Abstract
Urban expansion has far-reaching implications for economy, environment, and socio-cultural aspects of a city. Therefore, it is essential to have a thorough understanding of the complex dynamics and driving factors behind urban expansion in order to make informed decisions that promote the long-term sustainability of a city. Currently, cellular automata (CA) and agent-based modeling (ABM) have been widely employed to simulate urban land growth. However, existing research lacks a comprehensive consideration of the influence of individual agent attributes and land population capacity on site selection decisions. Consequently, we propose a novel approach that incorporates fine-scale population data into the site-selection decision simulation process, allowing for a granular depiction of individual decision attributes. Moreover, the site selection process integrates assessment criteria, including population capacity and neighborhood development status. Furthermore, to address the issue of fragmented simulated residential land use outcomes, population redistribution is iteratively conducted. Additionally, by integrating extended reinforcement learning mechanisms, the site selection process of residential multi-agent systems experiences a significant improvement in overall simulation accuracy. The proposed model was applied to simulate urban expansion in Shenzhen, Guangdong province, China. The results demonstrated that this model effectively enhances the behavioral decision-making capabilities of intelligent agents, thereby providing insights into the mechanisms underlying urban expansion. These findings hold considerable significance for making informed urban planning decisions and advancing the goal of sustainable urban development.
NETL
NSTL
Springer Nature