Advertisement

Urban Dynamics Simulation Considering the Allocation of a Facility for Stopped Off

  • Hideyuki NagaiEmail author
  • Setsuya Kurahashi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 528)

Abstract

In this paper, we propose an agent-based urban model in which the relationship between a central urban area and a suburban area is expressed simply. Allocation and bustle of a public facility where residents stop off in daily life are implemented in the model. We clarify that transportation selection and residence selection of residents make an effect to change the urban structure and environment. We also discuss how a compact urban structure and a reduction in carbon dioxide emissions are achieved with urban development policies and improvements on attractiveness of the facility for pedestrians and cyclists. In addition, we conduct an experiment of the exclusion of cars from the center of the city. The experimental results confirmed that the automobile control measure would be effective in decreasing the use of automobiles along with a compact urban structure.

Keywords

Compact city Urban sprawl Household relocation Facility location problem Traffic policy 

References

  1. 1.
    Batty, M.: Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals. The MIT Press, Cambridge (2007)Google Scholar
  2. 2.
    Brown, D.G., Robinson, D.T.: Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecol. Soc. 11 (1), 46 (2006)CrossRefGoogle Scholar
  3. 3.
    Fujii, S., Someya, Y.: A behavioral analysis on relationship between individuals’ travel behavior and residential choice behavior. Infrastruct. Plan. Rev. Jpn. Soc. Civil Eng. 24, 481–487 (2007)CrossRefGoogle Scholar
  4. 4.
    Glaeser, E.: Triumph of the City: How Our Greatest Invention Makes US Richer, Smarter, Greener, Healthier and Happier. Pan Macmillan, London (2011)Google Scholar
  5. 5.
    Haase, D., Lautenbach, S., Seppelt, R.: Modeling and simulating residential mobility in a shrinking city using an agent-based approach. Environ. Modell. Softw. 25 (10), 1225–1240 (2010)CrossRefGoogle Scholar
  6. 6.
    Isono, Y., Kishimoto, T.: Properties of utility model and optimal location of public library considering halfway stop. Pap. City Plan. 46 (3), 415–420 (2011)Google Scholar
  7. 7.
    Jacobs, J.: The Death and Life of Great American Cities. Vintage, New York (1961)Google Scholar
  8. 8.
    Jager, W., Mosler, H.J.: Simulating human behavior for understanding and managing environmental resource use. J. Soc. Issues 63 (1), 97–116 (2007)CrossRefGoogle Scholar
  9. 9.
    Kaido, K.: Urban densities, quality of life and local facility accessibility in principal Japanese cities. In: Future Forms and Design for Sustainable Cities. Architectural Press, Oxford (2005)Google Scholar
  10. 10.
    Kazepov, Y.: Cities of Europe: Changing Contexts, Local Arrangement and the Challenge to Urban Cohesion, vol. 46. Wiley, Oxford (2011)Google Scholar
  11. 11.
    Kim, J.H., Pagliara, F., Preston, J.: The intention to move and residential location choice behaviour. Urban Stud. 42 (9), 1621–1636 (2005)CrossRefGoogle Scholar
  12. 12.
    Millward, H.: Urban containment strategies: a case-study appraisal of plans and policies in Japanese, British, and Canadian cities. Land Use Policy 23 (4), 473–485 (2006)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Oldenburg, R.: The Great Good Place: Café, Coffee Shops, Community Centers, Beauty Parlors, General Stores, Bars, Hangouts, and How They Get You through the Day. Paragon House Publishers, New York (1989)Google Scholar
  14. 14.
    Railsback, S.F., Grimm, V.: Agent-based and individual-based modeling: a practical introduction. Princeton University Press, Princeton (2011)zbMATHGoogle Scholar
  15. 15.
    Rieniets, T.: Shrinking cities—growing domain for urban planning? (2005). Retrieved 11 Dec 2007Google Scholar
  16. 16.
    Rieniets, T.: Shrinking cities: causes and effects of urban population losses in the twentieth century. Nat. Cult. 4 (3), 231–254 (2009)CrossRefGoogle Scholar
  17. 17.
    Rouwendal, J., Meijer, E.: Preferences for housing, jobs, and commuting: a mixed logit analysis. J. Reg. Sci. 41 (3), 475–505 (2001)CrossRefGoogle Scholar
  18. 18.
    Taniguchi, T., Takahashi, Y.: Multi-agent simulation about urban dynamics based on a hypothetical relationship between individuals’ travel behavior and residential choice behavior. Trans. Soc. Instr. Control Eng. 47, 571–580 (2012)CrossRefGoogle Scholar
  19. 19.
    Togawa, T., Hayashi, Y., Kato, H.: A expansion of equilibrium type land use model by multi agent approach. 37th the Committee of Infrastructure Planning and Management (IP), Japan Society of Civil Engineers (2008)Google Scholar
  20. 20.
    Vega, A., Reynolds-Feighan, A.: A methodological framework for the study of residential location and travel-to-work mode choice under central and suburban employment destination patterns. Transp. Res. Part A: Policy Pract. 43(4), 401–419 (2009)Google Scholar
  21. 21.
    Zukin, S.: Naked City: The Death and Life of Authentic Urban Places. Oxford University Press, Oxford (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Risk EngineeringGraduate School of Systems and Information Engineering, University of TsukubaTokyoJapan
  2. 2.Graduate School of System ManagementUniversity of TsukubaTokyoJapan

Personalised recommendations