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General Information
    • ISSN: 2010-3700
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Engineering & Technology Digital Library, Google Scholar, Crossref, ProQuest, Electronic Journals Library, DOAJ and EI (INSPEC, IET).
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol.3(5): 440-444 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.356

Complexity and Dynamic Modeling of Urban System

Wensheng Zhou and Qiang Li
Abstract—Complex system theory has made people recognize and understand urban phenomena from a new perspective. In particular, The research method of the complex system has provided a powerful tool to explore problems of urban system. In this paper, basic theories of research on complexity science and complex system are described, the necessity of dynamic modeling and simulation methods as well as general steps are analyzed for the complex system. Based on this, the complexity of urban system is discussed and analyzed. Besides, the means by which dynamic simulation methods of complex system are applied in studying urban system and their significances are discussed. The result shows that it is necessary to depict, analyze and know about problems on urban complexity and explore deeper rules hidden in dynamical urban development with the theoretical framework of complexity science, so as to better interpret, design and plan cities.

Index Terms—Complexity science, CA, MAS, urban system.

Zhou Wensheng is with School of Architecture, Tsinghua University, Beijing, 100084, China (e-mail: zwsbj@163.com). Li Qiang is with 295847 Department of PLA, Beijing, 100120, China (e-mail: liquider@sina.com).


Cite:Wensheng Zhou and Qiang Li, "Complexity and Dynamic Modeling of Urban System," International Journal of Machine Learning and Computing vol.3, no. 5, pp. 440-444, 2013.

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