Skip to contentUnited States Department of Transportation - Federal Highway Administration FHWA HomeFeedback
Office of Transportation Policy Studies

FHWA > Policy > Office of Transportation Policy Studies > Transportation Futures


< Previous | Table of Contents | Next >

Executive Summary

A. Overview

Today’s teens are members of the first generation to have never known a world without instantaneous and nearly ubiquitous mobile phone access. They also must surmount greater hurdles to drivers licensing than any previous generation faced. And they are struggling to transition into the most unwelcoming job market since the Great Depression. These tectonic happenings surely augur equally dramatic changes in the travel choices and patterns of young adults in the years ahead. Or will they? This report examines this question.

While scholars have studied the travel choices and patterns of adults extensively over the years, our knowledge of youth travel behavior is surprisingly limited and uneven. There is a growing body of research on how children travel to school and a second body of research on youth and travel safety, in particular, the high rates of crashes and driving fatalities among teenagers. Beyond these two rather focused lines of inquiry, however, studies of travel by children, teens, and young adults are rare.

Researchers have posited several factors to explain differences in the travel behavior of youth and adults, and to support the argument that such differences may persist as today’s youth move into adulthood. First, the rapid profusion and adoption of new communication technologies influences how people use their time and may affect how much they travel (Kwan, 2002), and young people tend to be early and frequent adopters of these technologies (Mans et al., forthcoming; Lenhart et al., 2005; Pew Research Center, 2010b). Second, all 50 states have now adopted graduated driver’s licensing programs, making teen licensing more difficult and restrictive (with respect to time, trip purpose, and passengers) than in previous eras (Insurance Institute for Highway Safety, 2012). Third, unemployment rates during the current recession are highest for youth, thereby reducing journey-to-work and work-related travel and limiting the resources teens and young adults have to pay for non-work activities (and associated travel) of all types. This prolonged economic downturn may also influence youth travel patterns indirectly; fragmentary evidence suggests that young adults struggling to find work increasingly boomerang back home to live with parents (Kaplan, 2009; Pew Research Center, 2010b; Wiemers, 2011), drawn by a free or steeply discounted bedroom, groceries, and, perhaps, access to parents’ cars.

B. What We Did

To explore the influence of these and other factors on the travel behavior of youth, we analyze data from the Nationwide Personal Transportation Survey in 1990 and the National Household Travel Surveys in 2001 and 2009. We use the data to examine how the travel behavior of youth (teens and young adults ages 15–26) compares to that of middle-aged adults (ages 27–61), whether the basic determinants of youth travel behavior are changing, and whether we see evidence that today’s youth are likely to travel differently than adults. To do this we focus on four fundamental outcome measures of travel: personal miles traveled (PMT); activity participation (number of daily trips); journey-to-work (or commute) mode choice; and travel mode used for social trips. In analyzing each of these outcome measures, we employ a set of statistical models. These models allow us to assess the influence of life-cycle effects (changes that typically occur of the course one’s life), period effects (associated with particular events like an economic downturn), and cohort effects (where the patterns of one generation differ from others) on the travel behavior of youth relative to middle-aged adults.

C. What We Find in a Nutshell

We find that economic factors—employment status, household income, and the like—strongly influence the travel behavior of both adults and youth, the latter of which has been harder hit by our current, prolonged economic downturn. These economic effects help to explain the growth in mobility, trip-making, and driving among both youth and adults during the 1990s, and the subsequent contraction of mobility, trip-making, and driving during the 2000s. When it comes to changes in youth (and adult) travel behavior in recent years, the adage “It’s the economy, stupid” appears to hold.

With regard to the effects of young adults “boomeranging” to live at home with parents, the explosion of information and communications technologies, and stricter driver’s licensing requirements for teens, the effects are far milder, and mixed. While more young adults appear to be living at home than in years past, the effects on travel behavior are ambiguous at best. Likewise, despite the staggering increase in mobile phone and web access and use, the effects we were able to measure were both mild and tended to be associated with increases in travel. That is, when information and communications technologies affect travel, it appears to be as a complement to travel and not as a substitute for it. Finally, while teen licensing requirements have grown considerably stricter over the past two decades and more teens are obtaining their licenses in their late teens and early twenties, the effects of licensing on overall teen mobility are surprisingly muted. Sixteen- and 17-year-olds are driving less, but they appear to be (eventually) getting driver’s licenses and moving about as much as earlier generations of adults.

Our quasi-cohort models suggest moderate generational effects on travel behavior. Despite (or perhaps because of) what appears to be youth’s increasing reliance on the single-occupant vehicle for the journey to work and social trips, the youngest cohorts in our datasets are making somewhat fewer trips (-4%) and traveling considerably fewer miles (-18%) than was the case for previous generations at the same stage in their lives, all else equal.

Major Findings

  • Economic factors predominate—(a) employment status, household income, and other measures of economic status strongly influence all forms of youth and adult travel behavior across all three study years, (b) these factors generally have an even greater influence on the travel of youth than adults.
  • The effects of other factors are mixed— the effects of (a) young adults living with their parents, (b) the explosion of information and communications technologies use, and (c) stricter teen driver’s licensing requirements are far milder and more mixed compared to the consistently strong travel behavior effects of economic factors.
    • Information and communications technology use—is measured as daily web use and, when significant, tends to be associated with more travel, and not less.
    • Graduated driver’s license regulations—(a) more teens are licensing later, but most do eventually license and drive, (b) the regulations are associated lower teen person-miles of travel over the short-term, but not much change in trips, and (c) transit commuting is higher in states with stricter licensing regulations, but for adults as well as teens – as such, this probably says more about the states that adopt tough licensing laws than the effect of the laws on transit use.
  • Demographic travel distinctions are fading—Travel behavior has long been observed to vary by demographic factors, such as race/ethnicity; while we continue to observe racial/ethnic travel patterns among adults, such distinctions are more muted for youth and appear to be lessening over time.
  • Evidence of generational shifts in travel behavior— Our quasi-cohort models suggest moderate generational effects on travel behavior: all things equal, younger generations appear to (a) travel fewer miles and (b) make fewer trips than was the case for previous generations at the same stage in their lives; however, it also appears that younger commuters appear to drive alone to work more frequently than similarly aged workers from earlier generations.
  • Many findings are suggestive, but not definitive—While many of our findings are consistent and appear robust, others are merely suggestive due to (a) small sample sizes for some population groups (e.g. 1990 sample, recent birth cohorts, bike travelers, etc.), (b) construct validity questions related to our variables of interest (e.g. reported daily web use as a measure of information and communications technology use), and (c) a lack of true cohort data to allow us to follow the same individuals over time.

Perhaps the most significant overall finding from this analysis is how little youth travel behavior is deviating from that of adults, despite the enormous economic, social, technological, and policy changes over the past two decades. Specifically, we see little evidence in these data that living circumstances, technological innovations, or driving regulations are dramatically altering travel behavior. We do find that economic factors—specifically employment status, educational attainment, and household income—strongly affect youth travel behavior, but these factors strongly affect the travel of older adults as well.

D. What We Find in Each of Our Four Analyses

We summarize the principal findings of each of our four specific analyses—personal miles traveled, trip-making, commute mode choice, and social and recreational trip mode choice—in turn below.

1. What Explains PMT among Teens, Young Adults, and Adults?

The descriptive statistics and multivariate models of the factors affecting personal miles traveled (presented in Chapter V of the report) paint an interesting and nuanced picture of teen and young adult travel, both in comparison with adults and over time. Personal travel, measured here as personal miles traveled (or PMT), generally increases as one moves from teenage, to young adulthood, and into middle age. But personal travel is highly correlated with economic factors, such as employment and income. Indeed, we observe a substantial drop in metropolitan person travel nationwide between 2001 and the recession of 2009 across all age groups examined, though the largest drop (23%) was among teens.

The effect of economic factors on the travel of teens and young adults (measured here in terms of employment), and for that matter middle-age adults, is substantial and unambiguous—but the effect of employment on PMT is stronger for young adults than for teens, and stronger still for middle-aged adults than for young adults or teens. But while being employed is highly correlated with PMT, the effect of “boomeranging” young adults living at home is ambiguous at best; we observe no effect of living with parents on the PMT of young adults in our primary “Driver’s Status” model. And our models suggest that, far from acting as a substitute for travel, daily web use either has no effect on travel or, in the most recent 2009 survey, is associated with increased PMT across all age categories; this finding is likely because web use, auto access, and PMT are all positively associated with education and income. Finally, although licensing requirements have increased dramatically for teens in states across the country, we find no consistent relationship between license regulations and PMT. We summarize these findings in Table 1 below.

Table 1. Summary Table: PMT and Variables of Interest (1990, 2001, and 2009)
  Teen (15–18)
1990
Teen (15–18)
2001
Teen (15–18)
2009
Young Adult (19–26)
1990
Young Adult (19–26)
2001
Young Adult (19–26)
2009
Adult (27–61)
1990
Adult (27–61)
2001
Adult (27–61)
2009
Worker Status +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship
Young Adult Living at Home (Not included) 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship (Not included)
Technology (Web Use) n/a 0blue (0) indicates no statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship n/a 0blue (0) indicates no statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship n/a 0blue (0) indicates no statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship
License Stringency 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship 0blue (0) indicates no statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship 0blue (0) indicates no statistically significant relationship
Yellow (+) indicates a positive and statistically-significant relationship; red (-) indicates a negative and statistically-significant relationship; and blue (0) indicates no statistically-significant relationship.

One of the most significant findings of this analysis is how consistent the observed effects on travel are across the three age categories: teens, young adults, and adults. Education (or parents’ education for teens), employment, auto access, and being a driver are all positively associated with PMT across almost all age categories and survey years. Likewise, population density is negatively associated with PMT across nearly all age categories and years. There are a few exceptions to this pattern, but not many. Among adults (but not teens or young adults) income (apart from employment) and the presence of children in the household are also associated with greater personal travel. In the most recent survey year (2009), female teens travel more than male teens, a finding that constitutes a break from the past and may portend future changes in the gender division of travel.

One other possible break with the past may be revealed by our quasi-cohort model. This model suggests that those individuals born in more recent decades (the 1980s and 1990s) travel fewer miles per day than did previous cohorts at the same stage in their lives. Such a finding suggests that we may well be observing a gradual shift away from generational increases in PMT long associated with rising wealth and auto ownership. However, because we only have data on those born in the 1990s for one of the three data years, these results are suggestive rather than conclusive.

2. What Explains Patterns of Activities and Trips among Youth and Adults?

People travel in order to access opportunities to do, acquire, or sell. Thus trips, as opposed to traveling, act as a proxy for activity participation; the more trips one completes, the more activities in which one participates. Up to a point, therefore, more trips mean more activities and access to a higher quality of life. Likewise, more time and/or money spent traveling to a fewer number of destinations means fewer trips and lower levels of personal access and activities, in spite of high levels of mobility.

We find in our analysis presented in Chapter VI of this report that adults make more trips than youth, and that higher incomes and greater private vehicle access are associated with higher levels of trip-making and, hence, activity participation. In general, trip-making increases year to year as people age from their early teens through late middle age, and then it declines gradually thereafter. Trip-making is highly correlated with economic activity, as we observe that trips per person increased between 1990 and 2001, but declined between 2001 and the recession of 2009. Unemployment in 2009 was substantially higher among youth than adults, and the drop in trip-making between 2001 and 2009 was greater for youth than for adults. At a more micro level, our analysis shows that both income and employment are associated with greater levels of trip-making.

We constructed a set of structural equation models (SEMs) and find that income is a powerful predictor of trip-making among adults, and its importance in predicting youth trips increased substantially between 1990 and 2009 and is now on par with that of adults. The most important effect of income is in increasing automobile access, which in turn encourages more trips. While income and working are strongly associated with increased trip-making among both adults and, increasingly, youth, time spent commuting to and from work—reflects the opportunity cost of time and tends to depress trip-making.

Table 2. Summary Table: Number of Trips and Variables of Interest (1990, 2001, and 2009)
  Youth (15–26)
1990
Youth (15–26)
2001
Youth (15–26)
2009
Adult (27–61)
1990
Adult (27–61)
2001
Adult (27–61)
2009
Worker Status +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship
Young Adult Living at Home (Not included in the model)
Technology (Web Use) n/a 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship n/a +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship
License Stringency -red (-) indicates a negative and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship -red (-) indicates a negative and statistically significant relationship -red (-) indicates a negative and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship -red (-) indicates a negative and statistically significant relationship
Yellow (+) indicates a positive and statistically-significant relationship; red (-) indicates a negative and statistically-significant relationship; and blue (0) indicates no statistically-significant relationship.

We summarize our findings with respect to the variables of interest in this study in Table 2. While the effects of the economy, income, auto access, and working are all positively associated with trip-making among both adults and youth, internet access appears to have no effect on youth trip-making, and may actually be associated with a slight increase in trip-making among adults in 2009. Likewise, and remarkably, increasingly strict licensing regimes for teens appear to have decidedly mixed effects on youth trip-making. Finally, we note in these models that the independent effects of race/ethnicity on trip-making appear to be waning over time, especially among youth.

Finally, our quasi-cohort model suggests that, despite having high levels of auto access and higher incomes than previous generations did at the same stage in their lives, the most recent cohort (those born in the 1990s) appear to be making fewer trips (roughly 4% fewer) than did previous cohorts, all else equal.

3. How Do Commute Mode Choice Patterns Compare between Youth and Adults?

While the previous two analyses found that working has a substantial influence on both personal miles traveled and the number of daily trips, this analysis focused specifically on the mode choices of workers. As with the previous analyses, the effects of the recession on travel behavior, particularly on youth, are substantial. The proportion of working adults declined from 85 percent to 80 percent of the eligible population between 2001 and 2009; in contrast, the number of working youth dropped precipitously, from 70 percent in 2001 to just 56 percent in 2009.

In Chapter VIII of the report we focus on how those who do work get there. We find, not surprisingly, that household income is a consistent predictor of commute mode choice for both youth and adults. In general, income is strongly and positively associated with driving alone to work; put another way, as incomes go up, the probability of commuting via carpool, public transit, bicycle, or foot all go down—for both youth and adults, and across all three survey years.

With respect to the effect of the bad economy causing a growing number of youth to “boomerang” home to live with parents, we find that young working adults living at home are less likely to carpool and more likely to use public transit than other workers. Likewise, those who use the web daily were less likely to carpool (2001 and 2009) and more likely to commute via public transit (2001 only). Last, in terms of our variables of interest, youth in places with stricter teen licensing regimes were more likely to commute to work via public transit; however, we find that the strictness of licensing regimes has no significant effect on an individual’s choice to commute via carpool.

The analysis also revealed important differences between youth and adult workers. Previous studies have consistently shown that being female, Hispanic, or foreign born increases the likelihood of commuting by carpool over driving alone. Our analysis shows that this trend remains strongly consistent over time for adults, but not for young workers. This finding suggests that the relationship between travel mode and broad social categories, like sex, race/ethnicity, and immigration status, may be weakening over time. The findings from these models are summarized in Table 3 below.

Finally, the quasi-cohort model suggests that those born in more recent decades (the 1970s, 1980s, and 1990s) are far more reliant on the single-occupancy vehicle for their journey to work than were previous generations, though this effect appears to be diminishing over time.

Table 3. Summary Table: Commute Mode and Variables of Interest (1990, 2001, and 2009)
  Youth (15–26)
1990
Youth (15–26)
2001
Youth (15–26)
2009
Adults (27–61)
1990
Adults (27–61)
2001
Adults (27–61)
2009
Carpool (Base: driving alone)
Employment Status (Not included in the model) (Not included in the model)
Young Adult Living at Home -red (-) indicates a negative and statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship n/a n/a n/a
Technology (Web Use) n/a -red (-) indicates a negative and statistically significant relationship -red (-) indicates a negative and statistically significant relationship n/a 0blue (0) indicates no statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship
License Stringency 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship

Transit (Base: driving alone)
Employment Status (Not included in the model) (Not included in the model)
Young Adult Living at Home 0blue (0) indicates no statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship n/a n/a n/a
Technology (Web Use) n/a +yellow (+) indicates a positive and statistically significant relationship 0blue (0) indicates no statistically significant relationship n/a +yellow (+) indicates a positive and statistically significant relationship 0blue (0) indicates no statistically significant relationship
License Stringency +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship -red (-) indicates a negative and statistically significant relationship

Bicycling (Base: driving alone)
Employment Status (Not included in the model) (Not included in the model)
Young Adult Living at Home +yellow (+) indicates a positive and statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship n/a n/a n/a
Technology (Web Use) n/a 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship n/a 0blue (0) indicates no statistically significant relationship -red (-) indicates a negative and statistically significant relationship
License Stringency -red (-) indicates a negative and statistically significant relationship 0blue (0) indicates no statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship 0blue (0) indicates no statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship

Walking (Base: driving alone)
Employment Status (Not included in the model) (Not included in the model)
Young Adult Living at Home 0blue (0) indicates no statistically significant relationship0blue (0) indicates no statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship n/a n/a n/a
Technology (Web Use) n/a 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship n/a +yellow (+) indicates a positive and statistically significant relationship 0blue (0) indicates no statistically significant relationship
License Stringency +yellow (+) indicates a positive and statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship
Yellow (+) indicates a positive and statistically-significant relationship; red (-) indicates a negative and statistically-significant relationship; and blue (0) indicates no statistically-significant relationship.

4. How Do Social/Recreational Mode Choice Patterns Compare Over Time?

Social travel has received relatively little attention from researchers compared with other types of travel—in particular, the commute to work. While the previous analyses show that the recession has had a large effect on the travel behavior of teens and young adults, our analysis presented in Chapter IX finds that the effects of the economic downturn on social travel are less clear. The total number of social trips declined slightly from 2001 to 2009, but the share of social trips relative to other types of trips actually increased slightly. This finding contradicts expectations that people are most likely to reduce discretionary travel in worsening economic circumstances.

While the recession may have had unexpected effects on the amount of social travel, it has more expected effects on mode choice. As discussed in the previous section, household income strongly affects commuter mode choice, increasing the likelihood of driving alone, and we observe similar results for social trip mode choice. For youth, income has a negative effect on carpooling, transit use, and walking; for adults, income has a negative effect on transit use and bicycling. However, the models suggest that higher-income adults are more likely to carpool than drive alone for social trips, an unexpected—but highly social—result.

On the other hand, trip characteristics frequently had statistically significant effects on mode choice. For example, trip distance, the number of social trips taken in the survey day, and weekend travel all had positive effects on carpooling for both youth and adults. Conversely, trip distance had a negative effect on bicycling and walking for both groups. Geographic characteristics had less of an effect overall, but population density and living in New York both had positive effects on transit use.

As for other variables of interest (see the summary of our results in Table 4 below), daily Internet use had almost no effect for youth and adults, and licensing had either no or inconsistent effects. Young adults living at home were more likely to drive alone in most of the models, including the carpooling models. This finding perhaps reflects their desire to maintain a measure of independence while living with their parents.

Our analysis further revealed some differences in social travel between youth and adults. As noted previously, studies have shown that being Hispanic or an immigrant increases the likelihood of carpooling or walking over driving alone. As with our commuting analysis, the social trips analysis shows that this trend remains consistent over time for adults, but not for youth. In other words, the relationship between socio-demographic categories and mode choice for recreational travel may be weakening as well.

Finally, the quasi-cohort model finds that being born in the 1970s and especially the 1980s is associated with more driving alone (perhaps to bowl alone) to social destinations, while being born in earlier decades or in the 1990s has no effect on driving alone on social trips. While the 1990s results compares only the social travel mode choice of 15 to 19 year olds with similar age cohorts born in the 1980s and 1970s, this finding does suggest that rates of driving alone, after controlling for a wide array of other factors, is down among the latest generation of teens.

Table 4. Social Mode and Variables of Interest (1990, 2001, and 2009)
  Youth (15–26)
1990
Youth (15–26)
2001
Youth (15–26)
2009
Adults (27–61)
1990
Adults (27–61)
2001
Adults (27–61)
2009
Carpool (Base: driving alone)
Employment Status (Not included in the model) (Not included in the model)
Young Adult Living at Home -red (-) indicates a negative and statistically significant relationship -red (-) indicates a negative and statistically significant relationship -red (-) indicates a negative and statistically significant relationship (Not included)
Technology (Web Use) n/a -red (-) indicates a negative and statistically significant relationship -red (-) indicates a negative and statistically significant relationship n/a 0blue (0) indicates no statistically significant relationship -red (-) indicates a negative and statistically significant relationship
License Stringency 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship

Transit (Base: driving alone)
Employment Status (Not included in the model) (Not included in the model)
Young Adult Living at Home -red (-) indicates a negative and statistically significant relationship -red (-) indicates a negative and statistically significant relationship 0blue (0) indicates no statistically significant relationship (Not included)
Technology (Web Use) n/a +yellow (+) indicates a positive and statistically significant relationship 0blue (0) indicates no statistically significant relationship n/a 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship
License Stringency -red (-) indicates a negative and statistically significant relationship ?yellow (+) indicates a positive and statistically significant relationship -red (-) indicates a negative and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship ? ?

Bicycling (Base: driving alone)
Employment Status (Not included in the model) (Not included in the model)
Young Adult Living at Home 0blue (0) indicates no statistically significant relationship -red (-) indicates a negative and statistically significant relationship 0blue (0) indicates no statistically significant relationship (Not included)
Technology (Web Use) n/a 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship n/a 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship
License Stringency 0blue (0) indicates no statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship

Walking (Base: driving alone)
Employment Status (Not included in the model) (Not included in the model)
Young Adult Living at Home -red (-) indicates a negative and statistically significant relationship 0blue (0) indicates no statistically significant relationship -red (-) indicates a negative and statistically significant relationship (Not included)
Technology (Web Use) n/a 0blue (0) indicates no statistically significant relationship 0blue (0) indicates no statistically significant relationship n/a +yellow (+) indicates a positive and statistically significant relationship 0blue (0) indicates no statistically significant relationship
License Stringency +yellow (+) indicates a positive and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship -red (-) indicates a negative and statistically significant relationship +yellow (+) indicates a positive and statistically significant relationship ? +yellow (+) indicates a positive and statistically significant relationship
Yellow (+) indicates positive and statistically-significant relationship; red (-) indicates negative and statistically-significant relationship; blue (0) indicates no statistically-significant relationship; and white (?) means no consistent effect.
< Previous | Table of Contents | Next >


FHWA Home | Feedback
FHWA