My home town newspaper recently ran the standard repeat-what-the-engineer-says article on traffic projections. Essentially, the report indicated that we’re going to be inundated with traffic. As things continue to “full build out” (it was in quotes so I’m assuming it is an engineering term), traffic is going to increase by 75%, an astounding amount since most locals will attest we are already drowning in traffic (we’re not, but most would attest that we are). The recommendation for dealing with all this traffic seems sensible: make some prudent investments today to acquire more land for future road expansion and then, as they are built, oversize the roads to meet this future demand.

A lot of the rationale for these projections — as well as the public’s acceptance of them — comes from the fact that growth has been robust. In fact, if you go back decades and look at the projections that were made for the present time, they are laughable in how dramatically they underestimated the amount of traffic. We projected out based on what our experience had taught us to anticipate, but we were wrong, and it cost the city a lot of money to retrofit all of the places that were inundated with cars.

This reality fits a national trend. My experience is backed up by studies demonstrating that, the higher the functional classification and the larger the traffic volumes, the greater the degree of underestimate. This correlates with work by Patron Saint of Strong Towns Thinking, Nassim Taleb, who has made the same observations of economic systems, governments, etc… (For one example, go to the 5:10 mark of this recent video.)

Amazingly, the fact the we have been so consistently wrong doesn’t make us any less confident today, either in my hometown or nationwide. We’ve “enhanced” our models now and believe we have it figured out this time, revising the data upward to reflect what we have experienced in the “real” world. This is the essence of modeling, and what else could be more rational?

Or more foolish. In these models, we’ve taken something that is unpredictable — driver behavior — and treated it as if it were actuarial science, akin to estimating life expectancy or your odds of drawing a face card when the dealer is showing fifteen. The idea behind our hubris is that, while one driver may be unpredictable, the average driver will react in a predictable way and, thus, we can model based on a normal distribution. These models are failing to account for things like consumer preference, the ability to access financing, overall market growth, cost of construction materials, gas prices, government employment levels, and on and on and on…. We assume all drivers make predictible traffic decisions. They don’t.