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Simulation based population synthesis

Listed author(s):
  • Farooq, Bilal
  • Bierlaire, Michel
  • Hurtubia, Ricardo
  • Flötteröd, Gunnar
Registered author(s):

    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.

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    Article provided by Elsevier in its journal Transportation Research Part B: Methodological.

    Volume (Year): 58 (2013)
    Issue (Month): C ()
    Pages: 243-263

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    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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    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. S Openshaw & L Rao, 1995. "Algorithms for Reengineering 1991 Census Geography," Environment and Planning A, , vol. 27(3), pages 425-446, March.
    4. S Openshaw & L Rao, 1995. "Algorithms for reengineering 1991 Census geography," Environment and Planning A, Pion Ltd, London, vol. 27(3), pages 425-446, March.
    5. 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.
    6. 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.
    7. 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, Pion Ltd, London, vol. 30(5), pages 785-816, May.
    8. 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.
    9. 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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