An agent-based learning-embedded model (ABM-learning) for urban land use planning: A case study of residential land growth simulation in Shenzhen, China. (June 2020)

• Record Type: Journal Article
• Authors: Li, Feixue; Li, Zhifeng; Chen, Honghua; Chen, Zhenjie; Li, Manchun
• Is Part Of: Land use policy. Volume 95(2020)
• Publication Date: 2020-06
• Subjects: Agent-Based modelling -- Experience-Weighted attraction learning model -- Residential land growth -- ABM-learning -- Shenzhen city; Land use -- Periodicals; Land use -- Government policy -- Periodicals; Sol, Utilisation du -- Périodiques; Sol, Utilisation du -- Politique gouvernementale -- Périodiques; Electronic journals; 333.7305
• DOI: 10.1016/j.landusepol.2020.104620
• Languages: English
• ISSNs: 0264-8377

Abstract

Highlights: We propose an agent-based learning-embedded model (ABM-learning) for model for land use change simulation. We extend EWA learning model by redefining the payoff function and the to-be-reinforced strategy set. The pilot application in Shenzhen showed that the site-specific accuracy has been improved with learning model embedded. The model is applicable to aiding in land use and public service facility allocations in detailed planning of urban land use. 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.