Focusing on net trips (the green dashed line in Figure 10), about 15% of the sample made zero net trips, meaning they stayed in the same place all day (or, in very rare cases, made only a return-home and/or return-work trip without having gone to home and/or work within the same 24-hour reference period; this rare situation is just 0.3% of those with zero net trips and 0.05% of the overall sample). About 22% made one net trip, with a decaying incidence of people making successively higher numbers of trips.
In considering the form of the dependent variable, and what sort of process it
represents, there may be an important distinction between the phenomena of any trips (versus zero) and the phenomena of successively higher numbers of trips. This is for several reasons.
First, once out of the house, there may be increased propensity to make additional trips along the way. (That is, crossing the threshold from 0 to 1 might be more difficult, psychologically or logistically, than increasing from 1 to 2 or from 2 to 3, given 1.) Second, the sorts of activities that tend to draw someone out of the house at all, such as work, school, and household maintenance activities, are likely to generate even more activity. (So, those engaging in any travel at all are those most likely to make higher numbers of trips, again resulting in a perhaps discontinuous continuum from 0 to 1 trip, versus successively higher numbers of trips.) Finally, certain personal attributes and circumstances may differently influence the probability of making any trips versus the proclivity for successively higher numbers of trips. For instance, on average, women are less likely to get out of the house, but when they do, they are more likely to make more trips (that is, move between more addresses) than are men. This would be difficult to capture in a single model, since it essentially implies there is a different effect of being female (for instance) at different levels of the dependent variable.
For a heuristic, visual representation for a handful of attributes that may influence trip-making, Figure 11 shows the difference of the apparent effect of each attribute on the
dependent variable between 0 and 1, versus successively higher values. (Note that these only capture the bivariate relationship between a given variable and number of trips — not
controlling for other explanatory variables or capturing any interaction effects that might indeed matter.) The y-axis measures the percentage-point difference across groups (for instance, females versus males) for each level of trip-making (on the x-axis). If a factor were consistently associated with more trip-making, there would be a straight line with a positive slope (the percent of 0’s would less and the percent of 4’s and 5’s would be more).
There are relatively straight lines for some variables. For instance, having children is consistently associated with more travel: Among those with kids, the percent making 0 trips is about 8 percentage-points fewer; making 1 trip is 2 points fewer, making 4 trips is 2 points more, and making 5+ trips is 6 points more. Similarly, lower incomes are associated with less travel: Among those with below-average income (those whose household income per adult household member is less than the sample average), the percent making 0 trips is about 10 points more, making just 1 trip is 3 points more, making 4 trips is 3 points less, and making 5+
trips is 6 points less.
By contrast, for other variables, there are unusal elbows at 1, in some cases reversing the slope of the curve altogether, such as for females (versus males), recent immigrants (who arrived with the last 5 years versus longer ago or native-born), and employment (versus not working). Females are more likely to make 0 trips than are men, but then less likely to make 1 trip, and more likely to make successively higher numbers of trips above 1. Recent immigrants are much less likely to make 0 trips, but then less likely to make successively higher numbers of trips above 1. Employed people are much less likely to make 0 trips, but then not successively
higher numbers of trips, perhaps less likely to do so. By contrast, those employed with multiple jobs are both less likely to make 0 trips and to make successively higher numbers of trips.
Figure 11. Difference in trip-making by select attributes across trip-count levels
Another consideration is whether there are any differences in the distributional form across vehicle-access groups. In general, trip volumes are lower among those with lower access, with more people making zero trips and the average volume of trips above zero also being less (see Table 27). Figure 12 and Figure 13 show how the distribution of trips differs most (across groups) at 0, with the decay of trip counts 1 and above more similar across each of the access groups.
-25%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
0 1 2 3 4 5+
Percentage-point difference across groups
Number of trips
Female (vs male)
Kids (vs no kids)
Age 80+ (vs younger)
Recent immigrant (vs not)
Has car (vs none)
Weekend (vs weekday)
Below average income (vs above average)
Employed (vs. not working), among age <65
Employed multiple jobs (vs.
other status), among age <65
Table 27. Distribution of net daily trips, by vehicle-access group
Source: 2009 NHTS version 2.1. Among respondents age 18 and over,
unweighted, with one person randomly selected from each household, excluding respondents who were out of town on the survey day.
Figure 12. Distribution of net trips, by vehicle-access level
Figure 13. Distribution of net trips >0, by vehicle-access level 0%
Given the nature of trip-making counts, I explore several different modeling frameworks to capture these patterns most meaningfully.