Brigham Young University researchers are developing a scientific approach to land-use and transportation planning that could significantly aid high-growth areas like the Wasatch Front.
A team of professors from several disciplines, working under a grant from the National Science Foundation, has created an "optimization model" that sifts through thousands of potential plans. The plans are ranked based on several criteria, and new plans are devised from the most desirable ones.BYU researchers, in conjunction with Envision Utah and the Wirthlin Group, hope to survey 10,000 Wasatch Front residents to determine the factors they feel are most important as public officials and planners prepare to deal with the area's high population growth rate.
Those factors could then be plugged into the optimization model to create planning options that would most effectively accomplish what residents want - like better traffic flow - while minimizing things like cost and change.
Part of the process involves gathering Utahns' opinions about how to handle growth. That process began Tuesday when Envision Utah sponsored the first of a series of meetings to seek public comment. The meetings continue at various locations around the state through the end of the month.
BYU civil engineering professor Rick Balling believes the "genetic algorithm" the team is perfecting could be a significant breakthrough for urban planners everywhere. The formula involves creating 100 generations of 100 plans each, with the idea that the plans in the final generation will be highly refined.
"The key here is we didn't want to just pick 10,000 plans," Balling said. "We wanted to pick the best ones."
Balling's current research involves dividing the city of Provo into 130 zones depending on various land-use characteristics, such as high-density residential, low-density residential, commercial and industrial. Researchers also identified the 25 major traffic corridors in the city.
Balling then plugged information about those zones and traffic corridors into a computer program that processes it through numerous iterations before devising plans that most effectively divide the city's zones and roads. Inherent in the process is that the best plans must allow the city to accommodate 140,000 residents by 2018, as well as provide housing for residents of various income levels.
"This is a way of generating new plans which have a high probability of being good," Balling said. "There really isn't much formalization of land-use and transportation planning. Most of it is done by just trial and error."
The genetic algorithm uses concepts such as mutation, selection and crossover - ideas that are more familiar within the biological world than the urban planning one.
Balling next wants to develop a two-city plan. Sometimes, he said, what's best for one city is not good for a neighboring city. For example, dumping heavy traffic into Orem might be optimal for Provo, but it would harm Orem.
Also, Balling said, when the model is perfected it will allow planners to plug in various objectives to be achieved. As long as the objectives can be quantified and measured, the optimization model will produce plans that best achieve them.