Investigating generational disparities in attitudes toward automated vehicles and other mobility options

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Highlights

  • A modeling framework was developed to investigate the generational gaps in mobility attitudes.

  • Blinder–Oaxaca decomposition identified the sources of attitude gaps between Generations X and Y.

  • Millennials have more favorable views toward shared mobility, transit and automated vehicles.

  • A dominant portion of the gaps stemmed from the different perspectives between the generational cohorts.

  • The differences in the SED attributes explained a small portion of the generational gaps.

Abstract

This paper presents a study in investigating the generational gaps between Millennials and Generation X in terms of their mobility attitudes. A comprehensive analytical framework was proposed in this study and applied to data obtained from an SP survey in the U.S. Four modeling steps were involved, to measure the attitudes, identify generational gaps, recognize potential contributors to the attitudes, and decompose the contributions into Endowment, Coefficient, and Interaction effects. The Endowment effects measure how much of the generational differences can be attributed to socioeconomic and demographic variables, while the Coefficient effects reflect the gap that is due to actual behavioral changes or attitudinal shifts between the generations. The findings of this study confirmed the existence of generational gaps in mobility attitudes and revealed that a dominant portion of the gaps stemmed from the different perspectives between the generational cohorts. This indicates that these attitudinal disparities are likely to persist and remain at significant magnitudes, reflecting the unique views and values of the Millennials. Particularly, the preferences for transit and alternative modes and less reliance on private vehicles among Millennials were more of a reflection of their preferences in lifestyle choices and not so much constrained by their socioeconomic status as the previous generation. This study provides empirical evidence of the generational gaps between Millennials and the previous generation in terms of their mobility preferences. The findings provide valuable inputs for policy development in promoting sustainable transportation and community design.

Introduction

The Millennials (born between 1980 and 2000) are currently outnumbering the Baby Boomers (born between 1946 and 1964) as the largest adult group in the United States, accounting for about one-third of the labor force (Rainer and Rainer, 2011, Fry, 2016). This demographic cohort follows Generation X (born between 1965 and 1980) and has shown different behavior compared to their older peers in various domains such as education attainment, technology adoption, and consumption patterns. The term “generation of learners” were used in the literature to describe Millennials as they were much better educated than any other cohorts in U.S. history (Bialik and Fry, 2019). They were “digital natives”, in contrast to “digital immigrants”, which describes their older peers who became familiar with information and communications technology (ICT) after having a substantial life offline (Marc, 2001, Burstein, 2013, DeVaney, 2015). Millennials also showed distinct consumption behavior and shifted toward a shared and on-demand economy, in which access to merchandises and services was more important than the possession of them (Quinones and Augustine, 2015, Godelnik, 2017).

Millennials have also shown distinct travel behavior in terms of their choices and attitudes compared to the generations that preceded them (da Silva et al., 2019, Lee et al., 2019). Literature has indicated that Millennials made fewer trips and traveled less compared to Generation Xers (Tiedeman et al., 2017, McDonald, 2015). Millennials also contributed to the ridership of public transportation and shared mobility services across the United States and were referred to as “real-time riders” due to their constant access to real-time information of all transportation modes (Tiedeman and Circella, 2018, Rahimi et al., 2020). Hence, it is not surprising that they have shown a higher tendency to live in denser multimodal urban areas, in contrast to Generation Xers, who favored smaller cities and rural environments (Delbosc et al., 2019, Okulicz-Kozaryn and Valente, 2019). In this line, Millennials were also referred to as the “car-free” generation as they shift away from private vehicles (Sigall, 2016). The literature reported that members of this demographic cohort owned fewer cars and were less likely to be licensed drivers compared to the previous generation (Klein and Smart, 2017, McDonald, 2015). Regarding emerging transportation modes, Millennials have shown a higher inclination toward automated vehicles (AVs) and were estimated to be early adopters (Hardman et al., 2019, Jin et al., 2020, Shabanpour et al., 2018).

While there is a general consensus on the differences between Millennials and the preceding generations in terms of mobility preferences and travel patterns, the real debate lies on whether these generational gaps will vanish as Millennials enter later lifecycle stages or they may sustain much longer. In other words, the observed differences in mobility choices and travel behavior may represent actual distinct attitudes and preferences that could persist through one’s life, or they might just reflect the generation’s unique attributes, such as delayed marriage and childbearing, smaller household, lower vehicle ownership, along with other socioeconomic conditions that may have influenced their mobility choices and preferences. This forms the major motivation for this study. We aim to provide a closer look into the generational gaps and identify potential sources of these gaps. Specifically, we focus on different generations’ mobility attitudes and preferences, and provide insights on what might have contributed to the differences across the generational groups.

The next section provides an overview of relevant literature in identifying and analyzing disparities among groups, followed by a description of the data used in this study. The following section elaborates on the analytical framework and detailed methods used. The results were presented and discussed in the next section. The final section summarizes major findings and discusses policy implications and contributions.

Section snippets

Generational differences

A few studies in the transportation field have explored the generational differences in the attitudes and preferences for various modes of transportation.

Shamshiripour et al. (2020) investigated the preference for mobility on demand (MoD) services using the National Household Travel Survey (NHTS) data. The study showed that Millennials had a higher motivation to use MoD services compared to their older counterparts, mostly due to their higher tendency in using new modes of transportations and

Stated preference survey

A stated preference (SP) survey was conducted in the United States to collect the required data for this study. The survey was conducted in 2017 and included information such as socioeconomic and demographic characteristics, mobility profile, mobility preferences and attitudes. The survey used a stratified random sampling approach to select respondents given a preset quota for every cohort based on age, gender, ethnicity, education, and household income. The sampling plan followed the 2010

Analytical framework

The proposed analytical framework for this study consists of four main steps, as presented in Fig. 3. Leveraging data from the SP survey, the first step estimates the attitudes using confirmatory factor analysis (CFA). The attitudes are measured using observed indicators, such as current mode usage, mode preferences, and attitudes toward AV technologies. The identified latent attitude factors are labeled and interpreted based on the contribution of their indicators. Correlations between

Model results

The first part of this section presents the findings of the CFA in identifying various mobility attitudes. The second part identifies potential disparities in these attitudes between the generational cohorts through t-tests and Cohen’s d measure. The last section discusses the findings of the OLS regression analysis and the BO decomposition, and explains the various aspects of disparities between the generations for each attitude (dependent variable).

Discussions

In summary, five distinct mobility-related attitudes were identified based on existing mode usage frequency, mode choices under different circumstances, and preferences for various AV technologies. These attitudes represent the user’s inclination and preferences toward different mobility options, including shared mobility, transit, private vehicle, driving assistance features, and fully automated vehicles.

Comparing the average attitude scores between Generation X and Millennials, significant

Conclusions

Many researchers have noted that Millennials had distinct travel behavior compared to the previous generations, but it is unclear whether these generational gaps are due to their unique characteristics or the true taste differences between the generations. This has motivated this study to investigate the generational gaps in their attitudes toward various mobility options and identify whether and to what extent these gaps may be attributed to the true taste differences between the generations,

CRediT authorship contribution statement

Alireza Rahimi: Conceptualization, Methodology. Ghazaleh Azimi: . Xia Jin: Conceptualization, Methodology, Writing - review & editing, Supervision, Funding acquisition.

Acknowledgment

This work is funded by the research office of the Florida Department of Transportation (BDV29 977-47). The opinions, findings and conclusions expressed in this publication are those of the authors and not necessarily those of the Florida Department of Transportation or the U.S. Department of Transportation.

References (55)

  • Chen Song et al.

    Travel time use over five decades

    Transp. Res. Part A: Policy Pract.

    (2018)
  • I.Y. Wong et al.

    Validating an older adult driving behaviour model with structural equation modelling and confirmatory factor analysis

    Transport. Res. F: Traffic Psychol. Behav.

    (2018)
  • M. Zhou et al.

    Generational differences in attitudes towards car, car ownership and car use in Beijing

    Transport. Res. D: Transp. Environ.

    (2019)
  • H. Asgari et al.

    Incorporating attitudinal factors to examine adoption of and willingness to pay for autonomous vehicles

    Transp. Res. Rec.

    (2019)
  • G. Azimi et al.

    Role of attitudes in transit and auto users’ mode choice of ridesourcing

    Transp. Res. Record

    (2020)
  • K. Bialik et al.

    Millennial life

    (2019)
  • A.S. Blinder

    Wage discrimination: reduced form and structural estimates

    J. Human Resources

    (1973)
  • Brown, T.A., 2015. Confirmatory factor analysis for applied research. Guilford...
  • Bryant, F.B., Yarnold, P.R., 1995. Principal-components analysis and exploratory and confirmatory factor...
  • Burstein, D., 2013. Fast Future: How the Millennial Generation is Shaping Our World. Boston: Beacon...
  • J. Cohen

    Statistical power analysis for the behavioral sciences

    (2013)
  • J. Cotton

    On the decomposition of wage differentials

    The review of economics and statistics

    (1988)
  • da Silva, D.C., Astroza, S., Batur, I., Khoeini, S., Magassy, T.B., Pendyala, R.M., Bhat, C.R., 2019. Are Millennials...
  • S.A. DeVaney

    Understanding the millennial generation

    J. Financ. Serv. Profess.

    (2015)
  • S.P. Eisner

    Managing generation Y

    Quart. J. S.A.M. Adv. Manage. J.

    (2005)
  • A. Etezady et al.

    What drives the gap? Applying the Blinder-Oaxaca decomposition method to examine generational differences in transportation-related attitudes

    Transportation

    (2020)
  • R. Fry

    Millennials overtake Baby Boomers as America’s largest generation

    Pew Res. Center

    (2016)
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