Clustering Life Course to Understand the Heterogeneous Effects of Life Events, Gender, and Generation on Habitual Travel Modes

Abstract

Daily transportation mode choice is largely habitual, but transitions between life events may disrupt travel habits and can shift choices between alternative transportation modes. Although much is known about general mode switches following life event transitions, less is understood about differences that may exist between subpopulations, especially from a long-term perspective. Understanding these differences will help planners and policymakers introduce more targeted policy interventions to promote sustainable transportation modes and inform longer-term predictions. Extending beyond existing literature, we use data collected from a retrospective survey to investigate the effects of life course events on mode use situated within different long-term life trajectory contexts. We apply a machine-learning method called joint social sequence clustering to define five distinct and interpretable cohorts based on trajectory patterns in family and career domains over their life courses. We use these patterns as an innovative contextual system to investigate (1) the heterogeneous effects of life events on travel mode use and (2) further differentiation between gender and generation groups in these life event effects. We find that events occurring relatively early in life are more strongly associated with changes in mode-use behavior, and that mode use can also be affected by the relative order of events. This timing and order effect can have lasting impacts on mode use aggregated over entire life cycles: members of our "Have-it-alls" cohort-who finish their education, start working, partner up, and have children early in life-ramp up car use at each event, resulting in the highest rate of car use occurring the earliest among all the cohorts. Women drive more when having children primarily when their family formation and career formation are intertwined early in life, and younger generations rely relatively more on car use during familial events when their careers have a later start.

What Makes You Hold on to That Old Car? Joint Insights From Machine Learning and Multinomial Logit on Vehicle-Level Transaction Decisions

“…In contrast, the replacement decisions for parents older than 35 are not associated with the presence of young children. This finding is consistent with previous literature that has found that travel behaviors are more frequently changed before age 35 years old (Jin et al, 2020).…”

“…(Klein and Smart, 2019). Third, as life events are determined from the two−year window between waves, broader lead or lag effects [see (Fatmi and Habib, 2016)] may be included to understand the more enduring effects associated with certain life events such as the duration of marriage (Jin et al, 2020). Lastly, the present study only seeks to understand how soon an existing household vehicle will be transacted out of the household fleet.…”

Work-family trajectories and health: A systematic review

“…An exception was the study by Mooyaart et al (2019) , in which the most common trajectory in young men (29.2% of men) was characterised by unstable employment and the trajectories describing stable employment were as common in men as in women (32.3% and 33.6%, respectively). Similarly, the study by Jin et al (2020) did not find many differences between the trajectories of men and women.…”

“… Baxter, Hewitt, & Haynes, 2008 ; Kühhirt, 2012 ; Schober, 2013 ). The results of two studies ( Jin et al, 2020 ; Mooyaart et al, 2019 ) differed from the other included studies. Specifically, these two studies did not find differences between the trajectories of men and women, which can be explained in several ways.…”

“…Parental trajectories became less complex, possibly due to having fewer or no children. Jin et al (2020) found that the inter-generational differences in work-family trajectories are mostly due to the timing of the childbirth, i.e. people born earlier more often followed the trajectories family first and have-it-alls when compared with younger people.…”