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Does living in a neighborhood with high-quality public transit influence travel behavior later in life, even if you move to a neighborhood with worse transit service? To test this, we construct residential histories of individuals using decades of data from the Panel Study of Income Dynamics. We find that past experiences shape transportation futures. Exposure to transit during young adulthood in particular is associated with an auto-light lifestyle and greater transit usage later in life. This research suggests a long-term benefit for encouraging transit at younger ages to foster a “transit habit.”
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Remembrance of Cars and Buses Past: How Prior Life Experiences Influence Travel
Smart, Michael J., and Nicholas J. Klein. “Remembrance of Cars and Buses Past: How
Prior Life Experiences Influence Travel.” Journal of Planning Education and Research
38, no. 2 (June 1, 2018): 13951.
Michael J. Smart Ph.D.,
Assistant Professor
Edward J. Bloustein School of Planning and Public Policy
Rutgers, the State University of New Jersey
33 Livingston Avenue, New Brunswick, New Jersey 08901
Nicholas J. Klein Ph.D.,
Visiting Assistant Professor
Urban Planning Program
Graduate School of Architecture, Planning and Preservation
Avery Hall, Columbia University, New York, NY 10027
Does growing up in a neighborhood with high-quality public transit influence travel behavior
later in life, even if you move to a neighborhood with worse transit service? To test this, we
construct residential histories of individuals using decades of data from the Panel Study of
Income Dynamics. We find that past experiences shape transportation futures. Exposure to
transit during young adulthood, in particular, associated with an auto-light lifestyle and greater
transit usage later in life. This research suggests a long-term benefit for encouraging transit at
younger ages to foster a “transit habit.”
Imagine a thirty-something born and bred New Yorker who packs up and moves across
the country to Los Angeles. As a stereotypical New Yorker, she used transit or walked most
places. But now that she lives in LA, does her experience of using public transit in New York
continue to influence her daily travel? Is she more likely to use transit than her Angeleno peers
who have lived their whole lives in auto-dependent neighborhoods? Or does she adapt
completely to the new reality, abandoning her transit habits in the context of a new built
environment? This article tests whether past experience living in areas with high-quality public
transit influences travel behavior later in life.
If today’s travel choices are influenced by our prior experiences, this would suggest a
long-term rationale for encouraging public transportation use. Rather than focusing solely on
relatively short-term cost-benefit analyses or economic development gains for new and existing
public transit service, planners, policy-makers, politicians and advocates might argue that by
providing these public services, we habituate residents to public transportation. Familiarity with
public transit can have longer-term payoffs that do not appear on a typical cost-benefit ledger,
and policies like those that provide fare-free public transit access for high school and college
students could have long term benefits (Brown, Hess, and Shoup 2001; Brown, Hess, and Shoup
Our work builds on research that highlights how social factors can mediate the
relationship between the built environment and travel behavior. While decisions about how,
when and where we travel are still largely explained by cost, time, and distance, other factors
also contribute to these decisions, including a positive utility of travel (travel for the sake of
travel) (Mokhtarian and Salomon 2001), the influence of social networks (Axhausen 2005; Smart
and Klein 2013; Tilahun and Levinson 2011; Blumenberg and Smart 2014) and perceptions of
travel modes (Klein 2016; Guiver 2007). Here, we argue that past experiences also shape today’s
decisions about travel. Our research improves on earlier studies (Weinberger and Goetzke 2010;
Macfarlane, Garrow, and Mokhtarian 2015; Chen, Chen, and Timmermans 2009) by using a
long-term national panel dataset with information on travel and the built environment and
evaluates the influence of exposure to public transit throughout one’s life.
We test whether living in a transit-rich area in the past leads to an auto-light lifestyle and
greater transit usage later in life, even when the new community is not particularly transit-
friendly. To do this, we use the Panel Study of Income Dynamics (PSID), which has surveyed
the same families and their descendants for nearly 50 years. We create residential histories for all
respondents and measure their transit “exposure” throughout their life. We then test whether
higher exposure during earlier years leads to more transit use and less car ownership later in life.
We find that exposure to transit early in life leads to greater likelihood of using public
transportation and decreases an individual’s level of auto ownership later in life. We note that
exposure to transit during one’s formative years (ages five to eighteen), when one has no say in
where one lives, is a strong predictor of later transit use and lower rates of auto ownership. We
further note that our models suggest that the strongest predictor of future transit use is the transit
environment in which one lives in one’s late 20s and early 30s, suggesting that policy
interventions targeted to these age groups may bear the most fruit.
In the next section, we describe the existing research on the relationship between travel
behavior and past experiences. Then we describe our data and research approach. Next, we
discuss the findings from our regression models. We conclude with a discussion of the model
results and implications for policy.
When households relocate, their daily travel patterns are influenced by the built
environment context of their new neighborhood, their economic and demographic circumstances,
their attitudes and preferences, and, as we test here, perhaps by their past experiences. Among
these factors, researchers have recently focused most of their attention on the role of the built
environment (e.g., Ewing and Cervero 2010). Residential relocations, along with changes in
employment and family composition, function as critical events in an individual or household’s
mobility biography that alter their circumstances leading to a behavioral shift in travel (Scheiner
2007; Müggenburg, Busch-Geertsema, and Lanzendorf 2015; Lanzendorf 2003; Beige and
Axhausen 2012). This work suggests that programs that aim to influence travel behavior should
focus on young people (under 35 or so) who have not yet settled down and after these critical
events in one’s lives (Beige and Axhausen 2012).
Scholars focus on movers as a mechanism to address concerns about residential self-
selection, namely that the statistical associations between travel and the built environment may
be manifestations of a sorting process by which people who have an affinity for transit or
walking chose to live in neighborhoods where they can realize those preferences. By controlling
for attitudes, preferences and socioeconomic attributes, researchers frame a residential move as a
“treatment” effect and compare movers to “control group that did not relocate as a way to tease
out these self-selection effects (Cao and Ermagun 2016; Cao, Mokhtarian, and Handy 2007;
Handy, Cao, and Mokhtarian 2006; Krizek 2003; Scheiner and Holz-Rau 2012). Collectively,
this research finds that even when controlling for self-selection effects, movers do change their
travel behavior in response to the built environment, though there is less agreement on the
magnitude of these effects.
A smaller body of literature has examined how prior experiences both in the short and
long term can shape future travel behavior. In the short term, habits have a powerful influence on
daily travel behavior. Through repeated experiences, decisions about how, when and where to
travel become less calculating and more habitual, as long as the outcome is more or less
acceptable (Gärling and Axhausen 2003; Verplanken, Aarts, and Van Knippenberg 1997; Fujii
and Kitamura 2003). Some have criticized this formulation of travel habits as automatic and not
reflective (Schwanen, Banister, and Anable 2012) and have critiqued the narrow view of travel
habits as barriers to sustainable travel while ignoring the positive aspects of habits (e.g. freeing
individuals from the rational decision-making calculations and allowing them to be in the
moment) (Middleton 2011).
Prior experiences can also exert an influence over a longer time horizon, shaping car
ownership, commute time, and distance. Past experiences may influence later travel behavior if
these experiences shape attitudes and preferences for neighborhoods or travel modes (Chen,
Chen, and Timmermans 2009; Weinberger and Goetzke 2010; Macfarlane, Garrow, and
Mokhtarian 2015). For example, Weinberger and Goetzke (2010) use Census data to analyze
movers to and from cities with high-quality transit service to test whether households “learn
preferences” for a car-light lifestyle and then take those preferences with them when they move.
They find that when urbanites move to the suburbs, they have fewer vehicles than would be
expected and, conversely, when suburbanites and rural residents move to the central cities, they
own more vehicles than their urban peers. Chen et al. (2009) use data from the Puget Sound
Transportation Panel Survey to test the influence of the previous residential locations on
commuting distances of movers. Chen et al. find that exposure to lengthy commutes does indeed
“make them more tolerant of long commute distances” but that these “historical depositions” are
weakened by lifecycle factors, such as the birth of a child (p. 2773). Macfarlane et al. (2015)
construct residential histories for residents of the Atlanta region to test how past exposure to
neighborhood conditions (residential density and the share of workers commuting via non-auto
modes) influence auto ownership. While the authors find a significant effect of past built
environments, they conclude that “exposure to higher densities and non-vehicle transportation
options (either currently or in the past) has a relatively modest influence on vehicle ownership
decision” (Macfarlane, Garrow, and Mokhtarian 2015, 197). Lastly, Döring et al. (2014) find
multigeneration effects. Using a retrospective study of three generations, they find that parents’
and grandparents’ attitudes about travel and their residential location can influence the
subsequent generations’ travel behavior.
Past experiences can also influence how individuals make subsequent decisions about
their travel. For example, Simonsohn (2006) draws on the psychological concept of a “contrast
effect,” by which decisions are influenced by surrounding contexts, to test whether the average
commute duration in movers’ prior metropolitan area influences commute durations after the
move to a new region. Using data from the PSID, Simonsohn finds that “individuals coming
from cities with longer average commutes choose to commute significantly longer in their new
city than their peers coming from cities with shorter commutes” though this contrast effect
dissipates over time (p. 4).
Some have also speculated that past experiences may be one of the reasons why transit
use is higher among immigrants to the United States than among their US-born peers.
Immigrants from countries with lower car ownership rates may bring a transit habit with them
when they migrate (Blumenberg and Smart 2011). Additionally, many immigrants first settle in
transit-rich neighborhoods when they arrive in the US and they may develop a transit habit in
these contexts, which continue for many years (Chatman and Klein 2013).
Beyond the transportation literature, a host of social scientists have examined the role of
neighborhood effects on a range of outcomes including economic, academic, employment, and
health outcomes, to name a few. This literature has developed a rich conceptualization of the
mechanisms by which one’s residential location, at different stages in life, influences particular
outcomes (e.g., Ellen and Turner 1997; Galster 2012; Sharkey and Faber 2014). For example, the
role of family, schools, peers and institutions within a neighborhood may vary in importance
depending on an individuals age and the outcome of interest (Ellen and Turner 1997). And
responses to a particular neighborhood effect may require a threshold, might not be universal,
could be mediated or buffered by other factors, or influenced by the frequency, duration and
consistency of exposure (Galster 2012).
Our study builds on and expands existing research about how exposure to transit can
influence travel behavior over many years. While others have examined residential histories and
exposure to transit, they have not isolated the effects of exposure at specific ages, as we do here.
We also extend this literature by testing the effects of living in an area with high-quality transit
on both auto ownership and transit use. Finally, because the PSID is a national data set that has
been following the same families for many years, we can study the effects of past experiences in
a broader range of built environments and over a longer time horizon than previous studies.
We use a restricted version of the Panel Study of Income Dynamics (PSID) to test
whether past experiences influence future transit use and auto ownership (“Panel Study of
Income Dynamics, Restricted Use Data” 2014). The PSID is a long-running panel survey that
has been following the same families since 1968, when it began with 5,000 families. Since then,
the PSID has grown to over 9,000 families (and 22,000 individuals) through births and the
addition of Latino and immigrant samples in the 1990s. While the survey focuses on earnings
and expenditures, the questionnaires have often asked families about auto ownership and
transportation expenses, including transit expenses. We use the responses to these questions for
our analysis.
We estimate models that test whether prior exposure to transit influences future spending
on transit and auto ownership. We measure transit spending using a dichotomous variable where
a value of one indicates that the family had some transit expenses during the past month, and a
zero value means the family reported spending no money on transit. We thus use a logistic
regression to model transit spending. We measure auto ownership as the ratio of cars to adults in
the family, and model this using ordinary least squares (OLS) regression. We use random-effects
models for both outcome measures and evaluate multiple measures of prior exposure to transit,
which we describe below. We chose a random-effects model, rather than a fixed-effects model,
because the fixed-effects would exclude time time-invariant variables, including most of our
measures of past exposure to transit. Using a fixed-effects model would also be problematic for
our model of transit use since this excludes 84 percent of our sample who either always or never
report transit expenditures in each survey wave.
We use data from the PSID dating back to 1968 to inform our measures of exposure to
transit but we limit our models to more recent waves. For our models of auto ownership, we use
the eight PSID waves from 1999 through 2013 since the PSID omitted questions about auto
ownership between 1986 and 1999. For the models of transit expenditures, we use six waves
from 2003 to 2013. While the PSID has included questions about transit expenses since 1999, the
question wording and responses changed considerably between the 2001 and 2003 surveys.
Our variable of interest is past exposure to transit. Since we do not have historical data on
transit service quality for the entire US, we use two proxies for transit service. First, we use US
Census journey-to-work data as a proxy for transit quality. For each census tract, we use the
share of workers commuting by transit from the 1970, 1980, 1990 and 2000 decennial census
and the 2008-2012 American Community Survey 5-Year Estimates. For each wave of the PSID,
we use the most contemporary decennial census (e.g. panel waves 1968 through 1974 we use the
1970 census and for the panel waves 1975 through 1984 we use the 1980 census).
Our second proxy for transit service is a composite database of transit accessibility. We
use data on transit accessibility from The Brookings Institute and The Accessibility Observatory
at the University of Minnesota, each of which provides partial coverage for the entire United
States (Owen and Levinson 2014; Tomer et al. 2011). From both datasets, we use the number of
jobs accessible by transit within 30 minutes (including access and egress on foot). In places
where data were available from both sources, the measures are very highly correlated, and we
used the average of both. We convert this transit accessibility measure to a z-score (standard
deviations from the regional mean, normalized separately for each Combined Statistical Area in
the United States) to help account for the large variation in labor market sizes across metro areas.
There are a few obvious limitations with this data. First, the coverage is incomplete for the US
(covering only about 60 percent of our PSID records). Second, the data are not historical, though
our transit access metrics are just as correlated with previous decades’ transit use as they are for
current transit use (r=0.52 for the 1970 and 1980 censuses, 0.54 for 1990, 0.55 for 2000 and 0.53
for the 2010 ACS), suggesting that while new transit services have opened in many locales, the
landscape of transit service throughout the nation has remained remarkably stable in recent
Like Macfarlane et al. (2015), we evaluated several different measures of past transit
exposure. First, we used the head of family’s average exposure to transit since 1990. Second, we
constructed an exponential decay function, to give more weight to recent experiences. Our third
measure is an average exposure during one’s 20s. And finally, we measure the head of family’s
exposure to transit during the formative years (five to 18), when one has no say in the location of
the family; we include this measure to help control for residential self-selection, in which those
with a strong preference self-select to live in neighborhoods where they can ride transit (for an
overview, see Cao, Mokhtarian, and Handy 2009). Finally, we estimate a series of models using
moving averages for transit exposure from birth to age 40 and present these results in a summary
While the PSID collects data on transit use (expenditures) aggregated for the entire
family, we are only able to estimate the head of family’s exposure to public transit. This
mismatch may lead to some error, though we expect the magnitude of the error to be small; we
assume that partnering and marriage behavior is unrelated to both partners’ prior exposure to
transit in any way that would bias our results.
To account for other factors that likely influence transit expenditure and auto ownership,
we include several control variables in our models. We include the total family income in the
previous year, residential population density of the census tract where the family lives (measured
in thousands of people per square mile), race and ethnicity of the head of the household, the
family’s poverty status, the student status of the household head and his/her spouse or partner
(where present) and number of children in the household. We also include the number of cars per
adults that the family owns in the model of transit expenses.
We analyze the determinants of transit use and car ownership, focusing on the influence
of prior exposure to transit. We use a large panel dataset, and families who move from one
transit environment to another are of particular interest. Table 1 shows descriptive statistics for
our full sample, families who live in low- and high-transit tracts, families who moved and non-
movers, and families who moved from low- to high- and from high- to low-transit environments.
We define low- and high-transit environments using our transit access data and set the cutoff
point at the regional mean (z-score of zero). Transit accessibility is considerably right-skewed
(since there are far more locations below the regional mean than above it) and there are over six
times as many observations in low-transit environments as in high-transit environments. We
separately examined differences among those in the bottom and top third of transit accessibility,
and the results were broadly similar.
Those who live in high-transit areas are quite different from those who live in lower-
transit environments, as we expect given the literature on residential location and transit. They
use transit much more (25 percent had used transit in the prior month compared with just six
percent of families in low-transit environments) and own fewer cars per adult in the family. They
also have lower average incomes, are more likely to be in poverty, and are considerably more
likely to be students, immigrants, and people of color.
Families living in high-transit areas are also more likely to have grown up and lived
recently in higher-transit areas than are those who live in lower-transit environments, though the
data suggest a nuanced story. While families in transit-rich environments live in tracts that are on
average 1.30 standard deviations above the regional mean for transit access (vs. -0.18 for
families in low-transit environments), these two groups were not as different earlier in their lives.
The gap between the two groups’ average exposure since 1990 is considerably smaller (0.66
vs. -0.11) and the gap between their access to transit as children is smaller yet (0.29 vs -0.07).
The differences between movers and non-movers tell a similar story; those who have
moved in the past two years (35 percent of records) are younger, are more likely to be students,
have lower incomes, and are more likely to be people of color. They also have fewer cars per
adult in the family and use transit more than non-movers do. Movers also live in areas with
somewhat better transit service, and have a slightly higher level of exposure in the recent past
and in their twenties. However, both movers and non-movers grew up in census tracts with
average transit service.
Finally, we examine the differences between two specific groups of movers: those who
move from a low-transit environment to a high-transit environment, and those who make the
opposite move. Here, we observe fewer differences, though some are remarkable. Notably, those
who move to from low- to high-transit areas are considerably more likely to live in poverty (22
percent do) than the full sample (column a, 11 percent), the sample of all movers (column e, 16
percent) and those who move from high- to low-transit areas (column f, 14 percent), echoing the
findings of Glaeser et al (2008). We also observe something notable about transit use and car
ownership: movers in both directions use transit more than their new neighbors (columns f vs.
b and g vs. c; 12 vs. 6 percent and 27 vs. 25 percent). We suspect this may in part be due to other
systematic differences between movers and non-movers, and we explore this in greater detail
TABLE 1. Descriptive Statistics by Transit Quality in Home Tract and Move Status, PSID,
Outcome Data 2003-2013, Exposure Data 1968-2011
When people with a history of living in transit-rich neighborhoods move to a low-transit
area, do they use transit more and own fewer cars than their neighbors? Our data suggest they do.
As table 2 shows, those who recently moved from a high-transit area own fewer cars (0.83 per
adult in the family vs. 0.98 per adult) and are more likely to have used transit in the prior month
(13 percent versus just eight) compared to those who have lived there for two or more years. On
average, these movers spend 40 percent more on transit per adult in the family than their
neighbors do. When we adjust these figures for the presence of children in the family, the results
(a) (b) (c) (d) (e) (f) (g)
Lives in
Lives in
Sig. (b)
vs. (c)
Sig. (d)
vs. (e)
Sig. (f) vs.
Used transit 8% 6% 25% *** 7% 11% *** 12% 27% ***
Ratio of cars to adults in family 0.96 1.00 0.67 *** 1.00 0.86 *** 0.83 0.69 ***
Transit access to jobs
Current z-score -0.02 -0.18 1.30 *** -0.06 0.07 *** -0.33 1.35 ***
Prior exposure (z-scores)
Average exposure -0.02 -0.11 0.66 *** -0.03 0.01 *** -0.03 0.18 ***
Decay -0.06 -0.28 0.97 *** -0.10 0.04 *** 0.12 -0.23 ***
Twenties 0.05 -0.04 0.84 *** 0.03 0.09 *** 0.00 0.34 ***
Formative Years -0.02 -0.07 0.29 *** -0.02 -0.01 -0.03 0.12 ***
Transit JTW share in tract
Current z-score -0.06 -0.22 1.19 *** -0.10 0.05 *** -0.05 1.09 ***
Prior exposure (z-scores)
Average exposure -0.04 -0.15 0.83 *** -0.07 0.03 *** 0.14 0.48 ***
Decay -0.06 -0.19 0.99 *** -0.10 0.04 *** 0.22 0.25
Twenties 0.06 -0.02 0.60 *** 0.03 0.12 *** 0.18 0.58 ***
Formative Years 0.02 -0.07 0.56 *** 0.00 0.04 ** 0.15 0.48 ***
Head and/or spouse is student 1.4% 1.3% 2.5% *** 0.8% 2.9% *** 3.1% 4.5%
Number of adults in family 1.7 1.7 1.5 *** 1.8 1.5 *** 1.5 1.4 ***
Children present in family 30% 31% 26% *** 29% 33% *** 33% 22% ***
Total family income (mean) $78,371 $79,412 $70,009 * $85,391 $61,445 *** $66,004 $69,337
Total family income (median) $49,000 $51,660 $35,000 *** $57,200 $37,800 *** $37,260 $32,918 ***
Family is below poverty line 11% 10% 17% *** 8% 16% *** 14% 22% ***
Immigrant family 8% 7% 16% *** 8% 8% 13% 11%
Age of family head 50.7 51.1 47.0 *** 54.9 40.4 *** 36.0 40.6 ***
Race/ethnicity of head
Non-Hispanic White 75% 78% 54% *** 78% 70% *** 61% 49% ***
Non-Hispanic Asian 2% 2% 3% * 2% 2% 3% 3%
Non-Hispanic Black 14% 12% 28% *** 12% 18% *** 23% 36%
Hispanic of any race 7% 6% 14% *** 7% 8% ** 11% 23%
Other 0.7% 0.8% 0.4% * 0.6% 1.0% * 1.0% 0.2% **
Residential density 4,569 3,089 16,440 *** 4,398 4,980 ** 5,221 12,539 ***
N (person-years) 64,559 56,297 8,262 41,724 22,817 4,351 893
Note: stars indicate statistical significance: * p<0.10; ** p<0.05; *** p<0.01
are the same (not shown here). We note that these are quite large differences, though the actual
magnitude of transit use here is quite small; five or seven dollars’ worth of transit spending
translates to just a couple of rides per month.
TABLE 2. Transit use and car ownership by presence in a low- or high-transit tract and
mover status, PSID, 2003-2013
Because movers and non-movers are systematically different in important ways (movers
are younger and earn less) we also show the statistics for two subsamples: those never in poverty
and young adults. Among families who live in low-transit tracts and never report incomes below
the poverty line, we find somewhat more muted differences for transit spending and auto
ownership. When we examine families headed by a person age 20-35, we also find similar
results; those who have moved from a higher-transit tract own fewer cars and use transit more.
And what of new residents of transit-friendly places? We find little difference between
Did not
High →
Did not
Low →
Full Sample
Ratio of cars to adults 0.98 0.83 *** 0.70 0.69
Monthly spending, fares per adult $4.63 $6.50 *** $10.60 $11.01
Used transit last month 8% 13% *** 22% 27% **
N(person-years) 19,884 4,351 4,813 781
Never-in-Poverty Sample
Ratio of cars to adults 1.06 0.95 *** 0.84 0.88
Monthly spending, fares per adult $4.62 $6.50 ** $8.95 $9.32
Used transit last month 7% 11% *** 22% 27% *
N(person-years) 9,169 1,258 1,290 178
Age 20-35 Sample
Ratio of cars to adults 1.002 0.855 *** 0.692 0.733
Monthly spending, fares per adult $4.74 $6.22 * $17.89 $12.70 *
Used transit last month 8% 11% *** 29% 31%
N(person-years) 3,511 2,773 1,031 411
Note: stars indicate statistical significance: * p<0.10; ** p<0.05; *** p<0.01
Lives in Low-Transit Tract
Lives in High-Transit Tract
those who have lived in high-transit tracts for two or more years and their new neighbors who
have moved from a transit-poor neighborhood. Surprisingly, a slightly higher percentage of
movers reported using transit in the past month (27 vs. 22 percent), and these findings hold up
for the sample of families never living in poverty. These differences are likely explained by the
fact that new residents of high-transit neighborhoods are more likely to be young and to be
students. When we examine only young residents of transit-rich neighborhoods and young
movers, we find no difference in car ownership rates or the likelihood of using transit. The data
suggest that those young people who have moved into these transit-rich neighborhoods from
low-transit places spend less on transit than longer-term young residents do. However, the
magnitude of transit use here is moderate, at two or three transit trips per week, on average.
To explore these differences further, we estimate a series of panel regression models of
transit use and auto ownership. In the following subsections, we present the results.
Prior Exposure Shapes Transit Use
Our analysis suggests that prior exposure to public transportation can influence later
decisions to use transit and to own one or more automobiles. Table 3 shows the results for
current use of public transportation, with prior exposure to public transportation measured four
ways each using two datasets, for a total of eight models.
Our transit measures include the current transit environment in which the family lives,
measured as region-specific z-scores (standard deviations from the mean access to jobs by
transit) and a measure of past exposure to transit. In all models, both the current transit
environment and prior exposure to transit matter. In some of our models, current transit exposure
is a stronger predictor of transit use than is past exposure to transit; in other models, the opposite
is true. In particular, exposure to transit in one’s twenties appears to be a strong predictor of later
transit use, with a standard deviation increase in transit exposure predicting a roughly 30 to 60
percent increase (Model 5:   , model 6:   ) in the odds of using transit.
While this is a large increase in odds, only 10 percent of families used transit at least once in the
previous month; thus, the model suggests that an across-the-board standard deviation increase in
transit exposure would only increase the number of families using transit in a given month to
about 12.8 to 16.2 percent of families.
Our control variables largely function as expected. Family composition influences the
decision to use transit in a number of ways: families in which the head or spouse/partner (where
present) is a student are far more likely to use transit than are other households, and larger
households (more adults or children) are associated with lower odds of using transit across all
eight models. Similarly, families in which the head or spouse/partner were born outside the US
are more likely to use transit than are US-born families. Controlling for other variables in the
model, non-Hispanic Asian and black families are more likely to use transit than are Non-
Hispanic white families. The model suggests that poor families are far more likely to use transit
than are non-poor families, but that for families not in poverty, the probability of using transit
increases with income, in line with previous research (Pucher and Renne 2003). Geography
matters for transit use, too. All else equal, families that live in denser areas are more likely to use
transit, with a roughly seven percent increase in the odds of using transit for each additional
thousand persons per square mile in the home census tract.
We include year-specific intercepts to control for other factors that vary over time but
which cannot be included in the model. We find moderate year-specific effects, with a peak in
2009 and low points in 2005 and 2013.
Overall, we observe consistent findings across eight separate measurements of “transit
exposure.” The strong effect of transit exposure during one’s formative years (ages five to 18)—
when one has little or no say in one’s residential location—suggest that the results are not simply
an artifact of the self-selection of individuals with a preference for riding transit into those
neighborhoods where they can, in fact, ride transit.
Prior Exposure Shapes Auto Ownership
Our models of auto ownership tell a similar story. As Table 4 shows, current transit
access to jobs as well as prior exposure to public transportation have a strong and statistically
significant effect on the level of car ownership in a family. In general, a standard deviation
increase in today’s transit access is associated with a one to four percent decrease in the ratio of
cars to adults in the family. A standard deviation increase in one’s prior transit exposure is
associated with a three to eight percent decrease in auto ownership depending on the measure of
exposure. We note, however, that in four of the eight models the coefficients for current and
prior transit exposure are not statistically different from one another; we thus suggest that the
effects of current and prior exposure to transit are “roughly equal” in our models. Again, we note
the strong consistency in model results across all eight models, and particularly in the “formative
years” (age 5 to 18) measure of transit exposure, which likely controls for residential self-
In these models, our control variables also perform as expected. The presence of children
is associated with an increase in the ratio of cars to adults in the household, while income
increases auto ownership. Living below the poverty line has a strong negative association with
car ownership, with a roughly ten or eleven percent decrease in cars per adults. Controlling for
other variables in the model, immigrant families own fewer cars, and all else equal, non-Hispanic
blacks have lower rates of car ownership in all eight models. Greater residential density is
similarly associated with lower levels of car ownership, with an increase of 1,000 persons per
square mile associated with a half percent decrease in cars per adult in the family. The temporal
trends are somewhat less clear, with slight peaks in the early 2000s and in 2007-9, though the
magnitude of the differences is small.
TABLE 3 Random-Effects Panel Logistic Regression Model of Transit Use in Family, United States, Outcome Data 2003-2013,
Exposure Data 1968-2011
Transit exposure metric:
Transit data source: Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig.
Current transit (z-score, measured two ways) 0.223 *** 0.237 *** 0.199 *** 0.275 *** 0.313 *** 0.266 *** 0.233 *** 0.333 ***
Transit exposure (z-score, measured four ways) 0.250 *** 0.462 *** 0.194 *** 0.221 *** 0.248 *** 0.480 *** 0.174 *** 0.260 ***
Head and/or spouse is student 0.865 *** 0.660 *** 0.948 *** 0.659 *** 1.130 *** 0.931 *** 0.923 *** 0.651 ***
Number of adults in family -0.129 *** -0.154 *** -0.107 *** -0.151 *** -0.117 *** -0.112 *** -0.103 *** -0.093 ***
Children present in family -0.262 *** -0.335 *** -0.267 *** -0.315 *** -0.140 *** -0.134 *** -0.151 *** -0.274 ***
Total family income (log-transformed) 0.136 *** 0.137 *** 0.146 *** 0.135 *** 0.140 *** 0.134 *** 0.169 *** 0.157 ***
Family is below poverty line 0.408 *** 0.323 *** 0.425 *** 0.346 *** 0.519 *** 0.540 *** 0.719 *** 0.557 ***
Immigrant family 0.503 *** 0.612 *** 0.478 *** 0.687 *** -0.417 *** -0.174 *** -0.128 ** -0.002
Age of family head 0.072 *** 0.071 *** 0.068 *** 0.071 *** 0.120 *** 0.121 *** -0.032 *** -0.006
Age of family head, squared -0.0010 *** -0.0010 *** -0.0010 *** -0.0010 *** -0.0014 *** -0.0013 *** 0.0003 *** 0.0000
Race/ethnicity of head (omitted: non-Hispanic white)
Non-Hispanic Asian 0.355 *** 0.620 *** 0.394 *** 0.667 *** 0.850 *** 1.010 *** 0.426 *** 1.100 ***
Non-Hispanic Black 0.644 *** 0.445 *** 0.668 *** 0.692 *** 0.740 *** 0.398 *** 0.367 *** 0.298 ***
Hispanic of any race -0.669 *** -0.570 *** -0.596 *** -0.511 *** 0.333 *** 0.095 * -0.203 *** -0.174 ***
Other -0.395 *** -0.754 *** -0.163 -0.752 *** -1.290 *** -1.670 *** -0.844 *** -0.868 ***
Residential density in thousands 0.063 *** 0.071 *** 0.062 *** 0.073 *** 0.075 *** 0.088 *** 0.076 *** 0.084 ***
Ratio of cars to adults in family -2.230 *** -2.160 *** -2.250 *** -2.180 *** -2.080 *** -1.960 *** -2.470 *** -2.380 ***
Year (omitted: 2003)
2005 0.088 *** 0.063 *** 0.103 *** 0.037 ** 0.049 * -0.027 -0.126 *** -0.085 ***
2007 0.079 *** 0.061 *** 0.061 *** 0.064 *** 0.133 *** 0.073 *** -0.178 *** -0.219 ***
2009 0.237 *** 0.256 *** 0.251 *** 0.260 *** 0.140 *** 0.194 *** -0.075 *** -0.078 ***
2011 0.135 *** 0.180 *** 0.161 *** 0.182 *** -0.042 -0.024 -0.133 *** -0.084 ***
2013 0.041 ** 0.089 *** 0.029 0.078 *** -0.021 0.010 -0.250 *** -0.194 ***
Intercept -4.550 *** -5.150 *** -4.580 *** -5.190 *** -6.270 *** -7.040 *** -2.460 *** -3.530 ***
N 31,510 52,163 29,870 50,587 16,887 27,061 15,823 23,149
Pseudo R-squared 0.140 0.137 0.142 0.138 0.140 0.136 0.119 0.117
Rho (proportion of variance explained by panel-level variance) 0.61 0.61 0.61 0.61 0.61 0.61 0.56 0.57
Note: stars indicate statistical significance: * p<0.10; ** p<0.05; *** p<0.01
Formative Years (5-18)
Transit Access
Transit Access
Average Exposure
Transit Access
Transit Access
TABLE 4 Random-Effects Panel OLS Model of Ratio of Cars to Adults in Family, United States, Outcome Data 1999-2013,
Exposure Data 1968-2011
Transit exposure metric:
Transit data source: Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig.
Current transit (z-score, measured two ways) -0.024 *** -0.013 *** -0.031 *** -0.022 *** -0.035 *** -0.029 *** -0.036 *** -0.039 ***
Transit exposure (z-score, measured four ways) -0.066 *** -0.075 *** -0.030 *** -0.029 *** -0.064 *** -0.064 *** -0.047 *** -0.045 ***
Head and/or spouse is student 0.030 * 0.015 0.035 * 0.014 0.055 * -0.064 * 0.027 0.013
Number of adults in family -0.137 *** -0.150 *** -0.138 *** -0.151 *** -0.157 *** -0.064 *** -0.139 *** -0.153 ***
Children present in family 0.023 *** 0.037 *** 0.026 *** 0.038 *** 0.035 *** -0.064 *** 0.025 *** 0.034 ***
Total family income (log-transformed) 0.043 *** 0.044 *** 0.043 *** 0.044 *** 0.047 *** -0.064 *** 0.044 *** 0.053 ***
Family is below poverty line -0.122 *** -0.124 *** -0.119 *** -0.126 *** -0.129 *** -0.064 *** -0.144 *** -0.134 ***
Immigrant family -0.096 *** -0.087 *** -0.095 *** -0.096 *** -0.025 -0.064 -0.032 -0.006
Age of family head 0.031 *** 0.034 *** 0.031 *** 0.035 *** 0.024 *** -0.064 *** 0.023 *** 0.022 ***
Age of family head, squared -0.0003 *** -0.0004 *** -0.0003 *** -0.0004 *** -0.0002 *** -0.064 *** -0.0002 *** -0.0002 ***
Race/ethnicity of head (omitted: non-Hispanic white)
Non-Hispanic Asian 0.012 -0.034 0.001 -0.035 0.097 * 0.012 0.026 -0.035
Non-Hispanic Black -0.208 *** -0.177 *** -0.229 *** -0.219 *** -0.233 *** -0.179 *** -0.234 *** -0.174 ***
Hispanic of any race -0.036 ** -0.046 *** -0.046 *** -0.053 *** -0.023 -0.032 -0.037 -0.057 **
Other -0.033 -0.052 * -0.060 -0.075 ** -0.024 -0.057 -0.091 -0.113 **
Residential density in thousands -0.005 *** -0.006 *** -0.005 *** -0.006 *** -0.005 *** -0.006 *** -0.005 *** -0.006 ***
Year (omitted: 1999)
2001 0.022 *** 0.014 ** 0.019 ** 0.012 * 0.014 0.001 0.017 -0.005
2003 0.039 *** 0.035 *** 0.037 *** 0.033 *** 0.024 ** 0.024 ** 0.029 ** 0.025 **
2005 0.010 0.000 0.007 -0.002 -0.002 -0.012 0.010 0.007
2007 0.028 *** 0.027 *** 0.027 *** 0.026 *** 0.002 0.014 0.001 0.013
2009 0.038 *** 0.030 *** 0.039 *** 0.030 *** 0.022 * 0.013 0.025 * 0.018
2011 0.016 ** 0.007 0.016 * 0.008 -0.010 -0.017 0.002 -0.008
2013 0.012 0.000 0.009 -0.001 -0.013 -0.032 *** -0.012 -0.030 **
Intercept 0.067 * 0.041 0.077 ** 0.047 0.188 ** 0.167 ** 0.224 *** 0.190 ***
N 40,145 66,670 37,983 64,559 21,169 33,795 19,428 28,270
R-squared (within) 0.049 0.046 0.048 0.046 0.040 0.044 0.043 0.034
R-squared (between) 0.265 0.263 0.272 0.259 0.270 0.233 0.227 0.270
R-squared (overall) 0.265 0.167 0.196 0.166 0.151 0.161 0.132 0.185
Rho (proportion of variance explained by panel-level variance) 0.44 0.4 0.45 0.41 0.470 0.42 0.4 0.34
Note: stars indicate statistical significance: * p<0.10; ** p<0.05; *** p<0.01
Transit Access
Transit Access
Transit Access
Average Exposure
Transit Access
Formative Years (5-18)
Given that our analysis suggests that previous exposure to high-quality public transit can
influence travel behavior later in life, we extended our analysis to examine how exposure at
different ages can influence later outcomes. Here, we use one exposure method, a moving
average of prior exposure to transit, and model only one outcome, transit spending. We also limit
our analysis to the Census-based journey-to-work measure of transit quality.
We estimated a series of models to test the effect of past transit exposure during various
periods in one’s life, from birth through 40 years of age. Our models are identical to those above,
but we substitute our new exposure measures. Figure 1 shows a conceptual diagram of these
models. We use three different moving average windows: three, five and seven years, and we
include these separately in distinct models (e.g. for the three-year average, we would use
exposure at ages 10, 11, and 12 in one model of current transit use, exposure at ages 11, 12 and
13 in another, and so forth). Our exposure measure is the average of the share of workers in the
persons home census tract who used public transit to commute during the age for each moving
average window (e.g. for the five-year moving average, N through N+5 years old). We only
include observations in our model if the person is older than the moving average window
(otherwise these transit exposures would not be in the past); additionally, we estimated models
that restricted how recent the exposure could be (at least one year, at least three years, and at
least five years in the past), though, for most observations, the exposure metric lay much further
in the past. Using the three different moving average windows, the three time lags and the 40
different periods included in each moving average window, we ran 360 logistic regressions.
FIGURE 1 Conceptual Diagram of Moving-Window Models, Outcome Data 2003-2013,
Exposure Data 1968-2011
Figure 2 charts the coefficients for the past transit exposure and current transit
environment for the three moving average windows and the three time lags across the 40 moving
average periods. This analysis suggests that exposure to transit is particularly consequential
during one’s 20s and 30s. The points on the graph indicate the coefficients from all models,
while the lines plot the average of coefficients across our various models. The x-axis of this
graph indicates the midpoint for the moving average for the past exposure measure, not the age
of the person during the survey year. For example, the points at age 10 show the effect of
exposure to transit at age ten (in dark grey) along with the effect of the current transit
environment for all survey respondents (all of whom are older than ten). In this model, exposure
to transit at age 10 exerts a meaningful influence on current transit use, though the current transit
environment has a larger effect.
As the figure suggests, a person’s exposure to public transit during the period from their
early 20s to the late 30s has the strongest effect on their later travel behavior, even larger than the
effect of their current transit environment. For someone who is say 40, 50 or 60 years old, this
suggests that the quality of public transportation where they lived when they were 30 years old
has twice the effect on their current travel than the transit in their surrounding neighborhood. We
separately estimated a series of models using our alternative measure of transit access (jobs
accessible in 30 minutes on transit), and the results were similar, though the effect was muted, as
it was in our models presented above (Table 3).
FIGURE 2 Effect of Past Transit Exposure and Current Transit Environment on Transit
Use as a Function of Age used for Exposure Metric, Outcome Data 2003-2013, Exposure
Data 1968-2011
Why might exposure to transit in one’s late 20s and 30s be of particular importance? We
hypothesize that several factors may be at play during this period. This is typically the time of
life when people begin to “settle down” by establishing long-term relationships, having children,
and, for some, settling into a longer-term job. Beige and Axhausen (2012) note that residential
moves, job changes and changes in transportation are much more common between ages 15 and
35 than after. This may enhance habit formation, or it may simply mean that there are fewer
“shocks” in the future that may cause a re-evaluation of location choices and travel patterns.
The figure appears to show that the importance of exposure to transit in one’s 40s and
one’s current transit environment are about equal. However, this convergence is likely an artifact
of our method; for earlier-exposure models (in one’s 20s, for instance), many of the people
included in the model are much older than 20 (because being 20 is, roughly, only one-quarter of
the way “through” one’s life); for our models using exposure during one’s 40s, a larger share of
people included in the model will be in their 40s or early 50s, and are much more likely to
continue to live in the same transit environment. Thus, the convergence we observe to the right
of our graph may be at least in part due to the shrinking “distance” between prior exposure and
one’s current environment.
In response to reviewer suggestions, we also estimated several alternative model
specifications, which we describe here. In each case, the results confirmed our preferred models
presented above, though there are several intriguing differences.
Because early exposure to transit may result in later residential self-selection into
neighborhoods with high-quality transit service, we estimated three models to explore this. First,
we estimated our models as above, but excluded the variables describing one’s current built
environment (transit access to jobs and residential density). In each these models, the effect size
of our variables describing prior exposure to transit roughly doubled compared to our preferred
models presented above. We take this to mean that when it comes to the effect of prior exposure
to transit on current transportation choices, some of the effect may be due to residential self-
selection, but experiences in earlier contexts can also shape transportation preferences and habits
independent of self-selection.
We also estimated models using a subset of respondents who live in radically different
environments from those where they grew up: city kids who live their adult lives in exurban and
rural communities, and rural and exurban children who moved to dense urban environments. In
these models, we find a strong effect of prior exposure on auto ownership. Rural kids who move
to the city consistently own more cars than their neighbors who grew up in the city and the
opposite is true for city kids who move to rural or exurban places.
Because a small number of urban areas account for nearly all transit ridership in the US,
we estimated two models with dummy variables for living in, or growing up in these metro areas.
We find that living in or growing up in New York, Chicago, San Francisco, Washington DC,
Boston, or Philadelphia provides an additional “boost” to transit use, though the results for the
rest of the nation remain essentially unchanged from the preferred models presented above.
Finally, we evaluated a panel logit model of car ownership which would be directly
comparable with our model of transit usage. In this version of the model, our outcome variable
was one if the family had sufficient automobiles (at least one per adult) and zero otherwise.
The model suggests a halving in the likelihood of having sufficient autos for each adult when
exposure to transit in the past increases by a standard deviation.
Exposure to high-quality public transportation during one’s life can encourage later
transit use and lower auto ownership, even if one lives in a less transit-friendly environment. Our
analysis suggests a strong and robust linkage, and this linkage holds even when we examine
exposure to transit at a young age, when one has no say in where one lives.
In this article, our primary aim was to examine the effects of where a person has lived on
their current travel behavior. While many researchers often seek to disentangle the “built
environment effect” for the broader population from the “self-selection effect” for those who end
up living in those places for reasons of preference, we are interested in ascertaining whether
current travel is related to prior experiences, regardless of whether this is the result of self-
selection or simply being more open to using transit or some other reason.
Our findings suggest something for policy. Transit agencies and advocates could “plant
the seed” for future ridershipin addition to providing an important social serviceby
providing free or reduced-price transit passes for school or university students and targeted
programs for recent movers or new employees to encourage a transit habit. These types of future
payoffs may be difficult to quantify and incorporate in traditional cost-benefit analysis, though
our research suggests the payoffs may be substantial. Additionally, the growth in urban
populations over the past decade, particularly the increase young people living in cities (Myers
2016), could lay a foundation for transit use in the years to come. Our analysis suggests that
where someone lives during their 20s and 30s is particularly consequential for future travel
behavior. Even if many of the younger cohort do move out to the suburbs, our work suggests that
they will take some of their habits with them.
Although we find a relationship between past exposure to transit and future transit use
and car ownership, much about this relationship remains a mystery. First, we do not know how
this linkage forms. Perhaps, as some have suggested, preferences are developed through
exposure to transit-rich areas (Weinberger and Goetzke 2010; Macfarlane, Garrow, and
Mokhtarian 2015). Thus, the attitudes and preferences inherent in the self-selection effect
observed in many studies of transportation and land use may be shaped by prior experiences and
are likely mutable. Additionally, living in neighborhoods where many people use transit daily
could normalize transit ridership. Psychologists have long argued that individual behavior is
influenced to some degree from social learning and observations of other’s actions (e.g. Bandura
1977). Observing neighbors using transit could act as in the same way, providing a social model
that subtly encourages transit use. Alternatively, the processes could be more utilitarian rather
than social. Using transit at younger ages could be an individual learning process that individuals
carry with them later in life.
We do not know what dosage of transit is required to influence travel later in life. The
emerging consensus is that there is some effect, but we find a larger effect than previous studies
that used different data and measured different outcomes (Weinberger and Goetzke 2010;
Macfarlane, Garrow, and Mokhtarian 2015). Following Galster (2012), we suggest that there are
still many outstanding questions about the mechanisms for this “neighborhood effect.” We do
not know what levels of transit service are necessary early in life to lead to a lifelong habit of
transit use and decreases in car ownership. We also do not know if these effects are universal and
how the dosage of transit interacts with other factors. Chen et al. (2009) suggest at least one life-
cycle factor, parenthood, that moderates the effect of previous residential accessibility on
commute distance. Future research could shed light on the mechanisms and nuances of this
relationship to help guide policy.
We add to the growing body of knowledge that suggests thatwhile microeconomic
rationality likely drives the bulk of travel decisionssocial factors work at the margins to shape
these decisions. Our work suggests that experiencing high-quality transit earlier in life can lead
to a decrease of a couple of percentage points in car ownership rates and a meaningful increase
in the likelihood (moving, roughly, from very unlikely to simply “unlikely in the U.S.
context) of using transit once or more a month.
We obtained access to the restricted version of the Panel Study of Income Dynamics from
the Institute for Social Research at the University of Michigan, Ann Arbor. The collection of
data used in this study was partly supported by the National Institutes of Health under grant
number R01 HD069609 and the National Science Foundation under award number 1157698.
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... They have also been exposed to a wide application of information and communication technology since an early age (Circella & Mokhtarian, 2017). Such experiences, associated with the economic hardship and the delayed life cycles that the Millennials have experienced, might be able to influence their automobile travel behavior if they reside in suburban neighborhoods at later stages of their life (Smart & Klein, 2017) by influencing their activity patterns, cognitive maps and attitudes and beliefs (Tal & Handy, 2010). Thus, the relationship between the built environment and automobile travel among Millennials may be different from that of previous generations, even with the same socioeconomic conditions and life cycle stages. ...
... In 2017, the US economy has recovered from the recent recession, which was at its deepest when the 2009 NHTS was conducted. These findings using the 2017 data supports the arguments of Polzin et al. (2014) and Smart and Klein (2017) that some characteristics in travel may be consistent over time regardless of economic conditions. Pseudo R-squared 0.0133 0.0279 Note: Standard errors in parentheses, *, **, *** indicate p<0.1, p<0.05 and p<0.01, respectively. ...
... For instance, a recent survey in California has shown that Millennials' attitudes toward lifestyles and travel are different from those of Gen Xers (Circella et al., 2017). Staying in neighborhoods with a sufficient supply of transit, good walking environment, and additional opportunities for carpooling during young adulthood might encourage Millennials to explore various ways of travel (Smart & Klein, 2017). In addition, the future travel patterns of urban and suburban Millennials will be shaped by city-specific contexts (Delbosc et al., 2019). ...
Using U.S. nationwide travel surveys for 1995, 2001, 2009 and 2017, this study compares Millennials with their previous generation (Gen Xers) in terms of their automobile travel across different neighborhood patterns. At the age of 16 to 28 years old, Millennials have lower daily personal vehicle miles traveled and car trips than Gen Xers in urban (higher-density) and suburban (lower-density) neighborhoods. Such differences remain unchanged after adjusting for the socio-economic, vehicle ownership, life cycle, year-specific and regional-specific factors. In addition, the associations between residential density and automobile travel for the 16- to 28-year-old Millennials are flatter than that for Gen Xers, controlling for the aforementioned covariates. These generational differences remain for the 24- to 36-year-old Millennials, during the period when the U.S. economy was recovering from the recession. These findings show that, in both urban and suburban neighborhoods, Millennials in the U.S. are less auto-centric than the previous generation during early life stages, regardless of economic conditions. Whether such difference persists over later life stages remains an open question and is worth continuous attention.
... However, [24] argues that residues of past behavior resulting from previous habits influence attitudes and the perceived self-efficacy in a new place (for our study, regarding the transport options available). Moreover, empirical evidence links previous exposure to high-quality public transport with higher public transport use [25]. In this regard, past habits and experiences can contribute to expectations regarding the use of different transport options in the host city, making international students and researchers more inclined to use transport options similar to those used in their previous city of residence. ...
... Hypothesis 2 (H2). Past frequency of PT use affects the current frequency of PT use and the initial perception about the easiness to use PT in the new city [24,25]; Hypothesis 3 (H3). Residential location (which defines accessibility level) influences the current frequency of PT use [32] and satisfaction with PT [39]; Hypothesis 4 (H4). ...
... Hypothesis 2 (H2). Past frequency of PT use affects the current frequency of PT use initial perception about the easiness to use PT in the new city [24,25]; Hypothesis 3 (H3). Residential location (which defines accessibility level) influences the frequency of PT use [32] and satisfaction with PT [39]; Hypothesis 4 (H4). ...
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Travel behavior adaptations resulting from international temporary relocation is understudied, despite their increasing relevance. The scarce published literature on the subject overlooks the local contexts and ignores aspects related to the adaptation processes and motivations. This study aims to partially fill this gap by addressing the travel behavior adaptation of international students and researchers, focusing on public transport (PT) frequency of use and satisfaction. To investigate this, a Bayesian Structural Equation Model was estimated using data collected from a tailor-made online survey answered by temporary international exchange students and researchers. The model confirms that (i) travel behavior habit in the city of origin influences the residential location choice in the host city; (ii) the higher the frequency of PT use in the city of origin, the higher the PT use in the host city; (iii) the residential location in the host city affects individuals’ frequency of PT use and satisfaction; (iv) perceiving technology as helpful to move around leads to perceiving the PT system as easier to use at the beginning of the stay; (v) perceiving the PT as easier to use, leads to a higher frequency of its use and a higher level of satisfaction with the PT system.
... On one hand, the "demographic perspective" sees the generational differences in attitudes towards automobility as a by-product of the experiences in early life stages and may be constant over time. For instance, Smart and Klein (2018) find that if a person is exposed to high-quality public transit services during young adulthood, this experience affects his or her travel behavior in later life stages. On the other hand, the "life course perspective" argues that the generational differences in automobility may be due to different "schedules" in reaching life milestones (e.g., being employed, getting married, etc.) for people born in different time periods (Delbosc and Nakanishi, 2017;Scheiner and Holz-Rau, 2013;Susilo et al., 2019). ...
Whether the Millennials are less auto-centric than the previous generations has been widely discussed in the literature. Most existing studies use regression models and assume that all factors are linear-additive in contributing to the young adults' driving behaviors. This study relaxes this assumption by applying a non-parametric statistical learning method, namely the gradient boosting decision trees (GBDT). Using U.S. nationwide travel surveys for 2001 and 2017, this study examines the non-linear dose-response effects of lifecycle, socio-demographic and residential factors on daily driving distances of Millennial and Gen-X young adults. Holding all other factors constant, Millennial young adults had shorter predicted daily driving distances than their Gen-X counterparts. Besides, residential and economic factors explain around 50% of young adults' daily driving distances, while the collective contributions for life course events and demographics are about 33%. This study also identifies the density ranges for formulating effective land use policies aiming at reducing automobile travel demand.
... In addition, researchers do not yet have definitive answers on the ways in which attitudes might change in response to external factors. Still, studies and anecdotal evidence suggest that education, past exposure, and mobility culture are among factors accounting for individuals holding specific attitudes, in the context of sustainable transportation (Klinger & Lanzendorf, 2016;Macfarlane et al., 2015;Smart & Klein, 2018). Based on the findings from our study, we advise planners to consider educational campaigns (e.g., health benefits of active travel), information distribution (e.g., where/how to take transit, walk, and bike nearby, especially for recent movers from dense neighborhoods with transit/active-mode-oriented mobility culture), and the designation of zones with limited vehicle traffic (i.e., promotion of "local" mobility culture). ...
In this study, we explore the heterogeneous impacts of ridehailing on the use of other travel modes using survey data (N = 1,438) collected from June to October 2019 (i.e., before the COVID-19 pandemic) across three regions in southern U.S. states: Phoenix, Arizona; Atlanta, Georgia; and Austin, Texas. We apply a latent-class cluster analysis to indicators of changes in the use of various travel modes as a result of ridehailing adoption, with covariates of socioeconomics, demographics, a land-use attribute, and individual attitudes. We identify four distinctive latent classes of behavioral changes in response to the use of ridehailing. About half of ridehailing users in the sample (49.7%) are found to behave as Mobility augmenters, who use ridehailing rarely, in addition to other travel modes, and do not change their travel routines much as a result of the adoption of this mobility service. The second largest class includes Exogenous changers (24.5%), whose members report many changes in their use of various travel modes, but which can be largely explained by other reasons. Private car/taxi substituters (15%) frequently hail a ride, and as a result, reduce their use of private vehicles while making more trips by public transit and active modes, as the result of using ridehailing. Interestingly, Transit/active mode substituters (10.8%) often use ridehailing, likely for trips that they previously made by public transit or active modes, and consequently reduce their use of these less-polluting modes while enjoying enhanced mobility. This study reveals substantial heterogeneity in ridehailing impacts, which were masked in previous studies that focused on average impacts, and it suggests that policy responses should be customized by users’ socioeconomics and residential neighborhoods.
... Previous studies show that living outside urban areas (e.g. Delbosc and Currie, 2014;LeVine and Polak, 2014;Shults and Williams, 2013;Tefft et al., 2014;Zhao and Bai, 2019), and limited access to alternative travel modes (Delbosc et al., 2019;Smart and Klein, 2018) increase the likelihood of early licensing. Young people's car use is also known to be influenced by the level of urbanization of their neighborhood (Melia et al 2018) as well as the car use of their parents and peers Sigurðardóttir, et al., 2014). ...
... Previous studies show that living outside urban areas (e.g. Delbosc and Currie, 2014;LeVine and Polak, 2014;Shults and Williams, 2013;Tefft et al., 2014;Zhao and Bai, 2019), and limited access to alternative travel modes (Delbosc et al., 2019;Smart and Klein, 2018) increase the likelihood of early licensing. Young people's car use is also known to be influenced by the level of urbanization of their neighborhood (Melia et al 2018) as well as the car use of their parents and peers Sigurðardóttir, et al., 2014). ...
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In Denmark, the legal license age was lowered from 18 to 17, to allow practice with an experienced driver before solo driving from age 18. The change gives the candidate driver a choice between: a) licensing at age 17 followed by a phase of accompanied driving until solo driving at age 18 (L17), and b) licensing at age 18 (or older) giving immediate access to solo driving (L18). The purpose of this study is: First, to explore safety-related differences between youth choosing the L17 or the L18 option, with a particular focus on safety attitude and self-assessed driving skills. Second, to map patterns in the use of accompanied driving and its predictors as well as the interaction between the L17 driver and the accompanying person (ACP). A sample of 632 drivers (53% male) between 17 and 19 years of age completed a survey. Among the participants 61% licensed through L17 and 39% through L18. Our results identify different risk profiles between L17 and L18. A higher score on perceptual-motor skills, lower score on safety skills and lower support to speed limits predicted L17. Female L17 were more safety-oriented compared to male L17. L17 who had experienced a supportive atmosphere and engagement in complex traffic situations during the drive were more likely to indicate that accompanied driving had improved their driving skills. However, results also indicate that the amount of experience obtained by L17 may be insufficient to obtain a safety benefit. Measures to address speeding and other risk-taking behaviours among male candidate drivers are needed to ensure a safety benefit of the Danish accompanied driving scheme. In addition, requirements may be needed to increase the amount of accompanied driving. Finally, parent guidelines could support the creation of a positive atmosphere during the drive.
Millennials are the largest generation in the current U.S. population. Their travel preferences and choices have profound implications for the travel industry and transportation policy making. The existing literature, however, has presented mixed findings on whether Millennials differ from their preceding generations in vehicle usage, walking or biking, and transit riding. Furthermore, the majority of the existing studies investigated generational travel at the national level; few have explored the spatial variation of generational travel at the subnational scale. This study examines individuals’ modal shares in daily travel by Millennials, Generation X, and Baby Boomers across megaregions. A unique dataset is assembled with national travel surveys from 1977 to 2017, covering the age spectrum from five years to 71 years for the three generations. The study applies multilevel modeling to capture the dynamic effects associated with generational, megaregional, and period variations on individuals’ modal share. Results of the study show the varying trends of modal shares in different life stages between generations. Millennials in adulthood maintain the highest walk/bike share and the lowest share of vehicle travel among all generations. Megaregional variations exert differentiated influences on individuals’ mode share patterns across generation subgroups. The varying trends of modal shares over the age spectrum across generations highlight the importance of having cohort-tailored initiatives to achieve sustainable transportation objectives. The study’s quantification of megaregional and generational variations on modal shares provides useful information for modal split analysis and other transportation planning practices at the level between states and metropolitan areas.
Although many studies examine the relationship between the built environment and car ownership in large cities, few focus on smaller cities in developing countries. Their nonlinear and interaction relationships are often neglected. Using the 2019 data from Zhongshan, a medium-sized city in China, we employed gradient boosting decision trees to estimate the nonlinear and interaction effects of the built environment and motorcycles/E-bikes on car ownership. We found that wealth plays a crucial role in households’ car ownership decisions. Most built environment variables have threshold associations with car ownership, but the size of the associations is limited. The findings suggest that polycentricity and densification around centers help mitigate the growth of cars. More importantly, motorcycles and E-bikes, particularly owning a second one, attenuate the positive effects of income and/or distance to city center on car ownership. This challenges the policies of banning motorcycles and E-bikes.
The decline in driver’s licensure among teenagers in the U.S. in recent decades has led some observers to speculate that the newest generation of adults will be less car dependent than those that preceded them. Previous studies have identified a variety factors that may explain the decline, including graduated licensing policies and economic conditions. This paper delves beneath these trends with the goal of generating a deeper understanding of what is going on with teenagers and their travel. We explore what teenagers think about driving and its alternatives through in-depth interviews with 20 high school students and their parents in Davis, California, an unusually bicycling-oriented community by U.S. standards. Although bicycling was an important mode of travel for the teenagers when they were younger, all had acquired or planned to acquire a driver’s license at the time of the interview. The reasons teenagers cite for needing a driver’s license are more practical than social, though the ability to socialize with friends is an important benefit of driving. Both teenagers and their parents liked the independence that having a license brings, though both expressed some fears about their driving – both the danger driving poses to themselves as well as the danger their driving poses to others. Teenagers and their parents saw driving as inevitable, as a natural step towards adulthood.
Life course approaches to travel seek to understand the dynamics of travel behavior over the life course. This concept, often labeled “mobility biographies,” has recently generated a multitude of studies. The chapter discusses key concepts that may help understand the mechanisms that contribute to stability and change in mode choice. Specifically, it discusses levels of change and stability (ranging between the individual and “the system”), factors that serve resistance to change, factors that trigger change, and the role of socialization in stability and change. The chapter concludes with an outline of research needs. This includes the development and test of stronger theories, developing stronger links between qualitative and quantitative approaches, linking mobility biographies with research on the social embedding of travel, looking at interactions between life domains, behavioral dimensions, and population groups, and the further development of policy approaches.
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The rise and fall of the Millennial generation congregating in central cities is a product of life course meeting unique historical context. Three reinforcing cycles harmonized before 2010 to maximize Millennial presence, and then will harmonize in 2020 to reduce presence. In 2015, the peak Millennial birth cohort passed age 25, with smaller cohorts to follow. Job opportunity that had sharply worsened following the Great Recession is reversing, with renewed job growth opening entry positions, and with less competition from smaller cohorts. In housing, Millennials were doubled up at entry levels of their housing life cycle, blocked by older peers who were unable to turn over their apartments for better homes. With renewal of new construction and home buying, stronger vacancy chains will again stimulate outflow. The combined effect of the three reversed cycles will reduce central concentrations of young adults. Preferences may persist for urban walkability but, freed of their former constraints, preferences will now be expressed through choice from a broader range of locales. Cities and suburbs can compete for Millennials passing age 30 with walkable districts, transit, and better schools and housing.
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The mobility biography approach is increasingly recognised in travel behaviour studies. Related empirical studies analyse key events and mobility experiences over the life course of individuals. In addition to these personal experiences, social context and socialisation through family members play an important role in this respect. This paper presents a theoretical framework for analysing commute behaviour over the life course of different generations and gives preliminary empirical results. The empirical work is based on a retrospective survey conducted annually since 2007 at TU Dortmund University (Germany). The relationship between the travel mode use, the individual and parental attitudes towards travel modes, and residential locations over the life course is investigated. Our findings indicate that attitudes and residential locations of the younger generation in a family are associated with the same variables of their parents. The residential characteristics and attitudes in turn are significant predictors for travel mode use on commute trips. These preliminary results indicate the relevance of socialisation effects for commute mode choice.
Following scant evidence for the effects of proximity to rail transit on car use, we pinpoint the impacts of rail transit and neighbourhood characteristics on both transit and car use in the Minneapolis-St. Paul metropolitan area. We apply the structural equations modelling approach on 597 residents who moved into the Hiawatha Light Rail Transit (LRT) corridor after it opened. The analysis is based on a self-administrated survey where all attributes of the built environment and transit quality are perceived measures. Using a quasi-longitudinal design to compare the behaviour of movers into the Hiawatha and control corridors, we found that the Hiawatha LRT acts as both a catalyst and a magnet. Movers into the Hiawatha corridor experience transit improvement, which increases transit use and reduces car use. The LRT also enables transit-liking people who were unable to realise their preference previously to relocate near the LRT. However, the LRT has no significant effects on changes in car ownership.
The purpose of this article is to add another dimension to our understanding of travel behaviour by highlighting how individual decisions about travel are simultaneously influenced by both rational, calculable metrics of the transportation system but also by socially constructed, context-specific perceptions that travellers hold about the travel modes themselves. The context for this study is a rapid transformation of the market for intercity buses in the Northeast United States. In the past 15 years, new entrants have transformed a humdrum industry into a dynamic sector of the intercity travel market. The new entrants, curbside buses, have largely shunned traditional bus terminals in favour of picking up and dropping off bus passengers on city streets. Ridership has steadily increased, and these new bus companies have expanded operations throughout the country. Drawing on a series of focus groups with intercity bus passengers, I describe how two sets of factors drive intercity travellers’ choice to travel onboard the new intercity buses. First, the new companies offer operational and economic advantages. Second, and surprisingly, focus group participants have different perceptions of the new bus companies than the old – and these perceptions appear to be influencing their travel decisions.
Although there is now a large body of empirical research on neighbourhood effects, we know relatively little about the causal mechanisms responsible for relationships between neighbourhood attributes and individual outcomes. A list of 15 potential causal pathways which may lead to neighbourhood effects is given, grouped into four categories: social-interactive mechanisms, environmental mechanisms, geographical mechanisms, and institutional mechanisms. The ultimate goal of neighbourhood effects research is not only to identify which mechanisms are responsible for neighbourhood effects, but also to quantitatively ascertain their relative contributions to the outcome under investigation. A pharmacological metaphor of dosage-response is used to understand how the theoretical mechanisms could be causally linked to individual outcomes. This metaphor refers to questions regarding the composition and the administration of the neighbourhood dosage, and the neighbourhood dosage-response relationship. This chapter concludes that despite the ever growing literature on neighbourhood effects, there is far too little scholarship to make many claims about which causal links dominate for which outcomes for which people in which national contexts and any conclusions on the existence of such effects should be treated as provisional at best. © 2012 Springer Science+Business Media B.V. All rights reserved.
Problem: Localities and states are turning to land planning and urban design for help in reducing automobile use and related social and environmental costs. The effects of such strategies on travel demand have not been generalized in recent years from the multitude of available studies.Purpose: We conducted a meta-analysis of the built environment-travel literature existing at the end of 2009 in order to draw generalizable conclusions for practice. We aimed to quantify effect sizes, update earlier work, include additional outcome measures, and address the methodological issue of self-selection.Methods: We computed elasticities for individual studies and pooled them to produce weighted averages.Results and conclusions: Travel variables are generally inelastic with respect to change in measures of the built environment. Of the environmental variables considered here, none has a weighted average travel elasticity of absolute magnitude greater than 0.39, and most are much less. Still, the combined effect of several such variables on travel could be quite large. Consistent with prior work, we find that vehicle miles traveled (VMT) is most strongly related to measures of accessibility to destinations and secondarily to street network design variables. Walking is most strongly related to measures of land use diversity, intersection density, and the number of destinations within walking distance. Bus and train use are equally related to proximity to transit and street network design variables, with land use diversity a secondary factor. Surprisingly, we find population and job densities to be only weakly associated with travel behavior once these other variables are controlled.Takeaway for practice: The elasticities we derived in this meta-analysis may be used to adjust outputs of travel or activity models that are otherwise insensitive to variation in the built environment, or be used in sketch planning applications ranging from climate action plans to health impact assessments. However, because sample sizes are small, and very few studies control for residential preferences and attitudes, we cannot say that planners should generalize broadly from our results. While these elasticities are as accurate as currently possible, they should be understood to contain unknown error and have unknown confidence intervals. They provide a base, and as more built-environment/travel studies appear in the planning literature, these elasticities should be updated and refined.Research support: U.S. Environmental Protection Agency.