An agent-based learning-embedded model (ABM-learning) for urban land use planning: A case study of residential land growth simulation in Shenzhen, China

Feixue Li, Zhifeng Li, Honghua Chen, Zhenjie Chen and Manchun Li

Land Use Policy, 2020, vol. 95, issue C

Abstract: A forward-looking urban land use plan is crucial to a city’s sustainability, which requires a deep understanding of human-environment interactions between different domains, and modelling them soundly. One of the key challenges of modelling these interactions is to understand and model how human individuals make and develop their location decisions by learning that then shape urban land-use patterns. To investigate this issue, we have constructed an extended experience-weighted attraction learning model to represent the human agents’ learning when they make location decisions. Consequently, we propose and have developed an agent-based learning-embedded model (ABM-learning) for residential land growth simulation that incorporates a learning model, a decision-making model, a land use conversion model and the constraint of urban land use master plan. The proposed model was used for a simulation of the residential land growth in Shenzhen city, China. By validating the model against empirical data, the results showed that the site-specific accuracy of the model has been improved when embedding learning model. The analysis on the simulation accuracies has proved the argument that modelling individual-level learning matters in the agent’s decision model and the agent-based models. We also applied the model to predict residential land growth in Shenzhen from 2015 to 2035, and the result can be a reference for land-use allocation in detailed planning of Shenzhen. The ABM-learning is applicable to studying the past urban growth trajectory, aiding in the formulation of detailed residential land and public service facility planning and assessing the land use planning effectiveness.

Keywords: Agent-Based modelling; Experience-Weighted attraction learning model; Residential land growth; ABM-learning; Shenzhen city (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0264837719303254
Full text for ScienceDirect subscribers only

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:eee:lauspo:v:95:y:2020:i:c:s0264837719303254

DOI: 10.1016/j.landusepol.2020.104620

Access Statistics for this article

Land Use Policy is currently edited by Jaap Zevenbergen

More articles in Land Use Policy from Elsevier
Bibliographic data for series maintained by Joice Jiang ().