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An activity-based microsimulation analysis of transportation control measures

Author

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  • Pendyala, Ram M.
  • Kitamura, Ryuichi
  • Chen, Cynthia
  • Pas, Eric I.

Abstract

This paper describes the development and application of an activity-based microsimulation model system capable of simulating changes in individual travel patterns in response to a transportation control measure. A unique activity-based time use survey was conducted to obtain information on people's activity and travel patterns and their likely behavioral adjustment in response to various transportation control measures. This paper describes the survey and the use of the ensuing data set in estimating various components of the simulator, called AMOS. The first application in the Washington DC area demonstrated the capabilities of AMOS as a transportation policy analysis tool. Sample results from the Washington DC demonstration are presented.

Suggested Citation

  • Pendyala, Ram M. & Kitamura, Ryuichi & Chen, Cynthia & Pas, Eric I., 1997. "An activity-based microsimulation analysis of transportation control measures," Transport Policy, Elsevier, vol. 4(3), pages 183-192, July.
    • Handle: RePEc:eee:trapol:v:4:y:1997:i:3:p:183-192
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    Cited by:

    1. Kuldeep Kavta & Arkopal K. Goswami, 2021. "A methodological framework for a priori selection of travel demand management package using fuzzy MCDM methods," Transportation, Springer, vol. 48(6), pages 3059-3084, December.
    2. Michael Iacono & David Levinson & Ahmed El-Geneidy, 2007. "Models of Transportation and Land Use Change: A Guide to the Territory," Working Papers 200805, University of Minnesota: Nexus Research Group.
    3. Allahviranloo, Mahdieh & Recker, Will, 2013. "Daily activity pattern recognition by using support vector machines with multiple classes," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 16-43.
    4. Habibian, Meeghat & Kermanshah, Mohammad, 2013. "Coping with congestion: Understanding the role of simultaneous transportation demand management policies on commuters," Transport Policy, Elsevier, vol. 30(C), pages 229-237.
    5. Gärling, Tommy & Gärling, Anita & Johansson, Anders, 2000. "Household choices of car-use reduction measures," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(5), pages 309-320, June.
    6. Daniel Shefer, 2014. "Sustainable Transportation and Urban Development," ERSA conference papers ersa14p306, European Regional Science Association.
    7. Arentze, Theo & Hofman, Frank & Timmermans, Harry, 2004. "Predicting multi-faceted activity-travel adjustment strategies in response to possible congestion pricing scenarios using an Internet-based stated adaptation experiment," Transport Policy, Elsevier, vol. 11(1), pages 31-41, January.
    8. Staffan Algers & Jonas Eliasson & Lars-Göran Mattsson, 2001. "Activity-based model development to support transport planning in the Stockholm region," ERSA conference papers ersa01p19, European Regional Science Association.
    9. Yoram Shiftan & John Suhrbier, 2002. "The analysis of travel and emission impacts of travel demand management strategies using activity-based models," Transportation, Springer, vol. 29(2), pages 145-168, May.
    10. Gärling, Tommy & Eek, Daniel & Loukopoulos, Peter & Fujii, Satoshi & Johansson-Stenman, Olof & Kitamura, Ryuichi & Pendyala, Ram & Vilhelmson, Bertil, 2002. "A conceptual analysis of the impact of travel demand management on private car use," Transport Policy, Elsevier, vol. 9(1), pages 59-70, January.
    11. Mohammad Abu Afrahim Bhuiyan & Shakil Mohammad Rifaat & Richard Tay & Alex De Barros, 2020. "Influence of Community Design and Sociodemographic Characteristics on Teleworking," Sustainability, MDPI, vol. 12(14), pages 1-10, July.
    12. Staffan Algers & Jonas Eliasson & Lars-Göran Mattsson, 2005. "Is it time to use activity-based urban transport models? A discussion of planning needs and modelling possibilities," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 39(4), pages 767-789, December.
    13. Chen, Cynthia & Gong, Hongmian & Lawson, Catherine & Bialostozky, Evan, 2010. "Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 830-840, December.
    14. Yu Ding & Huapu Lu & Lei Zhang, 2016. "An analysis of activity time use on vehicle usage rationed days," Transportation, Springer, vol. 43(1), pages 145-158, January.
    15. Cathal O'Donoghue & Karyn Morrissey & John Lennon, 2014. "Spatial Microsimulation Modelling: a Review of Applications and Methodological Choices," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 26-75.
    16. Yu Ding & Huapu Lu & Lei Zhang, 2016. "An analysis of activity time use on vehicle usage rationed days," Transportation, Springer, vol. 43(1), pages 145-158, January.
    17. Hasnine, Md Sami & Habib, Khandker Nurul, 2018. "What about the dynamics in daily travel mode choices? A dynamic discrete choice approach for tour-based mode choice modelling," Transport Policy, Elsevier, vol. 71(C), pages 70-80.
    18. 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.
    19. Burris, Mark W. & Pendyala, Ram M., 2002. "Discrete choice models of traveler participation in differential time of day pricing programs," Transport Policy, Elsevier, vol. 9(3), pages 241-251, July.

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