International Choice Modelling Conference, International Choice Modelling Conference 2017

Font Size: 
Exploring the Impact of Shared Mobility on California Millennials and Older Adults’ Travel Patterns
Giovanni Circella, Farzad Alemi, Patricia Mokhtarian

Last modified: 28 March 2017


Millennials are increasingly reported to have different lifestyles and travel behavior from previous generations at the same stage in life. Among the observed changes, millennials often postpone the time they obtain a driver’s license, choose to live in urban locations and not to own a car, are heavy adopters of new technology-based transportation options and use other alternative means of transportation more often. Several explanations have been proposed to explain millennials’ behavior, including the changes in household composition (e.g. postponing marriage and procreation), the substitution of travel with telecommuting and social media, and the preference for urban lifestyles and locations closer to the vibrant parts of a city. Certainly, the tech-savvy millennials who live in central urban neighborhoods, are heavy users of Uber or Lyft and do not own a car are a popular figure in the media headlines, and a common presence in major cities. But the reality is more complex: not all millennials fit this stereotype, many older peers also have such lifestyles, and large masses of young adults behave in a way more similar to traditional older cohorts. Understanding the reasons behind these trends, and their likely impact on housing and travel demand, and the use of the various modes of transportation, is of extreme importance to researchers, planners and policy-makers. However, the debate is often dominated by speculations about the factors affecting millennials’ behavior, and their likely persistence (Polzin et al., 2014), and previous studies have been limited by the lack of information on specific variables (e.g. personal attitudes and preferences, for studies based on national or regional household travel survey data), or the use of convenience samples (e.g. studies on university students).

This study builds on a large research effort undertaken to investigate the relationships among millennials’ residential location, individual attitudes, lifestyles, travel behavior and vehicle ownership, the adoption of shared mobility services, and the aspiration to purchase and use a vehicle vs. use other means of transportation in California, which was designed to overcome some of the limitations from previous studies. A rich dataset was collected in fall 2015 with a comprehensive online survey administered to a sample of 2400 California residents, including millennials (i.e. young adults, 18-34) and members of the preceding Generation X (i.e. middle-age adults, 35-50). The data collection was part of a longitudinal study of the emerging transportation trends in California, designed with a rotating panel structure, with additional waves of data collection planned in future years. We used a quota sampling approach to recruit respondents from each of the six major regions of California and three dominant neighborhood types (urban, suburban and rural), while controlling for sociodemographic targets including household income, gender, race and ethnicity, and presence of children in the household. For additional information on the survey content and data collection, see Circella et al. (2016).

In this paper, we analyze the data from this first wave of data collection, and explore the relationships between the adoption of emerging transportation technologies and shared mobility services and other components of travel behavior. In particular, we explore the relationships between the adoption and frequency of use of on-demand ride services (also known as transportation network companies, or TNCs) such as Uber and Lyft and the use of other transportation modes through the estimation of bivariate models, with the eventual inclusion of a latent class component.

On-demand ride services have been attributed multiple (and often counteracting) effects on the use of other modes (Taylor et al, 2015): they may contribute to reducing car ownership by providing an alternative to owning a car, reduce driving by offering flexible travel alternatives, and increase or decrease the use of transit, depending on the local context and individuals’ characteristics. The ability to investigate the impact of these services on the use of other modes has been limited, though, by data availability, the evolving characteristics of these services, and the multifaceted impacts they may have.

Our dataset includes information about the awareness, adoption and frequency of use of the most common shared mobility services (including car-sharing, bike-sharing, dynamic ridesharing and on-demand ride services such as Uber or Lyft), information on the attributes that affect the respondents’ use of these services, and the self-reported impact that the last trip by Uber/Lyft had on the use of other modes, together with many other variables that can affect travel behavior. We are currently testing the use of a recursive probit model to simultaneously estimate the frequency of use of on-demand ride services and the use of public transportation, accounting for the impacts of individual and household characteristics, the land use features of the neighborhood where an individual lives, respondents’ attitudes and preferences, and testing the impacts of the adoption of these new mobility services on the use of transit. We plan to test a latent class component of the model to account for different behaviors among sub-segments of the population and different effects of the use of shared mobility, e.g. for some users, the use of Uber/Lyft might complement the use of transit through providing last-mile access to transit stations or a ride home outside of the hours of transit operations, while for other users the use of Uber/Lyft might substitute for the use of public transportation. We expect to have final results ready to be presented by the time of the conference.



Circella, G., L. Fulton, F. Alemi, R. Berliner, K. Tiedeman, P. Mokhtarian, and S. Handy. 2016. "What Affects Millennials’ Mobility? PART I: Investigating the Environmental Concerns, Lifestyles, Mobility-Related Attitudes and Adoption of Technology of Young Adults in California.” Project Report, National Center for Sustainable Transportation, May 2016. Available at (Last accessed on September 30, 2016).

Polzin, S., X. Chu and J. Godfrey. 2014. “The Impact of Millennials' Travel Behavior on Future Personal Vehicle Travel”, Energy Strategy Reviews, 5: 59-65.

Taylor, B., R. Chin, C. Melanie, et al. 2015. “Special Report 319: Between Public and Private Mobility: Examining the Rise of Technology-Enabled Transportation Services.” Transportation Research Board: Committee for Review of Innovative Urban Mobility Services.

Conference registration is required in order to view papers.