Freeways: The ultimate in pedestrian-friendly design

Published August 1, 2001

If we’ve heard it once, we’ve heard it a hundred times: There is no point in building new road capacity because added capacity simply leads people to drive more. We can find one test of the truth of this myth in the data gathered by the Texas Transportation Institute for its annual mobility report.

The institute’s raw data cover 68 urban areas and include the following information for each year from 1982 through 1999:

  • number of lane miles of freeway;
  • number of lane miles of other arterials;
  • total number of miles of roads;
  • number of miles driven on freeways;
  • number of miles driven on other arterials;
  • total number of miles driven on all roads;
  • population; and
  • density.

Comparing data

If building more roads leads people to drive more, then per-capita driving will increase faster in urban areas that rapidly expand their road systems than in urban areas that build few new roads. This can be tested with a simple statistical calculation known as the correlation coefficient, or r-squared.

The r-squared of two sets of data is a number between zero and one. If it is one, then the two data sets perfectly correlate with one another. For example, if one data set were 1, 2, 3, 4, and another were 2, 4, 6, 8, then they would perfectly correlate and the r-squared would be 1.0. 1, 2, 3, 4 is also perfectly correlated with 8, 6, 4, 2. But if the second data set were 4, 8, 6, 2, they would not be well correlated. and the r-squared would be close to zero.

Using my spreadsheet’s random number function for two sets of 68 numbers, I get r-squareds from less than 0.001 to 0.066. So any r-squareds in the Texas data sets that are less than 0.066 are no better than random.

Correlation, of course, does not imply causation. If data set A correlates with data set B, it could mean that A causes B, B causes A, or both A and B are influenced by some other factor C. The way to ferret out causation is to compare lots of data sets measuring many different factors, as well as applying a little common sense.

The raw data used by the Texas Transportation Institute for major urban areas are supplied by individual state transportation offices to the Federal Highway Administration. For smaller urban areas, the institute went directly to the states. Only some states cooperated, which is why most of the institute’s smaller urban areas are in Texas, Oregon, and a few other states.

How reliable are these data? The states know to the hundredth of a mile how many roads they have, so road miles and lane miles should be pretty reliable. Miles of driving (vehicle miles traveled, or VMTs) is not quite so reliable, but the states monitor traffic at scores of locations in every urban area so they should have a good idea about trends. Population data are estimated each year by the Census Bureau, and while the estimates aren’t perfect they are at least as good as miles traveled.

Perhaps the most questionable data provided by the states is the land area of each urban area. Some states update this every year; Atlanta’s land area increases by about 20 square miles each year. Other states aren’t as meticulous, so that the land areas of some regions remain the same for several years then suddenly grow a huge amount, then stay constant for a few more years. It is likely that these problems average out over the 17 years from 1982 to 1999.

Population, density, and driving

Table 1
Correlations Between Driving and Demographics
(r-squareds of changes from 1982 to 1999)
Population vs. total VMTs 0.57
Density vs. per-capita VMTs 0.0006

If the data are reliable, what do they tell us about the rate of growth in driving? First, Table 1 shows there is a strong correlation between changes in population and changes in driving (VMTs).

Per-capita driving increased in all urban areas except Colorado Springs and Oklahoma City. Per-capita driving in those two regions declined by 4 percent. However, both of these urban areas registered large increases in per-capita driving on freeways and arterials.

Smart-growth advocates argue that sprawl leads people to drive more. If true, then there should be a strong correlation between changes in population density and per-capita driving. Yet the data show absolutely no correlation. Thus, driving is independent of density or sprawl.

New highways and highway driving

Table 2
Correlations Between Freeway/Arterial Driving and Miles
(r-squareds of changes from 1982 to 1999)
Freeway lane miles vs. total freeway VMTs 0.60
Freeway lane miles vs. per-capita freeway VMTs 0.55
Arterial lane miles vs. total arterial VMTs 0.70
Arterial lane miles vs. per-capita arterial VMTs 0.62

Table 2 shows there are also strong correlations between the growth in lane miles of freeways and arterials and the miles driven on those freeways and arterials. The correlations are a little higher when comparing lane miles with total VMTs than with per-capita VMTs. This suggests that some of the correlation between lane miles and total VMTs is due to more lane miles being built in fast-growing regions.

In short, Table 2 suggests that smart-growth advocates are right: Building freeways and arterials does lead to more driving on those highways.

New highways and total driving

Table 3
Correlations Between Total Driving and Road Miles
(r-squareds of changes from 1982 to 1999)
Total road miles vs. per-capita VMTs 0.01
Freeway lane miles vs. per-capita VMTs 0.04
Arterial lane miles vs. per-capita VMTs 0.09

Table 3, however, provides a new insight on this claim. The correlation between per-capita driving on all roads and total road miles turns out to be no better than random. Moreover, the correlation between per-capita driving and freeway lane miles is also no better than random, while the correlation between per-capita driving and arterial lane miles is only slightly better than random.

This means that building more freeways leads to more freeway driving but not to more total driving. In other words, new freeways and arterials lead people to change routes from ordinary streets to the freeways and arterials. The result is less traffic on the collectors and local streets.

Accident data show that freeways are the safest highways in urban areas, followed by arterials, then collectors, then local streets. Freeways see few pedestrian accidents because pedestrians are generally excluded. But freeways also make cities safer for pedestrians because they take cars off of collectors and local streets, where most pedestrian accidents occur.

Freeways are thus the ultimate pedestrian-friendly design: If you want to make your city safer for pedestrians, then build more freeways.

Congestion and driving

Table 4
Correlations Between Congestion and Driving
(r-squareds of changes from 1982 to 1999))
Travel time index vs. VMTs 0.02
Travel time index vs. per-capita VMTs 0.0001

Table 4 refutes another smart-growth assumption: that increased congestion discourages driving and leads people to seek alternatives such as public transit. If true, then the growth in driving or per-capita driving would correlate with the growth in congestion.

In fact, the relationships are no better than random. The travel time index is the Texas Transportation Institute’s estimate of the ratio of the duration of trips taken at rush hour vs. other times of the day. Thus, if a trip takes 10 minutes in uncongested traffic, a travel time index of 1.5 suggests it will take 15 minutes at rush hour. Table 4 says there is no indication that increasing congestion has any influence on driving.

Thus, when urban planners say “We are going to increase livability by building rail transit instead of highways,” what they really mean is, “We are going to increase congestion by building rail transit instead of highways, and everyone who drives is going to suffer.”

Randal O’Toole ([email protected]) is senior economist with the Thoreau Institute ( and author of the recent book, The Vanishing Automobile and Other Urban Myths.