Chapter X. Conclusion

To explore potential influences 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 five fundamental measures of travel: personal miles traveled (PMT); activity participation (number of daily trips); trip purpose; journey-to-work (or commute) mode choice; and travel mode used for social trips. With the exception of our descriptive analysis of trends in trip purpose, we employ a set of statistical models to analyze each of the four other measures of travel. 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.

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 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 teen, 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 mixed 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 is as a complement to travel and not 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 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 appear to be 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.

Perhaps the most significant overall finding from this analysis is how little teen and youth travel behavior is deviating from that of adults, given 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 behaviors. We do find that economic factors—specifically employment status, educational attainment, and household income—strongly affect the travel behaviors of teens and young adults, but these factors strongly affect the travel of older adults as well.

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

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

The descriptive statistics and multivariate models of the factors affecting person-miles of travel 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 person miles of travel (or PMT), generally increases as one moves from teenage, to young adulthood, and into middle age. But person 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 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 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 far from acting as a substitute for travel, our models suggest that of daily web use either has no effect on travel or, in the most recent 2009 survey, is actually associated with increased PMT across all age categories; this 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 county, we find no consistent relationship between license regulations and PMT. These findings are summarized in Table 44 below.

Table 44. 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	+	+	+	+	+	+	+	+	+
Young Adult Living at Home	(Not included)			0	0	0	(Not included)
Technology (Web Use)	n/a	0	+	n/a	0	+	n/a	0	+
License Stringency	0	0	0	0	0	+	0	+	0

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), 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. And in the most recent survey year (2009), female teens are traveling more than male teens, which constitutes a break from the past and portend future changes in the gender division of travel.

Finally, the quasi-cohort model suggests that those born in more recent decades (the 1980s and 1990s) are traveling 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 should be viewed as suggestive rather than conclusive.

B. 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 s/he participates. Up to a point, therefore, more trips mean more activities and access to a higher quality of life. Likewise, more time and money spent traveling to a fewer number of destinations means fewer trips and lower levels of personal access, in spite of high levels of mobility.

We find 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 the 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. And at a more micro level, our analysis shows that both income and employment are associated with greater 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 45. 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	+	+	+	+	+	+
Young Adult Living at Home	(Not included in the model)
Technology (Web Use)	n/a	0	0	n/a	+	+
License Stringency	-	+	-	-	+	-

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 in adults in 2009. Likewise, and remarkably, increasingly strict licensing regimes for teens appear to have little, in any, effect 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.

C. 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 person miles of travel and the number of daily trips, this analysis has focused specifically on the mode choices of workers. As with the previous analyses, the effects of the recession on travel behavior, particularly by youth, are substantial. While the proportion of working adults declined from 85 to 80 percent between 2001 and 2009, the number of working youth dropped precipitously, from 70 percent in 2001 to just 56 percent in 2009.

While the proportion of adult and, especially, young workers was much lower in 2009 than in 2001, the commute mode analysis focused 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 a female, Hispanic, or foreign-born increases the likelihood of commuting by carpool over driving alone. Our analysis shows that this trend remains consistently strong for adults, but not for young workers. This suggests that the relationship between travel mode broad social categories, like gender, race/ethnicity, and immigration status, may be weakening over time.

Finally, our quasi-cohort model suggests that particularly 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 46. 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	-	0	0	n/a	n/a	n/a
Technology (Web Use)	n/a	-	-	n/a	0	+
License Stringency	0	0	0	0	0	0
Transit (Base: driving alone)
Employment Status	(Not included in the model)			(Not included in the model)
Young Adult Living at Home	0	+	+	n/a	n/a	n/a
Technology (Web Use)	n/a	+	0	n/a	+	0
License Stringency	+	+	+	+	+	-
Bicycling (Base: driving alone)
Employment Status	(Not included in the model)			(Not included in the model)
Young Adult Living at Home	+	0	0	n/a	n/a	n/a
Technology (Web Use)	n/a	0	0	n/a	0	-
License Stringency	-	0	+	0	+	+
Walking (Base: driving alone)
Employment Status	(Not included in the model)			(Not included in the model)
Young Adult Living at Home	0	0	+	n/a	n/a	n/a
Technology (Web Use)	n/a	0	0	n/a	+	0
License Stringency	+	0	0	+	0	0

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

While it appears in our previous analyses that the recession has had a large effect on the travel behavior of teens and young adults, its effects on social travel are less clear. The total number of social trips did decline slightly from 2001 to 2009, but the share of social trips relative to other types of trips actually increased slightly. This contradicts expectations that people would primarily 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 seen in the commute mode analysis, 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, young adults living at home were more likely to drive alone in most of the models, including the carpooling models and the quasi-cohort model. This finding perhaps reflects their desire to maintain a measure of independence while living with their parents and the fact that they are less likely to live with the peers with whom they socialize. Daily web use had mixed and generally modest effects on both youth and adult social travel mode choice, except for reducing the likelihood of carpooling for adults in the cross-sectional model and increasing the likelihood of driving alone in the cohort model. As in earlier analyses, web use has either a neutral or positive influence on travel, and in this case, driving. Finally, licensing had either no effect or mixed effects in most of the models—again, suggesting that the licensing variable captured other factors.

Our analysis did reveal 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 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 15–19 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 47. Summary Table: 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	-	-	-	(Not included)
Technology (Web Use)	n/a	-	-	n/a	0	-
License Stringency	0	0	0	0	0	0
Transit (Base: driving alone)
Employment Status	(Not included in the model)			(Not included in the model)
Young Adult Living at Home	-	-	0	(Not included)
Technology (Web Use)	n/a	+	0	n/a	0	0
License Stringency	-	?	-	+	?	?
Bicycling (Base: driving alone)
Employment Status	(Not included in the model)			(Not included in the model)
Young Adult Living at Home	0	-	0	(Not included)
Technology (Web Use)	n/a	0	0	n/a	0	0
License Stringency	0	+	0	0	+	+
Walking (Base: driving alone)
Employment Status	(Not included in the model)			(Not included in the model)
Young Adult Living at Home	-	0	-	(Not included)
Technology (Web Use)	n/a	0	0	n/a	+	0
License Stringency	+	+	-	+	?	+