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Micro-simulation of daily activity-travel patterns for travel demand forecasting


  • Ryuichi Kitamura


  • Cynthia Chen
  • Ram Pendyala
  • Ravi Narayanan


The development and initial validation results of a micro-simulator for the generation of daily activity-travel patterns are presented in this paper. The simulator assumes a sequential history and time-of-day dependent structure. Its components are developed based on a decomposition of a daily activity-travel pattern into components to which certain aspects of observed activity-travel behavior correspond, thus establishing a link between mathematical models and observational data. Each of the model components is relatively simple and is estimated using commonly adopted estimation methods and existing data sets. A computer code has been developed and daily travel patterns have been generated by Monte Carlo simulation. Study results show that individuals' daily travel patterns can be synthesized in a practical manner by micro-simulation. Results of validation analyses suggest that properly representing rigidities in daily schedules is important in simulating daily travel patterns. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Ryuichi Kitamura & Cynthia Chen & Ram Pendyala & Ravi Narayanan, 2000. "Micro-simulation of daily activity-travel patterns for travel demand forecasting," Transportation, Springer, vol. 27(1), pages 25-51, February.
  • Handle: RePEc:kap:transp:v:27:y:2000:i:1:p:25-51
    DOI: 10.1023/A:1005259324588

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    6. Arun Kuppam & Ram Pendyala, 2001. "A structural equations analysis of commuters' activity and travel patterns," Transportation, Springer, vol. 28(1), pages 33-54, February.
    7. Zhang, Xiaoning & Yang, Hai & Huang, Hai-Jun & Zhang, H. Michael, 2005. "Integrated scheduling of daily work activities and morning-evening commutes with bottleneck congestion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(1), pages 41-60, January.
    8. Waddell, Paul & Wang, Liming & Charlton, Billy & Olsen, Aksel, 2010. "Microsimulating parcel-level land use and activity-based travel: Development of a prototype application in San Francisco," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(2), pages 65-84.
    9. Fagiolo, Giorgio & Santoni, Gianluca, 2015. "Human-mobility networks, country income, and labor productivity," Network Science, Cambridge University Press, vol. 3(03), pages 377-407, September.
    10. Christopher Upchurch & Michael Kuby, 2014. "Evaluating light rail sketch planning: actual versus predicted station boardings in Phoenix," Transportation, Springer, vol. 41(1), pages 173-192, January.
    11. Sundo, Marloe B. & Fujii, Satoshi, 2005. "The effects of a compressed working week on commuters' daily activity patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(10), pages 835-848, December.
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    13. Bhat, Chandra & Zhao, Huimin, 2002. "The spatial analysis of activity stop generation," Transportation Research Part B: Methodological, Elsevier, vol. 36(6), pages 557-575, July.
    14. Sean Doherty & Abolfazl Mohammadian, 2011. "The validity of using activity type to structure tour-based scheduling models," Transportation, Springer, vol. 38(1), pages 45-63, January.
    15. Malayath, Manoj & Verma, Ashish, 2013. "Activity based travel demand models as a tool for evaluating sustainable transportation policies," Research in Transportation Economics, Elsevier, vol. 38(1), pages 45-66.
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    17. Sean Doherty, 2006. "Should we abandon activity type analysis? Redefining activities by their salient attributes," Transportation, Springer, vol. 33(6), pages 517-536, November.


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