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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 ) 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

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  1. 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)

  2. 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

  3. IPF Method Control Total Attribute 2 Pre-defined categories Generate the number of agents given by the census dataetc. Attribute 1 Control Total Cell-based Synthesis

  4. Analogy : Modifiable Area Unit Problem • Spatial analysis • The results vary according to the spatial zoning model • Two factors • Scale of units • Type of units

  5. Cell Organization Elemental set of cells Combine categories Which is better? What is the best?

  6. Modifiable Attribute Cell 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

  7. Computational Complexity of MACP 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

  8. Symbiotic Evolution • 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

  9. Partial-solution population Whole-solution population Flowchart of Symbiotic Evolution Start Initialization Evaluation Evolution G generation? No Yes Best whole-solution End

  10. Partial-solution (1) For continuous-valued attributes • Bit string • Length : the number of categories • the adjoining same bits = a combination of some categories 000111011110000000 ① ② ③ ④ ⑤ Serial numbers for combination of categories

  11. Partial-solution (2) For categorical attributes • String of binary numbers 101011110110101110 5 6 3 6 5 6 Decimal numbers ↓ ↓ ↓ ↓ ↓ ↓ ① ② ③ ③ ① ③ Serial numbers for combination of categories

  12. Whole-solution • Combination of pointers for partial solution 2nd attribute 3rd attribute 1st attribute 001111110001110001 011100000111100001 001111010000111100 000111110001100000 000001111100110001 011110000110011000 Partial-solution population

  13. Fitness Value (1) • For a whole-solution Iw • Difference of the key output value • Fitness value Elemental set of cells Key output variable Constraint

  14. Fitness Value (2) • For a Partial-solution Ip • the best fitness value in whole-solutions that are pointed by the partial-solution

  15. Case 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)

  16. Case Study (2) • Microsimulation model • Aging • Death • Birth Monte Carlo simulation • Work status change • Key output value • Trip generation number after 5 years

  17. Results • 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

  18. Conclusion • 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

  19. Please ask questions in easy English...

  20. ０ ０ ０ ０ １ ０ ０ １ １ １ １ １ ０ ０ ０ ０ ０ １ １ ０ １ １ １ １ １ ０ １ ０ Population ０ ０ ０ ０ ０ ０ １ ０ ０ １ ０ ０ １ １ １ １ １ １ ０ ０ ０ ０ ０ ０ １ ０ ０ １ 1 １ １ １ １ １ ０ １ ０ １ １ １ ０ ０ ０ ０ １ ０ １ １ ０ ０ ０ ０ ０ ０ ０ ０ １ ０ １ １ ０ １ ０ ０ １ １ ０ ０ ０ ０ １ ０ ０ ０ １ １ ０ ０ ０ ０ １ ０ ０ １ ０ ０ １ １ ０ ０ ０ ０ ・・・ ０ １ １ ０ ０ １ ０ ０ ０ ０ １ ０ 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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