One of the elements that makes prediction difficult is uncertainty. In one of the chapters of Donald Shoup’s High Cost of Free Parking (adapted for Access here), Professor Shoup poses the question:

HOW FAR IS IT from San Diego to San Francisco? An estimate of 632.125 miles is precise—but not accurate. An estimate of somewhere between 400 and 500 miles is less precise but more accurate because the correct answer is 460 miles. Nevertheless, if you had no idea how far it is from San Diego to San Francisco, whom would you believe: someone who confidently says 632.125 miles, or someone who tentatively says somewhere between 400 and 500 miles? Probably the first, because precision implies certainty.

Shoup uses this example to illustrate the illusion of certainty present in the parking and trip generation estimates from the Institute of Transportation Engineers. Many of the rates are based on small samples of potentially unrepresentative cases – often with a very wide range of observed parking/trip generation. Shoup’s concluding paragraph states:

Placing unwarranted trust in the accuracy of these precise but uncertain data leads to bad policy choices. Being roughly right is better than being precisely wrong. We need less precision—and more truth—in transportation planning

Part of the challenge is not just knowing the limitations of the data, but also understanding the ultimate goals for policy. David Levinson notes that most municipalities simply adopt these rates as requirements for off-street parking. This translation of parking estimates to hard-and-fast regulation is “odd” in and of itself. What is the purpose of a parking requirement? To meet the demand generated by new development?

Parking demand for a given building will be a range throughout the course of a day and a year, and demand for any given building category will itself fall within a large range. That range is reality, but that unfortunately doesn’t translate into simply codified regulations.

In the previous post, I discussed the challenges of accurate prediction and specifically referenced Nate Silver’s work on documenting the many failures and few successes in accurate forecasting. One area where forecasting improved tremendously is in meteorology – weather forecasts have been steadily improving – and a large part of that is disclosing the uncertainty involved in the forecasts. One example is in hurricane forecasts, where instead of publicizing just the predicted hurricane track, they also show the ‘cone of uncertainty‘ where the hurricane might end up:

Example of a hurricane forecast with the cone of uncertainty – image from NOAA.

So, why not apply these methods to city planning? A few ideas: as hypothesized before, the primary goal for parking regulations isn’t to develop the most accurate forecasts. The incentives for weather forecasting are different. The shifts to embrace uncertainty stems from a desire finding the most effective way to communicate the forecast to the population. There are a whole host of forecast models that can predict a hurricane track, but their individual results can be a bit messy – producing a ‘spaghetti plot,’ often with divergent results. The cone of uncertainty both embraces the lack of precision in the forecast, but also simplifies communication.

For zoning, a hard and fast requirement doesn’t lend itself to any cone of uncertainty. Expressing demand in terms of a plausible range means that the actual requirement would need to be set at the low end of that range – and in urban examples, the low end of potential parking demand for any given project could be zero. Of course, unlike weather forecasts, these regulations and policies are political creations, not scientific predictions.

Meteorologists also have the benefit of immediate feedback. We will know how well hurricane forecasters did within a matter of days, and even then we will have the benefit of several days of iterations to better hone that forecast. Comparatively, many cities added on-site parking requirements to their zoning codes in the 1960s; regulations that often persist today. Donald Shoup didn’t publish his parking opus until 2005.

There’s also the matter of influencing one’s environment. Another key difference between a hurricane forecast and zoning codes is that the weather forecasters are looking to predict natural phenomena; ITE is trying to predict human behavior – and the very requirements cities impose based on those predictions will themselves influence human behavior. Build unnecessary parking spaces, and eventually those spaces will find a use – inducing the very demand they were built to satisfy. There, the impacts of ignoring uncertainty can be long-lasting.

Here’s to embracing the cone of uncertainty!