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Artificial Intelligence and Street Space Optimization in Green Cities: New Evidence from China

Author

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  • Yuwei Liu

    (School of Urban Design, Wuhan University, Wuhan 430072, China
    School of Arts and Communication, China University of Geosciences, Wuhan 430074, China)

  • Shan Qin

    (Huzhou University, Huzhou 313000, China)

  • Jiamin Li

    (Westminster School of Arts, University of Westminster, London HA1 3TP, UK)

  • Ting Jin

    (School of Political Science and Public Administration, Wuhan University, Wuhan 430072, China)

Abstract

In the context of the green economy and sustainable urban development, the rapid expansion of urban construction has given rise to pressing public health concerns, notably environmental pollution and the increased prevalence of chronic illnesses linked to swift urbanization. These urban health issues are escalating, prompting significant attention to the concept of creating “healthy cities”. Meanwhile, the planning and design of urban street space have a far-reaching impact on urban residents’ quality of life and health. Urban planners are facing challenges and need to follow the principle of a green economy while meeting the needs of residents for public activities and adapting to motor vehicle traffic. This study explores the optimization of urban street space to promote the harmonious coexistence between people and cars. This study actively explores the relationship between health, urban environment, and social background, focusing on promoting the harmonious coexistence between people and vehicles, especially the optimization goal of sharing urban streets. The study’s main goal is to design a road that can meet the needs of citizens’ public activities and accommodate motor vehicles, which conforms to the principle of a green economy. To achieve this, geographic information system (GIS) technology and a genetic algorithm (GA) are employed to optimize shared urban street spaces. Among them, GIS tools are used for spatial simulation to evaluate the effect of different shared street space configurations. The urban shared street space is gradually optimized through GA’s selection, crossover, and mutation operations. Simulation experiments are conducted to determine the relationship between street space utilization and the elements of a healthy city, ultimately striving to identify the optimal design parameters for shared street spaces. The research results reveal that the urban street space is optimized from the three aspects of shared allocation of facilities resources, replacement of land use functions, and mixed layout of facilities, and the utilization rate of urban streets is finally ensured to reach 53.43%, fully assuming the essential functions of urban streets. This innovative approach bridges the gap between urban development and public health, offering valuable insights for sustainable urban space planning and enhanced living environments within the framework of the green economy.

Suggested Citation

  • Yuwei Liu & Shan Qin & Jiamin Li & Ting Jin, 2023. "Artificial Intelligence and Street Space Optimization in Green Cities: New Evidence from China," Sustainability, MDPI, vol. 15(23), pages 1-15, November.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16367-:d:1289483
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    References listed on IDEAS

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