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Regional Land-Use and Transportation Planning Using a Genetic Algorithm

Regional Land-Use and Transportation Planning Using a Genetic Algorithm. Brigham Young University Richard Balling, Ph.D., P.E. Michael Lowry Mitsuru Saito, Ph.D., P.E. funded by the National Science Foundation. Outline. Problem Formulation Genetic Algorithm Results

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Regional Land-Use and Transportation Planning Using a Genetic Algorithm

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  1. Regional Land-Use and Transportation Planning Using a Genetic Algorithm Brigham Young University Richard Balling, Ph.D., P.E. Michael Lowry Mitsuru Saito, Ph.D., P.E. funded by the National Science Foundation

  2. Outline • Problem Formulation • Genetic Algorithm • Results • Conclusions and Future Work

  3. Problem FormulationWasatch Front Region • Divide region into 343 districts. • Find optimum scenario assignment for each district from set of defined scenarios. Status Quo Scenario Assignment

  4. Problem FormulationWasatch Front Region • Identify 260 inter-district streets. • Find optimum street type assignment for each street. C2 two-lane collector C3 three-lane collector C4 four-lane collector C5 five-lane collector A2 two-lane arterial A3 three-lane arterial A4 four-lane arterial A5 five-lane arterial A6 six-lane arterial A7 seven-lane arterial F1 freeway Status Quo Street Assignment

  5. Feasible Plans Wasatch Front Region = 10420 possible plans housing capacity > 2,401,000 residents (2020 Forecast) employment capacity > 1,210,000 jobs (2020 Forecast) open space > 165,000 acres (20% of developable land)

  6. Objectives Minimize Land-Use and Street Change from Status Quo Minimize Travel Time of all trips in a 24 hour day • link-node network • peak commute period, off-peak period • home-based work trips, home-based non-work trips, non-home-based trips • trip production and attraction rates for each scenario • gravity model • Dial's multipath assignment model • congestion delays for peak commute period • measured in terms of status quo people affected • multiply people affected by degree of change factor • summed over streets and over districts

  7. Genetic Algorithm Represent plans as chromosomes 1) Random starting generation 2) Calculate feasibility and fitness of each plan 3) Create child generation from parent generation a) tournament selection b) single-point crossover c) gene-wise mutation d) maturation (elitism) 343 District Genes 260 Street Genes ... ... ... A2 C4 A3 F C2

  8. Genetic AlgorithmWasatch Front Region Start Generation

  9. Genetic AlgorithmWasatch Front Region 2nd Generation

  10. Genetic AlgorithmWasatch Front Region 4th Generation

  11. Genetic AlgorithmWasatch Front Region 6th Generation

  12. Genetic AlgorithmWasatch Front Region 12th Generation

  13. Genetic AlgorithmWasatch Front Region 30th Generation

  14. Genetic AlgorithmWasatch Front Region 100th Generation

  15. ResultsWasatch Front Region

  16. ResultsWasatch Front Region

  17. ResultsWasatch Front Region

  18. ResultsWasatch Front Region

  19. ResultsWasatch Front Region

  20. ResultsWasatch Front Region

  21. ResultsWasatch Front Region

  22. Conclusions • Genetic algorithms can be used to search over thousands of plans to find an optimum trade-off set of plans for regions. • 2) Minimizing change converted open space land to residential land – sprawl. This seems to be what has occurred in the Wasatch Front Region over the past two decades. • Minimizing travel time favored mixed usage land and upgraded street capacity. Total travel time was less than half the travel time of the min change plan.

  23. The Next Step: City Planning Region specifies scenario for a particular district City determines zone land uses that match the scenario percentages Region specifies inter-district street types City determines intra-district street types

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