Public Planning Models

Craig and I just posted our entry into the Knight Newschallenge Lottery. It is called Public Planning Models, in a classic case of a working title ending up being the final title.

The basic idea is that planning models are opaque and mysterious, and really buggy and error prone. The problem isn’t the fault of the modelers or the model systems, but rather the lack of input data. Consider that a planning model first tries to model today’s world, and then tries to model the future using that same model with extrapolated conditions. There are two sources of error—the model of the present, and the extrapolation of that model into the future.

In a perfect, totalitarian state, the government would know everywhere you go, and all that information could be loaded into the model of the present. Calibration would be simple, because every vehicle is already in the model, so of course it captures reality. But even in a totalitarian, all-knowing state, predicting the future isn’t possible. Trends reverse themselves, people pick up different habits, and technology happens, changing the way we do things.

We have been watching and participating in the evolution of planning models, in particular pushing for the adoption of activity-based models over trip-based models. The big problem here is the burden of data collection, as well as the increased complexity of the model framework. Activity-based models are being incrementally adopted because they are too complicated and cost too much money to deploy.

Public Planning Models takes a different approach. Rather than trying to come up with better data collection processes and better modeling techniques, we thought it would be better to try to expose the full ugliness of current planning models to the public. This serves three purposes. First, people can see just how weak many of the fundamental assumptions in these models are. Second, everybody can take a look at the model system and suggest corrections and improvements, in the spirit of crowd-sourcing the model calibration step. And third, exposing the models and the applications of those models will give people an incentive to become more involved. That involvement can run the gamut from simply providing a few days worth of travel and activity data to the model’s input data set, to taking the model system itself and playing around with alternate planning scenarios.

Anyway, take a look at our proposal, add comments, and if you know one of the judges, put in a good word for our efforts. There are tons of submissions, and all of the ones I’ve read so far look pretty good.


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