Abstract
During the past several decades, many new towns have emerged in suburbs along new railway lines in Japan . Numerous problems in those towns are emerging as their population age. This study aimed to build a micro-simulation model of households to estimate residents’ assessments of quality of life in a suburban new town of a metropolis. Approximately 1500 households were sampled to collect survey data. Using census data and the survey data, base-year household microdata were estimated using the agent-based synthesis method. The survey data provided information on household histories after taking up residence in the present house. A microsimulation model was built using the household history data and simulations were performed to predict household transitions in the study area in five-year increments between 2015 and 2045.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abraham, J. E., Garry, G. R., & Hunt, J. D. (2005). The sacramento Pecas model. In Transportation Research Board Annual Meeting.
Chengxiang, Z., Chunfu, S., Jian, G., Chunjiao, D., & Hui, Z. (2016). Agent-based joint model of residential location choice and real estate price for land use and transport model. Computers, Environment and Urban Systems, 57, 93–105.
Ettema, D., de Jong, K., Timmermans, H., & Bakema, A. (2007). PUMA: Multi-agent modelling of urban systems. In Modelling land-use change (pp. 237–258) Springer.
Hansen, J. Z., & Stephensen, P. (2013). Modeling household formation and housing demand in Denmark using the dynamic microsimulation model SMILE. In DREAM Conference Paper, 4th General Conference of the International Microsimulation Association.
Hunt, J. D., & Abraham, J. E. (2009). Pecas—for spatial economic modelling: Theoretical formulation, system documentation technical memorandum 1 working draft.
Lee, D. H., & Fu, Y. F. (2011). Cross-entropy optimization model for population synthesis used in activity-based microsimulation models. In TRB 90th Annual Meeting Compendium of Papers.
Miyamoto, K., & Sugiki, N. (2009). An estimation method of household micro-data for the base year in land-use micro simulation. In Proceedings of CUPUM’09.
Miyamoto, K., Sugiki, N., Otani, N., & Vichiensan, V. (2010a). An agent based estimation method of household micro-data for the base year in land-use microsimulation. In TRB 89th Annual Meeting Compendium of Papers.
Miyamoto, K., Sugiki, N., Otani, N., & Vichiensan, V. (2010b). An agent based estimation method of household micro-data including housing information for the base year in land-use microsimulation. In Selected Proceedings of 12th WCTR.
Miyamoto, K., Sugiki, N., Otani, N., & Vichiensan, V. (2013). Qualitative and quantitative comparisons of agent-based and cell-based synthesis estimation methods of base-year data for land-use microsimulations. In S. Geertman et al. (Eds.), Planning Support Systems for Sustainable Urban Development, Lecture Notes in Geoinformation and Cartography (pp. 91–106).
Moeckel, R., Spiekermann, K., & Wegener, M. (2003). Creating a synthetic population. In: Proceedings of 8th International Conference on Computers in Urban Planning and Urban Management.
MĂĽller, K., & Axhausen, K. W. (2011). Population synthesis for microsimulation: State of the art. In TRB 90th Annual Meeting Compendium of Papers.
Otani, N., Fukuoka, Y., Sugiki, N., & Miyamoto, K. (2015). Tailor-made selection of policy measures for households based on the detailed attributes by segmentation approach with decision tree analysis. In: Proceedings of CUPUM’15.
Otani, N., Sugiki, N., & Miyamoto, K. (2010). Goodness-of-fit evaluation method between two sets of household micro-data for land-use microsimulation model. In: Selected Proceedings of 12th WCTR.
Otani, N., Sugiki, N., & Miyamoto, K. (2011). Goodness-of-fit evaluation method for agent-based household micro-data sets composed of generalized attributes. Transportation Research Record, 2254, 97–103.
Pritchard, D. R., & Miller, E. J. (2009). Advances in agent population synthesis and application in an integrated land use/transportation model. In TRB 88th Annual Meeting Compendium of Papers.
Rogers, S. M., et al. (2014). A geospatial dynamic microsimulation model for household population projections. International Journal of Microsimulation, 7(2), 119–146.
Salvini, P., & Miller, E. J. (2005). ILUTE: An operational prototype of a comprehensive microsimulation model of urban systems. Networks and Spatial Economics, 5(2), 217–234.
Strauch, D., Moeckel, R., Wegener, M., Gräfe, J., Mühlhans, H., Rindsfüser, G., et al. (2005). Linking transport and land use planning: The microscopic dynamic simulation model ILUMASS. In Geodynamics (pp. 295–311).
Sugiki, N., Muranaka, T., Otani, N., & Miyamoto, K. (2015). Agent-based estimation of household micro-data with detailed attributes for a real city. In Proceedings of CUPUM’15.
Sugiki, N., Vichiensan, V., Otani, N., & Miyamoto, K. (2012). Agent-based household micro-datasets: An estimation method composed of generalized attributes with probabilistic distributions from sample data and available control totals by attribute. Asian Transport Studies, 2(1), 3–18.
Waddell, P. (2002). UrbanSim: Modeling urban development for land use, transportation, and environmental planning. Journal of the American Planning Association, 68(3), 297–314.
Acknowledgements
The survey fielded in this study was financially supported by Tokyu Research Institute, Inc. The authors deeply appreciate Ms. Ryoko Okumura, TRI, for her kind help with conducting the survey.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Sugiki, N., Miyamoto, K., Kashimura, A., Otani, N. (2017). Household Micro-simulation Model Considering Observed Family Histories in a Suburban New Town. In: Geertman, S., Allan, A., Pettit, C., Stillwell, J. (eds) Planning Support Science for Smarter Urban Futures. CUPUM 2017. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-57819-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-57819-4_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57818-7
Online ISBN: 978-3-319-57819-4
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)