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!
Well, you were doing well on this series until you brought up the great fraud Shoup.
And that goes to something else you didn’t mention — which is the tendency to use a set of numbers for a purpose which they were not intended.
Shoup loves this. He takes one set of numbers (high percentage of cars on street are driving around looking for parking) and then another (maximum efficiency of street parking is at 85% usage based on cars from the 1970s) and comes up with the magic of performance parking.
Sometimes this works. For the past 20-30 years the price of copper has been a good barometer for economic activity. Maybe not anymore.
But mostly it is just fraud. If I tried to convince you the price of pork bellies was tied into the value of the IBB you’d be quick to see that. There are connection to everything, of course, but very few ones that are truly tied together.
I might suggest that improvements in metrology are more based on satellite imagery than the cone of uncertainty.a
I definitely agree that these predictions need to be more aware of their uncertainty! But it’s not totally obvious how to put that uncertainty into regulatory practice. You seem to suggest being as liberal as possible compatible with any forecast in the zone of uncertainty:
“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.”
While this might be right for some land use regulations, it’s not obvious that it’s right for all of them. Returning to the hurricane example for the moment: In deciding what level of warning to issue, officials need to look at the cone of uncertainty. But no one thinks that the warning should be the minimum warning compatible with any forecast in the cone – if the cone is wide, that means that no one would issue a warning at all, because each recognizes that it’s possible that the hurricane will go the other way! Rather, we have to compare the costs of not issuing the warning (or regulation) against the costs of doing so.
Of course, the relevant difference is that with a hurricane, not preparing for one that hits is so much worse than preparing for one that doesn’t hit, that you’d rather issue the warning whenever the cone includes the location at all. With parking of course, the costs of over-providing parking are definitely higher than the costs of under-providing. But in the absence of performance pricing for on-street parking, there definitely are social costs to under-providing private parking, so it makes sense to require more than the bare minimum compatible with any conceivable forecast.
Existing regulations seem to treat any under-supply as a problem much worse than any possible over-supply, so they use the hurricane strategy of warning against any conceivable high demand future. Going to the opposite extreme might be an improvement on this, but the optimum strategy probably should be a little bit more careful in between.
Sorry, charlie – you’re going to need a lot more than that to allege ‘fraud’ from Shoup.
I think you’ve completely misread Shoup’s work. The 85% parking occupancy isn’t an empirical figure, it is (and always has been) a policy statement. It’s a ballpark figure. It’s meant to provide 1-2 free spaces per block.
Your misunderstanding doesn’t make it fraud.
As for the tendency to use a set of numbers for a purpose they were not intended – that is exactly the argument here. ITE trip and parking generation numbers were never intended as regulatory figures.
I think you’ve also missed the point about meteorology: it’s not that the cone of uncertainty has improved forecasts (as those forecasts have always been based in probability), but that as the models have improved, the communication of that inherent uncertainty is a big part of the success in improvement in interpretation by human meteorologists.
Kenny,
” if the cone is wide, that means that no one would issue a warning at all, because each recognizes that it’s possible that the hurricane will go the other way! Rather, we have to compare the costs of not issuing the warning (or regulation) against the costs of doing so.”
The earlier problem was that the hurricane forecasts would show just the central track, not the cone (nevermind the fact that hurricanes are huge storms – Katrina was 400 miles wide at landfall), and those away from the main track but inside the cone would ignore the forecast and fail to evacuate.
“But in the absence of performance pricing for on-street parking, there definitely are social costs to under-providing private parking, so it makes sense to require more than the bare minimum compatible with any conceivable forecast.”
I agree, but I think the conclusion to draw here is that zoning alone cannot and will not solve all of a city’s parking issues. You can’t do it through zoning alone, nor should you try.
This again asks the question: what is the policy goal for these parking requirements?
If the goal is to avoid problems on-street, then we need to acknowledge that zoning codes are always going to be an incomplete solution. You can’t get away from the need to manage on-street parking actively. I wrote about this previously here: http://www.alexblock.net/blog/?p=2740
Yes, I agree with all of that! Policy makers need to figure out what the problem is they’re trying to solve, and then propose policies that work well for those problems, in light of the uncertainty they face about the future. Proper management of on-street parking seems much better designed to address all the relevant problems than parking minimums. Zoning is probably a poor tool (though it has uses in making sure certain types of noxious chemical uses are appropriately segregated from other uses). My point is just that if the relevant policy makers can’t change the way on-street parking is managed, their second best policy change might need to incorporate the degree of uncertainty in the forecast, and not just aim for the pure lower end.
I didn’t include it in the post, but I think some of the backlash in Portland shows what a ‘range’ of parking requirements might look like.
While you can’t build a zero-parking building by right anymore, you can build one without parking if you include various things, like extra bike parking, etc. In other words, you can get to zero car parking by following a set of alternate rules. Those kinds of options provide flexbility that better reflects the uncertainty of demand.