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Urban Dynamics Simulation Considering Street Activeness and Transport Policies

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11186))

Abstract

The purpose of this research is to verify the effectiveness of policies to control urban sprawl. By using an agent-based model (ABM), which was built for simulating urban structure changes through autonomous behavior of urban residents, this research clarified the following points and how they were. First, the combination of the proper location of a public facility for urban residents and the implementation of a policy to promote activeness around it was effective in maintaining a compact urban structure. Second, similarly, the synergistic effects of some transport policies and the above-mentioned policies could impact positively on urban environment generally.

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References

  1. White paper on land, infrastructure and transport in 2016 - the ministry of land, infrastructure and transport. http://www.mlit.go.jp/hakusyo/mlit/h28/index.html

  2. Acheampong, R.A., Silva, E.: Land use-transport interaction modeling: a review of the literature and future research directions. J. Transp. Land Use 8(3), 11–38 (2015)

    Google Scholar 

  3. Anderson, W.P., Kanaroglou, P.S., Miller, E.J.: Urban form, energy and the environment: a review of issues, evidence and policy. Urban Stud. 33(1), 7–35 (1996)

    Article  Google Scholar 

  4. Batty, M.: Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals. The MIT press, Cambridge (2007)

    Google Scholar 

  5. Behan, K., Maoh, H., Kanaroglou, P.: Smart growth strategies, transportation and urban sprawl: simulated futures for Hamilton, Ontario. Can. Geogr./Le Géographe canadien 52(3), 291–308 (2008)

    Article  Google Scholar 

  6. Brueckner, J.K., Mills, E., Kremer, M.: Urban sprawl: lessons from urban economics. In: Brookings-Wharton Papers on Urban Affairs, pp. 65–97 (2001)

    Article  Google Scholar 

  7. Deal, B., Schunk, D.: Spatial dynamic modeling and urban land use transformation: a simulation approach to assessing the costs of urban sprawl. Ecol. Econ. 51(1), 79–95 (2004)

    Article  Google Scholar 

  8. Ewing, R.: Is Los Angeles-style sprawl desirable? J. Am. Plann. Assoc. 63(1), 107–126 (1997)

    Article  Google Scholar 

  9. Gilbert, N.: Agent-Based Models, vol. 153. Sage, London (2008)

    Book  Google Scholar 

  10. Gimblett, R., Daniel, T., Cherry, S., Meitner, M.J.: The simulation and visualization of complex human-environment interactions. Landscape Urban Plann. 54(1), 63–79 (2001)

    Article  Google Scholar 

  11. Johnson, M.P.: Environmental impacts of urban sprawl: a survey of the literature and proposed research agenda. Environ. Plann. A 33(4), 717–735 (2001)

    Article  Google Scholar 

  12. Kaneda, T.: Modeling visitors’ shopping-around behaviors in shopping district. J. Jpn. Soc. Artif. Intell. 30(4), 423–428 (2015)

    Google Scholar 

  13. Lagarias, A.: Urban sprawl simulation linking macro-scale processes to micro-dynamics through cellular automata, an application in Thessaloniki, Greece. Appl. Geogr. 34, 146–160 (2012)

    Article  Google Scholar 

  14. Ligtenberg, A., Bregt, A.K., Van Lammeren, R.: Multi-actor-based land use modelling: spatial planning using agents. Landscape Urban Plann. 56(1), 21–33 (2001)

    Article  Google Scholar 

  15. Railsback, S.F., Grimm, V.: Agent-Based and Individual-Based Modeling: A Practical Introduction. Princeton University Press, Princeton (2011)

    Google Scholar 

  16. Schneider, A., Woodcock, C.E.: Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information. Urban Stud. 45(3), 659–692 (2008)

    Article  Google Scholar 

  17. Scott, D.M., Kanaroglou, P.S., Anderson, W.P.: Impacts of commuting efficiency on congestion and emissions: case of the Hamilton CMA, Canada. Transp. Res. Part D Transp. Environ. 2(4), 245–257 (1997)

    Article  Google Scholar 

  18. Tsai, Y.H.: Quantifying urban form: compactness versus ‘sprawl’. Urban Stud. 42(1), 141–161 (2005)

    Article  MathSciNet  Google Scholar 

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Correspondence to Hideyuki Nagai .

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Nagai, H., Kurahashi, S. (2018). Urban Dynamics Simulation Considering Street Activeness and Transport Policies. In: Staab, S., Koltsova, O., Ignatov, D. (eds) Social Informatics. SocInfo 2018. Lecture Notes in Computer Science(), vol 11186. Springer, Cham. https://doi.org/10.1007/978-3-030-01159-8_21

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  • DOI: https://doi.org/10.1007/978-3-030-01159-8_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01158-1

  • Online ISBN: 978-3-030-01159-8

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