The Texas Transportation Institute has just released the latest version of its much-criticized Urban Mobility Report. Although the conclusions and recommendations made by the TTI tend to reflect its funding sources (APTA, American Road and Transportation Builders Association), the underlying data seems sound, and suggests conclusions orthogonal to those made by the report. In addition, looking at the correlations more closely suggests obvious hazards coming from any simplistic analysis of linear regression. It even showcases how we could use data dishonestly and lie with statistics. So let’s take the data that’s relevant right now and see what we can conclude ourselves.
First, the size of an urban area is a very strong correlate of its level of congestion. The linear correlation between size and per capita congestion cost is 0.71. The correlation increases to 0.8 if we take the log of population and the log of congestion, or if we consider congestion in the absence of public transportation; in both cases, it comes from the fact that New York is far below the population-congestion regression line.
Now, more freeways do not really lead to congestion reduction. There’s some correlation between freeway miles per capita and congestion per capita, going in the expected direction, but it’s weak, -0.2, and while it’s statistically significant, the p-value is an uninspiring one-tailed 0.025. Looking at a scattergram doesn’t make any nonlinear relationship obvious.
Moreover, size is a correlate of both congestion (0.71 as above) and freeways (-0.23). This is fully expected: literature on cities’ economies of scale (here is a story of one controversial example) suggests that congestion and the economic activity causing it grow faster than linearly in city size while the amount of required energy and infrastructure grows slower than linearly. I open the floor to anyone with more powerful tools than OpenOffice Calc to do multiple regression; again, the sanitized data is here.
Even without controlling for population, freeways are not a very strong correlate. The regression coefficient is -233: increasing the number of freeway miles per thousand people by 1 (the range is 0.13-1.4, with few large metros above 1 or below 0.35) reduces the congestion cost per capita by $233 per year, also uninspiring.
The regression number alone can be used as a dishonest trick when arguing on the Internet. If we overinterpret weak correlations, we can declare that the only way to decrease congestion is to build an unrealistic number of freeways, and thus declare the problem unsolvable. Of course, for most cities we can find other cities of comparable size with much less congestion and without enormous amounts of asphalt – this is why the correlation is so weak. But a good hack should not bother himself with such caveats to talking points.
So if making an urban area larger makes it more congested, independently of and much more strongly than all else, should we give up on cities? Well, no. Assuming no change in traffic policy, congestion results from more economic activity. It then becomes straightforward to institute congestion pricing. It’s no different from how big cities can use their resources to hire more cops to deal with the crime that could result from extra interactions between people. On top of this, in very large cities, mass transit becomes a serious option: this not only reduces the amount of congestion per capita, but also removes many people from the highways to the point that congestion becomes irrelevant to their daily lives, except perhaps through higher transportation prices, which they can fully afford given the extra wealth.
Another thing to consider is that most American cities have added more freeways than people since 1982, the first year for which TTI data is available, while also becoming much more congested. If a simple relationship between freeway miles per capita and congestion held, it would be robust to these changes over time. Of course, traffic has grown even faster, leading the main report to showcase on PDF-page 21 how congestion increased the fastest in regions where road demand outgrew supply the most. But this raises the question of whether the main issue is one of demand, rather than one of supply. This is not just an issue of size: the log-log regression coefficients with cost and time is 0.42, i.e. doubling an urban area’s population will raise its per-driver congestion cost and travel delay by a factor of 2^0.42; since 1982, the average urban area on the list has seen its population grow by a factor of 1.46 and its travel delay per driver grow by a factor of 2.85 = 1.46^2.77. Cost has grown even faster, because of higher value of time.
That said, quantity of freeways does not equal quality (from the drivers’ perspective, of course, rather than the city’s). On paper, Greater New York has added freeway lanes about 9% faster than people over the last thirty years. In practice, none has addressed the major chokepoints within and into the city itself, where traffic is worst. Of course, commutes involving Manhattan are overwhelmingly likely to be done on public transportation, but diagonal commutes within the city are more likely to be done by car than on transit.
On a parenthetical note, the units of comparison here are TTI-defined urban areas. TTI’s belief about urban area population growth trends is sometimes at odds with that of the Census Bureau, but the raw population numbers are close enough. More important is the question of what to do about urban areas that are really exurbs of larger areas, such as Poughkeepsie-Newburgh and the Inland Empire. My first instinct was to lump them in with their core metro areas, but their congestion level per capita is not high. Their commutes are long, but not very congested for their size. Finally, although most correlations here are with congestion cost, the correlation numbers with travel delay and excess fuel consumptions are very similar; the one exception I’ve checked, for which I have no explanation, is log-log congestion-fuel correlation (0.84, with regression coefficient 0.73).