Authors: Heeseo Kwon*, University of Cambridge, Elisabete A. Silva, University of Cambridge
Topics: Spatial Analysis & Modeling, Urban and Regional Planning, Coupled Human and Natural Systems
Keywords: SLEUTH, behavioral theories, agent-based modelling, cellular automata, data science, complexity theory, geographic information systems, planning support systems
Session Type: Virtual Paper
Start / End Time: 1:30 PM / 2:45 PM
Room: Virtual 9
Presentation File: No File Uploaded
Understanding and predicting spatial patterns of urban change is greatly useful for planners to deliver evidence-based and adaptive policies to address current problems and future sustainability. While approaches like agent-based modelling (ABM) and Cellular Automata (CA) have been applied to generate dynamic simulation, the current approach is limited in taking the complex agent behaviour into account. This research makes an empirical application of structuring behavioural theories into rules by extending the existing CA-based SLEUTH urban growth model into a CA/ABM-based land use-transport interaction model on NetLogo using data from Sejong, Korea. Here, residents’ travel behaviour (car to non-car mode switch) is linked with the road-influenced growth of SLEUTH. First, we refined the spatial data by applying traffic volume big data into the transport layer and using building height and building use data as variables for resident agents’ mode switch behaviour. Second, we set the individual characteristics of each resident agents based on the raw data of annual socio-economic household survey. Based on three behavioural theories from psychology and sociology, we selected twelve variables from the survey and formulated both equation- and language-based rules for each resident agents’ mode switch decision. Finally, we present simulation results of two policy scenarios and discuss about the importance of linking behavioural rules with theories and using language-based rules, benefits of using existing survey data to assign individual agent attributes for microsimulation, policy implications for behavioural intervention, the complexity of urban modelling, and suggestions for further research.