Residential development simulation based on learning by agent-based model

  • Hamid Mirzahossein Department of Civil-Transportation Planning Imam Khomeini International University, Qazvin https://orcid.org/0000-0003-1615-9553
  • Vahid Noferesti Department of Civil-Transportation Planning, Imam Khomeini International University, Qazvin
  • Xia Jin Florida International University, Miami, Florida https://orcid.org/0000-0002-8660-3528
Keywords: Agent-Based Model (ABM), Agent Learning, Residential Development, Qazvin

Abstract

Increasing population and desire for urbanization increase housing demand in urban areas and ultimately induce growth and development of residential land-uses that result in urban sprawl. This paper simulates these sprawls of residential land-use in Qazvin city based on learning method by agent-based model. For this purpose, a model with the ability to learn from agents has been developed, in which families as agents can interact with each other and learn based on previous decisions. The model makes it possible to simulate residential land-use conversion based on the agent-based structure over the ten years by applying both demographic changes and household relocation desirability. The multiplication of the average level of land occupation by each family and the number of inserted new families indicates the potential magnitude of land-use changes. Also, results show the priority of residential development locations partially in the northeast regions and a small part of the south of Qazvin. These developments are expected to move towards the east in ten years.

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Author Biographies

Hamid Mirzahossein, Department of Civil-Transportation Planning Imam Khomeini International University, Qazvin

Associate Professor in the Civil - Transportation Planning Department of Imam Khomeini International University since 2017. He holds his Ph.D. in transportation planning and engineering from the Iran University of Science and Technology and passed his research scholar at the University of Arizona. He conducts research in transportation and land-use interaction, accessibility modeling, intelligent transportation, and smart city.

Vahid Noferesti, Department of Civil-Transportation Planning, Imam Khomeini International University, Qazvin

Ph.D. candidate in the Civil - Transportation Planning Department at Imam Khomeini International University. He received his master of science from the Iran University of Science and Technology. His interest is machine learning and modeling the land use and transportation interaction.

Xia Jin, Florida International University, Miami, Florida

Dr. Jin is an Associate Professor at Florida International University - College of Engineering & Computing. She holds her Ph.D. in Civil Engineering at the University of Wisconsin – Milwaukee. She conducts research in Activity-Travel Behavior Analysis, Transportation System Modeling and Simulation, Freight planning and modeling, Data Analytics, Land Use-Transportation Interactions, Smart City Initiatives, Geographic Information Systems, Travel Survey Methods, and Emerging Technologies and Mobility Options.

References

Acosta, L.A., Rounsevell, M.D.A., Bakker, M., van Doorn, A., Gómez-Delgado, M., & Delgado, M. (2014). An agent-based assessment of land use and ecosystem changes in traditional agricultural landscape of Portugal. Intelligent Information Management, 6(2), 55-80. https://doi.org/10.4236/iim.2014.62008.

Alonso, W. (1964). Location and land use. toward a general theory of land rent. Location and Land Use. Toward a General Theory of Land Rent. Harvard: Harvard University Press.

Arentze, T., Hofman, F., van Mourik, H., & Timmermans, H. (2000). ALBATROSS: multiagent, rule-based model of activity pattern decisions. Transportation Research Record. 1706(1), 136-144. https://doi.org/10.3141/1706-16.

Azari, K.A., Arintono, S., Hamid, H., & Davoodi, S. R. (2013). Evaluation of demand for different trip purposes under various congestion pricing scenarios. Journal of Transport Geography, 29(0), 43-51. https://doi.org/10.1016/j.jtrangeo.2013.01.001.

Beck, M.J., Hess, S., Cabral, M.O., & Dubernet, I. (2017). Valuing travel time savings: A case of short-term or long-term choices? Transportation Research Part E: Logistics and Transportation Review. 100(4), 133-143. https://doi.org/10.1016/j.tre.2017.02.001.

Benenson, I., Omer, I., & Hatna, E. (2002). Entity-based modeling of urban residential dynamics: the case of Yaffo, Tel Aviv. Environment and Planning B: Planning and Design. 29, 491-512. https://doi.org/10.1068/b1287.

Bert, F.E., Podestá, G.P., Rovere, S.L., Menéndez, Á.N., North, M., Tatara, E., Laciana, C.E., Weber, E., & Toranzo, F.R. (2011). An agent-based model to simulate structural and land use changes in agricultural systems of the argentine pampas. Ecological Modelling, 222(19), 3486-3499. https://doi.org/10.1016/j.ecolmodel.2011.08.007.

Bousquet, F., & le Page, C. (2004). Multi-agent simulations and ecosystem management: a review. Ecological Modelling, 176(3-4), 313-332. https://doi.org/10.1016/j.ecolmodel.2004.01.011.

Campos, P.B.R., de Almeida, C.M., & de Queiroz, A. P. (2018). Educational infrastructure and its impact on urban land use change in a peri-urban area: a cellular-automata based approach. Land Use Policy, 79, 774-788. https://doi.org/10.1016/j.landusepol.2018.08.036.

Chen, Y., Irwin, E., Jayaprakash, C., & Park, K.J. (2021). An Agent Based Model of a Thinly Traded Land Market in an Urbanizing Region. Journal of Artificial Societies and Social Simulation, 24(2). https://doi.org/10.18564/jasss.4518.

Huang, Q., Parker, D.C., Filatova, T., & Sun, S. (2014). A review of urban residential choice models using agent-based modeling. Environment and Planning B: Planning and Design, 41(4), 661-689. https://doi.org/10.18564/jasss.4518.

Hunt, J.D. (2003). Design and Application of The Pecas Land Use Modelling System. Proceedings of the 8th International Conference on Computers in Urban Planning and Urban Management (CUPUM).

Iacono, M., Levinson, D., El-Geneidy, A., & Wasfi, R. (2015). A Markov Chain Model of Land Use Change. TeMA - Journal of Land Use, Mobility and Environment, 8(3), 263-276. https://doi.org/10.6092/1970-9870/2985.

Li, F., Xie, Z., Clarke, K.C., Li, M., Chen, H., Liang, J., & Chen, Z. (2019). An agent-based procedure with an embedded agent learning model for residential land growth simulation: The case study of Nanjing, China. Cities, 88, 155-165. https://doi.org/10.1016/j.cities.2018.10.008.

Liu, D., Zheng, X., & Wang, H. (2020). Land-use Simulation and Decision-Support system (LandSDS): Seamlessly integrating system dynamics, agent-based model, and cellular automata. Ecological Modelling, 417, 108924. https://doi.org/10.1016/j.ecolmodel.2019.108924.

Macal, C.M., & North, M.J. (2005). Tutorial on agent-based modeling and simulation. Proceedings of the Winter Simulation Conference, 14 pp. https://doi.org/10.1109/WSC.2005.1574234.

Macal, C.M., & North, M.J. (2006). Tutorial on agent-based modeling and simulation part 2: how to model with agents. Proceedings of the 2006 Winter Simulation Conference, 7383. https://doi.org/10.1109/WSC.2006.323040.

Matthews, R.B., Gilbert, N.G., Roach, A., Polhill, J.G., & Gotts, N.M. (2007). Agent-based land-use models: a review of applications. Landscape Ecology, 22(10), 1447-1459. https://doi.org/10.1007/s10980-007-9135-1.

Moeckel, R. (2017). Constraints in household relocation: Modeling land-use/transport interactions that respect time and monetary budgets. Journal of Transport and Land Use, 10(1), 211-228. http://dx.doi.org/10.5198/jtlu.2015.810.

Müller, B., Bohn, F., Dreßler, G., Groeneveld, J., Klassert, C., Martin, R., Schlüter, M., Schulze, J., Weise, H., & Schwarz, N. (2013). Describing human decisions in agent-based models–ODD+ D, an extension of the ODD protocol. Environmental Modelling & Software, 48, 37-48. https://doi.org/10.1016/j.envsoft.2013.06.003.

Murray-Rust, D., Robinson, D.T., Guillem, E., Karali, E., & Rounsevell, M. (2014). An open framework for agent-based modelling of agricultural land use change. Environmental Modelling & Software, 61, 19-38. https://doi.org/10.1016/j.envsoft.2014.06.027.

Pagliara, F., Preston, J., & Simmonds, D. (2010). Residential location choice: models and applications. Berlin: Springer Science & Business Media.

Polhill, J.G., Ge, J., Hare, M.P., Matthews, K.B., Gimona, A., Salt, D., & Yeluripati, J. (2019). Crossing the chasm: a ‘tube-map’for agent-based social simulation of policy scenarios in spatially-distributed systems. GeoInformatica, 23(2), 169-199. https://doi.org/10.1007/s10707-018-00340-z.

Saffarzadeh, M., Mirzahossein, H., & Amiri, E. (2021). Congestion toll pricing and commercial land-use: clients’ and vendors’ perspective. TeMA - Journal of Land Use, Mobility and Environment, 14(1), 33-49. https://doi.org/10.6092/1970-9870/7355.

Schirmer, P.M., van Eggermond, M.A.B., & Axhausen, K.W. (2013). Towards comparability in residential location choice modeling: A review of literature. Arbeitsberichte Verkehrs-Und Raumplanung, 962. 1-20. https://doi.org/10.3929/ethz-b-000441314.

Schirmer, P.M., van Eggermond, M.A.B., & Axhausen, K.W. (2014). The role of location in residential location choice models: a review of literature. Journal of Transport and Land Use, 7(2), 3-21. https://doi.org/10.5198/jtlu.v7i2.740.

Sun, Z., & Müller, D. (2013). A framework for modeling payments for ecosystem services with agent-based models, Bayesian belief networks and opinion dynamics models. Environmental Modelling & Software, 45, 15-28. https://doi.org/10.1016/j.envsoft.2012.06.007.

Valbuena, D., Verburg, P.H., Bregt, A.K., & Ligtenberg, A. (2010). An agent-based approach to model land-use change at a regional scale. Landscape Ecology, 25(2), 185-199. https://doi.org/10.1007/s10980-009-9380-6.

von Thünen, J H. (1966). Von Thünen’s Isolated State: An English Edition Of: Der Isolierte Staat. Edited with an Introduction by Peter Hall. Oxford: Pergamon Press.

Waddell, P. (2002). UrbanSim: Modeling Urban Development for Land Use, Transportation, and Environmental Planning. Journal of the American Planning Association, 68(3), 297-314. https://doi.org/10.1080/01944360208976274.

Waddell, P. (2011). Integrated Land Use and Transportation Planning and Modelling: Addressing Challenges in Research and Practice. Transport Reviews, 31(2), 209-229. https://doi.org/10.1080/01441647.2010.525671.

Zagaria, C., Schulp, C.J. E., Kizos, T., Gounaridis, D., & Verburg, P.H. (2017). Cultural landscapes and behavioral transformations: an agent-based model for the simulation and discussion of alternative landscape futures in East Lesvos, Greece. Land Use Policy, 65, 26-44. https://doi.org/10.1016/j.landusepol.2017.03.022.

Zullo, F., Paolinelli, G., Fiordigigli, V., Fiorini, L., & Romano, B. (2015). Urban development in Tuscany. Land uptake and landscapes changes. TeMA - Journal of Land Use, Mobility and Environment, 8(2), 183-202. https://doi.org/10.6092/1970-9870/2864.

Published
2022-08-31
How to Cite
MirzahosseinH., NoferestiV., & JinX. (2022). Residential development simulation based on learning by agent-based model. TeMA - Journal of Land Use, Mobility and Environment, 15(2), 193-207. https://doi.org/10.6093/1970-9870/8980