1. 1 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)

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 2

3. IPF Method 3 12jΣ 1Z 11 Z 12 Z 1j Σ 2Z 21 Z 22 Z 2j Σ Σ iZ i1 Z i2 Z ij Σ Σ Σ ΣΣΣΣΣΣΣ Control Total Attribute 2 Attribute 1 Cell-based Synthesis Control Total Pre-defined categories Generate the number of agents given by the census data etc.

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

5. Cell Organization 5 123Σ 1Σ 2Σ 3Σ ΣΣΣΣ 123Σ 1Σ 2Σ 3Σ ΣΣΣΣ 123456Σ 1Σ 2Σ 3Σ 4Σ 5Σ 6Σ ΣΣΣΣΣΣΣ Elemental set of cells Combine categories Which is better? What is the best?

6. Modifiable Attribute Cell Problem (MACP) 6 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 Possible number of cell organization Continuous-valued attribute  16for 5 categories  512for 10 categories  524,288for 20 categories Categorical attribute  52for 5 categories  115,975for 10 categories  51,724,158,235,372for 20 categories 7 Apply Symbiotic Evolution

8. 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. 9 Flowchart of Symbiotic Evolution Initialization Evolution Evaluation G generation? No Yes End Start Partial-solution population Whole-solution population Best whole-solution

10. 10 For continuous-valued attributes Bit string Length : the number of categories the adjoining same bits = a combination of some categories Partial-solution (1) 000111011110000000 ①④③②⑤ Serial numbers for combination of categories

11. 11 For categorical attributes String of binary numbers Partial-solution (2) 101011110110101110 ①①③②③ Serial numbers for combination of categories ③ ↓ 5 5 6 366 ↓↓↓ ↓↓ Decimal numbers

12. 12 Combination of pointers for partial solution Partial-solution population Whole-solution 011100000111100001 011110000110011000000001111100110001 001111010000111100 000111110001100000 001111110001110001 1st attribute 2nd attribute 3rd attribute

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

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

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) 15

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

17. Categories of work status => one category Categories of age => five categories 17 Results Category of age diff(I w ) 0-90-9 10 - 14 14 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 - 74 75 - 79 80 - 84 85 - 89 90 - 1 8.5×e -9 2 1.1×e -6 3 1.3×e -6 Baby, Kindergartener, Elementary school student, Junior high school student High school student, College student, Young worker Very busy worker People who enjoy their life after retirementPeople who enjoy their life in their house

18. 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... 19

20. Genetic Algorithm Optimization algorithm Chromosome = Solution of a problem １００１０１０１０１００ １０１００１１０００１０ １００１０００１０１０１ Population １００１０１０１０１００１０１００１１０００１０ Parents １００１０１１１００１０ Children １００１０１１１００１０ Crossover Mutation １００ 1 ０１１０００１０１０１００１０００１００１０１００１０００１００１０１００１００１０００ Cannot keep good partial solutions Converge on a local minimum