Coordinated Regional and City Planning Using a Genetic Algorithm Coordinated Regional and City Planning Using a Genetic Algorithm
Improved methods of planning are needed to deal with today’s issues of traffic congestion, sprawl, and loss of greenspace. Past research and recent legislation call for new methods that will consider a regional perspective. Regional planning is challenged with two difficult questions:
1.Is it possible to achieve regional goals without infringing upon the local autonomy of city planners?
2.Is it possible to objectively analyze the thousands, even millions, of land use and transportation plans to find the best design?
Metropolitan regions across the country have made great efforts to answer the first question. Unfortunately, effective methods for harmonizing the goals of regional and city planners have not been developed. Likewise, efforts have been made to introduce objectivity into the planning process. However, current methods continue to be subjective because there is no way to efficiently analyze the millions of alternative plans for objective decision-making.
This thesis presents a new approach to regional planning that provides an affirmative answer to the two questions posed above. The first question is answered through a unique problem formulation and a corresponding 3 stage process that compels coordination between the regional and city planners. Regional goals are achieved because they are cast as objectives and constraints in stage one. Local autonomy is achieved because some of the decisions are left for the city planners to decide in the second stage. The third stage allows for negotiation between the regional and city planners. The second question is answered through the use of a genetic algorithm. The genetic algorithm provides the means to objectively consider millions of plans to find the best ones. The new approach is demonstrated on the main metropolitan region of Utah and a local city center within the region. The results from the case study provided the opportunity to learn valuable lessons concerning land use and transportation planning that can be applied to other regions experiencing rapid growth.
School:Brigham Young University
School Location:USA - Utah
Source Type:Master's Thesis
Keywords:coordinate planning regional land use transportation genetic algorithm multiobjective city conflict objectivity automation scenario street
Date of Publication:06/14/2003