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Simulation based population synthesis

Author

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  • Farooq, Bilal
  • Bierlaire, Michel
  • Hurtubia, Ricardo
  • Flötteröd, Gunnar

Abstract

Microsimulation of urban systems evolution requires synthetic population as a key input. Currently, the focus is on treating synthesis as a fitting problem and thus various techniques have been developed, including Iterative Proportional Fitting (IPF) and Combinatorial Optimization based techniques. The key shortcomings of these procedures include: (a) fitting of one contingency table, while there may be other solutions matching the available data (b) due to cloning rather than true synthesis of the population, losing the heterogeneity that may not have been captured in the microdata (c) over reliance on the accuracy of the data to determine the cloning weights (d) poor scalability with respect to the increase in number of attributes of the synthesized agents. In order to overcome these shortcomings, we propose a Markov Chain Monte Carlo (MCMC) simulation based approach. Partial views of the joint distribution of agent’s attributes that are available from various data sources can be used to simulate draws from the original distribution. The real population from Swiss census is used to compare the performance of simulation based synthesis with the standard IPF. The standard root mean square error statistics indicated that even the worst case simulation based synthesis (SRMSE=0.35) outperformed the best case IPF synthesis (SRMSE=0.64). We also used this methodology to generate the synthetic population for Brussels, Belgium where the data availability was highly limited.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transb:v:58:y:2013:i:c:p:243-263
    DOI: 10.1016/j.trb.2013.09.012
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    References listed on IDEAS

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    1. Arentze, Theo A. & Timmermans, Harry J. P., 2004. "A learning-based transportation oriented simulation system," Transportation Research Part B: Methodological, Elsevier, vol. 38(7), pages 613-633, August.
    2. David Pritchard & Eric Miller, 2012. "Advances in population synthesis: fitting many attributes per agent and fitting to household and person margins simultaneously," Transportation, Springer, vol. 39(3), pages 685-704, May.
    3. Brown, Morton B. & Fuchs, Camil, 1983. "On maximum likelihood estimation in sparse contingency tables," Computational Statistics & Data Analysis, Elsevier, vol. 1(1), pages 3-15, March.
    4. 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.
    5. S Openshaw & L Rao, 1995. "Algorithms for Reengineering 1991 Census Geography," Environment and Planning A, , vol. 27(3), pages 425-446, March.
    6. P Williamson & M Birkin & P H Rees, 1998. "The Estimation of Population Microdata by Using Data from Small Area Statistics and Samples of Anonymised Records," Environment and Planning A, , vol. 30(5), pages 785-816, May.
    7. Chen, Shyh-Huei & Ip, Edward H. & Wang, Yuchung J., 2011. "Gibbs ensembles for nearly compatible and incompatible conditional models," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1760-1769, April.
    8. Johan Barthelemy & Philippe L. Toint, 2013. "Synthetic Population Generation Without a Sample," Transportation Science, INFORMS, vol. 47(2), pages 266-279, May.
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    Cited by:

    1. Melvin Wong & Bilal Farooq, 2019. "Information processing constraints in travel behaviour modelling: A generative learning approach," Papers 1907.07036, arXiv.org, revised Jul 2019.
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    9. 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.
    10. Jian Liu & Xiaosu Ma & Yi Zhu & Jing Li & Zong He & Sheng Ye, 2021. "Generating and Visualizing Spatially Disaggregated Synthetic Population Using a Web-Based Geospatial Service," Sustainability, MDPI, vol. 13(3), pages 1-16, February.
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    13. Assaf, A. George & Gillen, David & Tsionas, Efthymios G., 2014. "Understanding relative efficiency among airports: A general dynamic model for distinguishing technical and allocative efficiency," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 18-34.
    14. 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.
    15. Hadrien Salat & Dustin Carlino & Fernando Benitez-Paez & Anna Zanchetta & Daniel Arribas-Bel & Mark Birkin, 2023. "Synthetic population Catalyst: A micro-simulated population of England with circadian activities," Environment and Planning B, , vol. 50(8), pages 2309-2316, October.
    16. 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.
    17. ANTONI Jean-Philippe & VUIDEL Gilles & KLEIN Olivier, 2017. "Generating a located synthetic population of individuals, households, and dwellings," LISER Working Paper Series 2017-07, Luxembourg Institute of Socio-Economic Research (LISER).
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    20. 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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