The dynamics of commuting over the life course: Swiss experiences

Beige, Sigrun; Axhausen, Kay W.

doi:10.1016/j.tra.2017.01.015

Cited by 32 publications

(30 citation statements)

References 22 publications

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“…First, research that has explored this type of heterogeneity in the population has tended to define subpopulation segments using individual characteristics that do not change over time, such as gender or generation [26], [27]; previous mode preferences and attitude characteristics [19], [28], [29]; or life cycle stages or sociodemographic characteristics (e.g., age and household structure) concurrent with life events [26], [30]. However, long-term mobility decisions depend on one's current situation, past experiences, and future plans [11], and such a dynamic and multidimensional decision context can only be revealed in a long-term life history, rather than in life cycle stages defined statically at the moment an event occurs. Our analysis uses longitudinal data and brings this critical dynamic perspective.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…In addition, scholars have increasingly used the life-oriented approach to better understand travel behaviour and daily life (Zhang and Acker 2017;Zhang 2017;Beige and Axhausen 2017). For example, Gerber et al (2017) found that residents had higher life satisfaction after relocating from Luxembourg to neighbouring countries with lower real estate prices, despite increased commute times.…”

Section: Introductionmentioning

confidence: 99%

Subjective well-being in China: how much does commuting matter?

Zhu

Chen

et al. 2017

Transportation

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With rapidly increasing urbanization and motorization in China, the effect of commuting on residents' subjective well-being (SWB) is likely growing. We used 13,261 individual, 124 city, and 401 neighbourhood samples from the 2014 China Labour-Force Dynamics Survey (CLDS 2014) and applied multilevel mixed-effects ordered probit regressions to investigate the relationship between commuting and SWB. We found huge differences between urban and rural areas in relation to commuting. Urban respondents' daily average commuting time was 0.56 h while rural respondents' daily average commuting time was 0.41 h. Further, the daily average commute time for residents living in cities with high urbanization rates ([ 70%) was longer than for those living in cities with low urbanization rates (\ 70%). The subjective well-being of residents who commute by walking or cycling was significantly lower than that of those who commute by other transportation modes. The regression results indicated that the longer the commute time, the lower the subjective well-being. Among residents who live in rural areas or cities with low urbanization, subjective well-being was more easily affected by commuting time.

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“…Beige and Axhausen assessed travel behavior over a 20-year period with retrospective questions and revealed that car ownership increases after the age of 18 and is the highest for the age group between 25 and 50 years while the opposite is true for national and regional seasonal public transport tickets (Beige and Axhausen 2008). With the same data, the authors observed that the main mode of transport to the workplace is relatively stable throughout aging while bicycling tends to decrease in the late 40s and public transport use in the 60s (Beige and Axhausen 2017). The results of another study indicate that life stage better explained the number of trips made per day than did variation in the built environment (Sun et al 2009).…”

Section: Life Stage Influences Travel Behaviormentioning

confidence: 93%

“…Several studies have found evidence for the influence of life events on travel behavior change (Clark et al 2016;Beige and Axhausen 2017;Dargay and Hanly 2004;Oakil et al 2016;Scheiner and Holz-Rau 2015;Verhoeven et al 2005). For the UK, Clark et al (2016) using a longitudinal dataset found that a higher share of commuters who experienced changes in the workplace and residential location switched mode compared to commuters who did not experience life events.…”

Section: Life Events Trigger Change In Travel Behaviormentioning

confidence: 99%

“…These results still hold when the availability of public transport and commute distance are taken into account and point to a separate effect of life events on commute behavior. Besides changes in residential location and employment, the acquisition of a driver's license and children moving out of the household have been shown to affect car ownership and individual travel behavior (Beige and Axhausen 2017;Dargay and Hanly 2004;Oakil et al 2014). An increase in household size such as cohabitation or a childbirth positively influences car ownership while divorce has a negative effect (Oakil et al 2014).…”

Section: Life Events Trigger Change In Travel Behaviormentioning

confidence: 99%

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Examining the effect of life course events on modality type and the moderating influence of life stage

2020

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Previous research has demonstrated the relevance of life events to explain changes in travel behavior. Less clear is the moderating role played by life stages on the relationship between life events and travel behavior. Our goal in this study is to explore how the influence of life events on travel behavior differs by life stage. We use data from a travel survey of faculty, staff and students at a US university. We define four life stages: millennials living in shared apartments or alone, millennials living with partners, parents (of any generation) living with their children, and non-millennial adults living without children. Four modality types were defined: active travelers, car users, transit users, and multimodal travelers. We use a Manifest Markov Model to estimate probabilities of switching modality types between two waves of the survey. Life stage does not significantly moderate effects of life events on change in travel behavior but does affect modality type: the prevalence of active travel modality types, particularly the share of women who are active travelers, decreases throughout the life stages. Millennials living with their partners and parents living with their children relocate to another town more often than those in other life stages, a life event associated with a higher probability of switching to car use only. Our results identify "windows of opportunity", such as residential relocation, that planners can use to promote sustainable travel behavior.

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The dynamics of commuting over the life course: Swiss experiences

Cited by 32 publications

References 22 publications

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

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

Subjective well-being in China: how much does commuting matter?

Examining the effect of life course events on modality type and the moderating influence of life stage

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