Simulation based population synthesis

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, the authors 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). The authors also used this methodology to generate the synthetic population for Brussels, Belgium where the data availability was highly limited.

• Record URL:
  • https://doi.org/10.1016/j.trb.2013.09.012
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0191261513001720
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01912615
• Supplemental Notes:
  • Abstract reprinted with permission by Elsevier.
• Authors:
  • Farooq, Bilal
  • Bierlaire, Michel
  • Hurtubia, Ricardo
  • Flötteröd, Gunnar
• Publication Date: 2013-12

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 243-263
• Serial:
  • Transportation Research Part B: Methodological
  • Volume: 58
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0191-2615
  • Serial URL: http://www.sciencedirect.com/science/journal/01912615

Subject/Index Terms

• TRT Terms: City planning; Extrapolation; Intelligent agents; Markov chains; Monte Carlo method; Population; Population forecasting; Simulation; Systems analysis
• Candidate Terms: Data aggregation
• Uncontrolled Terms: Agent based models
• Geographic Terms: Brussels (Belgium); Switzerland
• Subject Areas: Data and Information Technology; Planning and Forecasting; Transportation (General); I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01515223
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 21 2014 3:18PM