Advances in population synthesis: fitting many attributes per agent and fitting to household and person margins simultaneously
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DOI: 10.1007/s11116-011-9367-4
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References listed on IDEAS
- Beckman, Richard J. & Baggerly, Keith A. & McKay, Michael D., 1996. "Creating synthetic baseline populations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(6), pages 415-429, November.
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Cited by:
- Trond Husby & Olga Ivanova & Mark Thissen, 2018. "Simulating the Joint Distribution of Individuals, Households and Dwellings in Small Areas," International Journal of Microsimulation, International Microsimulation Association, vol. 11(2), pages 169-190.
- Nicholas Fournier & Eleni Christofa & Arun Prakash Akkinepally & Carlos Lima Azevedo, 2021. "Integrated population synthesis and workplace assignment using an efficient optimization-based person-household matching method," Transportation, Springer, vol. 48(2), pages 1061-1087, April.
- Qingxu Huang & Dawn C Parker & Tatiana Filatova & Shipeng Sun, 2014. "A Review of Urban Residential Choice Models Using Agent-Based Modeling," Environment and Planning B, , vol. 41(4), pages 661-689, August.
- He, Brian Y. & Zhou, Jinkai & Ma, Ziyi & Chow, Joseph Y.J. & Ozbay, Kaan, 2020. "Evaluation of city-scale built environment policies in New York City with an emerging-mobility-accessible synthetic population," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 444-467.
- Lopes, Mafalda Mendes & Moura, Filipe & Martinez, Luis M., 2014. "A rule-based approach for determining the plausible universe of electric vehicle buyers in the Lisbon Metropolitan Area," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 22-36.
- Andrew Bwambale & Charisma F. Choudhury & Stephane Hess & Md. Shahadat Iqbal, 2021. "Getting the best of both worlds: a framework for combining disaggregate travel survey data and aggregate mobile phone data for trip generation modelling," Transportation, Springer, vol. 48(5), pages 2287-2314, October.
- Lovelace, Robin & Ballas, Dimitris & Watson, Matt, 2014. "A spatial microsimulation approach for the analysis of commuter patterns: from individual to regional levels," Journal of Transport Geography, Elsevier, vol. 34(C), pages 282-296.
- M. Esteban Muñoz H. & Ivan Dochev & Hannes Seller & Irene Peters, 2016. "Constructing a Synthetic City for Estimating Spatially Disaggregated Heat Demand," International Journal of Microsimulation, International Microsimulation Association, vol. 9(3), pages 66-88.
- Farooq, Bilal & Bierlaire, Michel & Hurtubia, Ricardo & Flötteröd, Gunnar, 2013. "Simulation based population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 243-263.
- Saadi, Ismaïl & Mustafa, Ahmed & Teller, Jacques & Farooq, Bilal & Cools, Mario, 2016. "Hidden Markov Model-based population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 1-21.
- Yu Han & Changjie Chen & Zhong-Ren Peng & Pallab Mozumder, 2022. "Evaluating impacts of coastal flooding on the transportation system using an activity-based travel demand model: a case study in Miami-Dade County, FL," Transportation, Springer, vol. 49(1), pages 163-184, February.
- Sun, Lijun & Erath, Alexander & Cai, Ming, 2018. "A hierarchical mixture modeling framework for population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 199-212.
- Ma, Lu & Srinivasan, Sivaramakrishnan, 2016. "An empirical assessment of factors affecting the accuracy of target-year synthetic populations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 247-264.
- Nejad, Mohammad Motalleb & Erdogan, Sevgi & Cirillo, Cinzia, 2021. "A statistical approach to small area synthetic population generation as a basis for carless evacuation planning," Journal of Transport Geography, Elsevier, vol. 90(C).
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Keywords
Iterative proportional fitting; Population synthesis; Microsimulation; Agent-based; Census microdata; Transportation models; Trip forecasting;All these keywords.
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