Regional Land-Use and Transportation Planning Using a Genetic Algorithm

Brigham Young UniversityRichard Balling, Ph.D., P.E.Michael LowryMitsuru Saito, Ph.D., P.E.

funded by the National Science Foundation

Outline

Problem Formulation Genetic Algorithm Results Conclusions and Future Work

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

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

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)

Objectives Minimize

Travel Time of all trips in a

24 hour day

Minimize Land-Use and Street Change from

Status Quo

• measured in terms of status quo people affected

•multiply people affected by degree of change factor

• summed over streets and over districts

• 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

Genetic Algorithm Represent plans as chromosomes

1) Random starting generation2) Calculate feasibility and fitness of each plan3) Create child generation from parent generation

a) tournament selectionb) single-point crossoverc) gene-wise mutationd) maturation (elitism)

343 District Genes

A2 C4 A3 F

260 Street Genes

C2...... ...

Genetic AlgorithmWasatch Front Region

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final

ResultsWasatch Front Region

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start final

status minimum minimumquo change travel time

change 0 59,934 1,119,385 359,597travel time 1,349,617 2,025,681 984,436 1,278,768

housing 1,742,914 2,401,937 2,401,360 2,404,375employment 995,293 1,210,048 1,466,150 1,433,446open space 349,583 248,541 247,840 235,941

compromise

Minimum Feasibility

Constraints

2,401,000

1,210,000

165,000

ResultsWasatch Front Region

ResultsWasatch Front Region

ResultsWasatch Front Region

ResultsWasatch Front Region

ResultsWasatch Front Region

ResultsWasatch Front Region

Conclusions

1) 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.

3) 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.

The Next Step: City PlanningRegion 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