regional land-use and transportation planning using a genetic algorithm brigham young university...
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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
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
50000
250000
450000
650000
850000
1050000
1250000
900000 1400000 1900000 2400000 2900000 3400000travel time
ch
an
ge
Start Generation
Genetic AlgorithmWasatch Front Region
2nd
Generation
50000
250000
450000
650000
850000
1050000
1250000
900000 1400000 1900000 2400000 2900000 3400000travel time
chan
ge
Genetic AlgorithmWasatch Front Region
4th
Generation
50000
250000
450000
650000
850000
1050000
1250000
900000 1400000 1900000 2400000 2900000 3400000
travel time
chan
ge
Genetic AlgorithmWasatch Front Region
6th Generation
50000
250000
450000
650000
850000
1050000
1250000
900000 1400000 1900000 2400000 2900000 3400000travel time
chan
ge
Genetic AlgorithmWasatch Front Region
12th
Generation
50000
250000
450000
650000
850000
1050000
1250000
900000 1400000 1900000 2400000 2900000 3400000travel time
chan
ge
Genetic AlgorithmWasatch Front Region
30th
Generation
50000
250000
450000
650000
850000
1050000
1250000
900000 1400000 1900000 2400000 2900000 3400000
travel time
chan
ge
Genetic AlgorithmWasatch Front Region
100th
Generation
50000
250000
450000
650000
850000
1050000
1250000
900000 1400000 1900000 2400000 2900000 3400000travel time
chan
ge
final
ResultsWasatch Front Region
50000
250000
450000
650000
850000
1050000
1250000
900000 1400000 1900000 2400000 2900000 3400000travel time
chan
ge
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
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.