Modifiable attribute cell problem in population synthesis for land use microsimulation
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Modifiable Attribute Cell Problem in Population Synthesis for Land-Use Microsimulation. Noriko Otani (Tokyo City University ) Nao Sugiki ( Docon Co., Ltd.) Kazuaki Miyamoto (Tokyo City University ). Land-Use Microsimulation.

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Modifiable attribute cell problem in population synthesis for land use microsimulation

Modifiable Attribute Cell Problem in Population Synthesis for Land-Use Microsimulation

Noriko Otani (Tokyo City University)

NaoSugiki (Docon Co., Ltd.)

Kazuaki Miyamoto (Tokyo City University)


Land use microsimulation
Land-Use for Land-Use Microsimulation

  • A popular approach to describe detailed changes in land use and transportation

  • Micro-level modeling of a dataset of individuals

    Micro-data

    Require micro-data for the base year

    Synthesize data based on

    Iterative Proportional Fitting (IPF) method


Ipf method
IPF Method for Land-Use

Control Total

Attribute 2

Pre-defined categories

Generate the number of agents

given by the census dataetc.

Attribute 1

Control Total

Cell-based Synthesis


Analogy modifiable area unit problem
Analogy : Modifiable Area Unit Problem for Land-Use

  • Spatial analysis

    • The results vary according to the spatial zoning model

    • Two factors

      • Scale of units

      • Type of units


Cell organization
Cell Organization for Land-Use

Elemental set of cells

Combine categories

Which is better?

What is the best?


Modifiable attribute cell problem macp
Modifiable Attribute Cell for Land-Use Problem (MACP)

  • Optimization problem for microsimulation

    Target output : “key output variable”

    Base of decision making

  • Condition

    Benchmark : Elemental set of cells

    (pre-defined categories)

    Constraint : No significant difference of the key output variable from the benchmark

    Goal : Minimize the number of cells


Computational complexity of macp
Computational Complexity of MACP for Land-Use

Apply Symbiotic Evolution

  • Possible number of cell organization

    • Continuous-valued attribute

      • 16 for 5 categories

      • 512 for 10 categories

      • 524,288 for 20 categories

    • Categorical attribute

      • 52 for 5 categories

      • 115,975 for 10 categories

      • 51,724,158,235,372 for 20 categories


Symbiotic evolution
Symbiotic Evolution for Land-Use

  • One kind of “Genetic Algorithm”

    • Optimization algorithm

    • Imitates biological evolution process

    • Applicable to various problems

  • Parallel evolution of two population

    • Whole-solution = Combination of partial solutions

    • Partial-solution

  • Avoid local minimum and find good solution


Flowchart of symbiotic evolution

Partial-solution for Land-Use population

Whole-solution population

Flowchart of Symbiotic Evolution

Start

Initialization

Evaluation

Evolution

G generation?

No

Yes

Best whole-solution

End


Partial solution 1
Partial-solution (1) for Land-Use

For continuous-valued attributes

  • Bit string

    • Length : the number of categories

    • the adjoining same bits = a combination of some categories

000111011110000000

Serial numbers for combination of categories


Partial solution 2
Partial-solution (2) for Land-Use

For categorical attributes

  • String of binary numbers

101011110110101110

5

6

3

6

5

6

Decimal numbers

Serial numbers for combination of categories


Whole solution
Whole-solution for Land-Use

  • Combination of pointers for partial solution

2nd attribute

3rd attribute

1st attribute

001111110001110001

011100000111100001

001111010000111100

000111110001100000

000001111100110001

011110000110011000

Partial-solution population


Fitness value 1
Fitness for Land-Use Value (1)

  • For a whole-solution Iw

    • Difference of the key output value

    • Fitness value

Elemental set of cells

Key output variable

Constraint


Fitness value 2
Fitness for Land-Use Value (2)

  • For a Partial-solution Ip

    • the best fitness value in whole-solutions that are pointed by the partial-solution


Case study 1
Case for Land-Use Study (1)

  • Data

    • obtained from the person-trip-survey for the Sapporo metropolitan area in Japan

    • 5,000 persons

  • Attribute

    • Age

      18 categories (0-9, 10-14, 15-19, ..., 85-89, >90)

    • Work status

      5 categories (primary industry, secondary industry, tertiary industry, student, housewife or other)


Case study 2
Case for Land-Use Study (2)

  • Microsimulation model

    • Aging

    • Death

    • Birth Monte Carlo simulation

    • Work status change

  • Key output value

    • Trip generation number after 5 years


Results
Results for Land-Use

  • Categories of work status => one category

  • Categories of age => five categories

High school student, College student, Young worker

Baby, Kindergartener, Elementary school student, Junior high school student

Very busy worker

People who enjoy their life after retirement

People who enjoy their life in their house


Conclusion
Conclusion for Land-Use

  • Addressed the modifiable attribute cell problem in cell-based population synthesis for microsimulation

  • Proposed a method for the cell organization

  • Proved the usefulness by simple case study


Please ask questions in for Land-Use easy English...


Genetic algorithm

for Land-Use

Population

1

・・・

Genetic Algorithm

  • Optimization algorithm

    • Chromosome = Solution of a problem

Parents

Crossover

Children

Cannot keep good partial solutions

Converge on a local minimum

Mutation


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