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

Yuwei Liu, Shan Qin, Jiamin Li and Ting Jin ()
Additional contact information
Yuwei Liu: School of Urban Design, Wuhan University, Wuhan 430072, 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

Sustainability, 2023, vol. 15, issue 23, 1-15

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.

Keywords: urban space planning; green economy; geographic information system; genetic algorithm; artificial intelligence technology (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/23/16367/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/23/16367/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:23:p:16367-:d:1289483

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().