Design of Machine Learning Model for Urban Planning and Management Improvement

doi:10.23940/ijpe.20.06.p14.958967

Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (6): 958-967.doi: 10.23940/ijpe.20.06.p14.958967

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Design of Machine Learning Model for Urban Planning and Management Improvement

Jiafeng Zhou^a, Tian Liu^b, and Lin Zou^c,*

^a Creative Centre for Arts Science Architecture (CCASA), Jilin Jianzhu University, Changchun, 130000, China;
^b School of Tourism and Foreign Languages, Henan Institute of Economics and Trade, Zhengzhou, 450000, China;
^c School of Informatics, University of Leicester, Leicester, LE1 7RH, UK

Submitted on ; Revised on ; Accepted on
Contact: * E-mail address: lz179@leicester.ac.uk
About author:Dr. Jiafeng Zhou has a doctoral degree in politics from Jilin University. He is now an associate professor at Jilin Jianzhu University. His has conducted research in urban planning and management.
Tian Liu has a master's degree in English language and literature from Jilin University. She works as an English lecturer at Henan Institute of Economics and Trade, is also a Ph.D. student at the University of Bathspa, and has conducted research on humour generation from the perspective of creative computing. Her research interest mainly lies in English linguistics, TESOL, cognitive linguistics, and creative computing.
Lin Zou has a bachelor's degree in accounting and finance from De Montfort University with first class honours. He is currently a Ph.D. student at the University of Leicester and has researched creative computational framework to support user's creativity in rich knowledge environments. His fields of interest are creative computing, semantic web, and creative decision-making model development.

Abstract

Abstract: With the aid of artificial intelligence, this paper builds a machine learning model with the KNN algorithm to optimize the traditional method of urban planning and management (UPM). The optimization is realized through two steps. First, the relevant theories of the updated UPM of livelihood oriented UPM (LOUPM) are explored for the later machine learning architecture design. People's livelihood has a great influence on UPM. Livelihood orientation makes the complex UPM even more complicated due to diverse living needs of citizens. This paper analyzes the deeper relationship between the people's livelihood and UPM, systematically studies the function and connotation of the people's livelihood behavior, and profoundly discusses current contradictions and restraining factors. Second, based on a better understanding of LOUPM, this paper further proposes an artificial intelligence approach to select most related factors to optimize UPM from databases. In the first paper of this series, in the analysis of the relevant UPM theories, three scopes of LOUPM are concluded to be the evaluation of data sets: authority, time, and space. Then, this paper continues to design a software model with the KNN algorithm to evaluate the urban plans and generates optimization advice for the user. Based on this research, it is possible to explore LOUPM. In order to make every effort to seek ways and methods to meet the challenge of the LOUPM in the new period, and by introducing a creative computing approach of government management and social governance, this article can contribute to the growing demand of China's urbanization process.

Key words: creative computing approach, machine learning, urban planning and management, natural language processing

Jiafeng Zhou, Tian Liu, and Lin Zou. Design of Machine Learning Model for Urban Planning and Management Improvement [J]. Int J Performability Eng, 2020, 16(6): 958-967.

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References

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Design of Machine Learning Model for Urban Planning and Management Improvement

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