility. Sustainability, 10(12), 4342.
16.
PrattA. N.MorrisE. A.ZhouY.KhanS.ChowdhuryM. (2019). What do riders tweet about the people that they meet? Analyzing online commentary about UberPool and Lyft Shared/Lyft Line. Transportation Research Part F: Traffic Psychology and Behaviour, 62, 459–472. https://doi.org/10.1016/j.trf.2019.01.015
17.
R Core Team. (2020). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
18.
RayleL.ShaheenS. A.ChanN.DaiD.CerveroR. (2014). App-based, on-demand ride services: Comparing taxi and ridesourcing trips and user characteristics in San Francisco (No. No. UCTC-FR-2014-08). Berkeley: University of California Transportation Center.
19.
SarrieraJ. M.ÁlvarezG. E.BlynnK.AlesburyA.ScullyT.ZhaoJ. (2017). To Share or Not to Share: Investigating the Social Aspects of Dynamic Ridesharing. Transportation Research Record: Journal of the Transportation Research Board, 2605(1), 109–117.
20.
ShaheenS.GosselinK.BroaderJ.CohenA. (2022). Advancing Social Equity and Congestion Relief: Understanding the Travel Needs of Underserved Populations That Rely on Transportation Network Companies in the San Francisco Bay Area. Research Reports. 13.
21.
SimonettoA.MonteilJ.GambellaC. (2019). Real-time city-scale ridesharing via linear assignment problems. Transportation Research Part C: Emerging Technologies, 101, 208–232.
YanX.LevineJ.ZhaoX. (2019). Integrating ridesourcing services with public transit: An evaluation of traveler responses combining revealed and stated preference data. Transportation Research Part C: Emerging Technologies, 105, 683–696. https://doi.org/10.1016/j.trc.2018.07.029
24.
ZhangF.RobertsS. C.GoldmanC. (2019). A survey study measuring people’s preferences towards automated and non-automated ridesplitting. The Tenth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 92–98.
