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A bridge between travel demand modeling and activity-based travel analysis

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  • Recker, W. W.

Abstract

The focus of this paper is on the demonstration that some rather well-known network-based formulations in operations research, that have heretofore largely gone unnoticed in activity-based travel research, offer a potentially powerful technique for advancing the general development of the activity-based modeling approach. These formulations can provide an analytical framework that unifies the complex interactions among the resource allocation decisions made by households in conducting their daily affairs outside the home, while preserving the utility-maximizing principles presumed to guide such decisions. A mathematical programming formulation is developed and used to identify the similarities and differences between traditional trip-based modeling methodologies and those pertaining to an activity-based approach. It is demonstrated that the two approaches are directly related.

Suggested Citation

  • Recker, W. W., 2001. "A bridge between travel demand modeling and activity-based travel analysis," Transportation Research Part B: Methodological, Elsevier, vol. 35(5), pages 481-506, June.
    • Handle: RePEc:eee:transb:v:35:y:2001:i:5:p:481-506
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    1. Recker, W. W. & Chen, C. & McNally, M. G., 2000. "Measuring the impact of efficient household travel decisions on potential travel time savings and accessibility gains," University of California Transportation Center, Working Papers qt3kc5j7dc, University of California Transportation Center.
    2. Recker, W. W., 1995. "The household activity pattern problem: General formulation and solution," Transportation Research Part B: Methodological, Elsevier, vol. 29(1), pages 61-77, February.
    3. Recker, W. W. & Chen, C. & McNally, M. G., 2000. "Measuring the impact of efficient household travel decisions on potential travel time savings and accessibility gains," University of California Transportation Center, Working Papers qt1qq2t12b, University of California Transportation Center.
    4. Bhat, Chandra R. & Koppelman, Frank S., 1993. "A conceptual framework of individual activity program generation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 27(6), pages 433-446, November.
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    Cited by:

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    2. Mahdieh Allahviranloo & Thomas Bonet & Jérémy Diez, 2021. "Introducing shared life experience metric in urban planning," Transportation, Springer, vol. 48(3), pages 1125-1148, June.
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    4. Jee Eun Kang & Will Recker, 2015. "Strategic Hydrogen Refueling Station Locations with Scheduling and Routing Considerations of Individual Vehicles," Transportation Science, INFORMS, vol. 49(4), pages 767-783, November.
    5. Recker, Will W & Duan, J. & Wang, H., 2008. "Development of an estimation procedure for an activity-based travel demand model," University of California Transportation Center, Working Papers qt0rz778v6, University of California Transportation Center.
    6. Kang, Jee Eun & Chow, Joseph Y.J. & Recker, Will W., 2013. "On activity-based network design problems," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 398-418.
    7. Chow, Joseph Y.J. & Recker, Will W., 2012. "Inverse optimization with endogenous arrival time constraints to calibrate the household activity pattern problem," Transportation Research Part B: Methodological, Elsevier, vol. 46(3), pages 463-479.
    8. Gan, Li Ping & Recker, Will, 2008. "A mathematical programming formulation of the household activity rescheduling problem," Transportation Research Part B: Methodological, Elsevier, vol. 42(6), pages 571-606, July.
    9. He, Fang & Yin, Yafeng & Lawphongpanich, Siriphong, 2014. "Network equilibrium models with battery electric vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 306-319.
    10. Tong, Lu & Zhou, Xuesong & Miller, Harvey J., 2015. "Transportation network design for maximizing space–time accessibility," Transportation Research Part B: Methodological, Elsevier, vol. 81(P2), pages 555-576.
    11. Abdul Rawoof Pinjari & Chandra R. Bhat, 2011. "Activity-based Travel Demand Analysis," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 10, Edward Elgar Publishing.
    12. Oskar Blom Västberg & Anders Karlström & Daniel Jonsson & Marcus Sundberg, 2020. "A Dynamic Discrete Choice Activity-Based Travel Demand Model," Transportation Science, INFORMS, vol. 54(1), pages 21-41, January.
    13. Xie, Chi & Wang, Tong-Gen & Pu, Xiaoting & Karoonsoontawong, Ampol, 2017. "Path-constrained traffic assignment: Modeling and computing network impacts of stochastic range anxiety," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 136-157.
    14. Blom Västberg, Oskar & Karlström, Anders & Jonsson, Daniel & Sundberg, Marcus, 2016. "Including time in a travel demand model using dynamic discrete choice," MPRA Paper 75336, University Library of Munich, Germany, revised 11 Nov 2016.
    15. Liu, Peng & Liao, Feixiong & Huang, Hai-Jun & Timmermans, Harry, 2015. "Dynamic activity-travel assignment in multi-state supernetworks," Transportation Research Part B: Methodological, Elsevier, vol. 81(P3), pages 656-671.
    16. Maruyama, Takuya & Sumalee, Agachai, 2007. "Efficiency and equity comparison of cordon- and area-based road pricing schemes using a trip-chain equilibrium model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(7), pages 655-671, August.
    17. Ballis, Haris & Dimitriou, Loukas, 2020. "Revealing personal activities schedules from synthesizing multi-period origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 224-258.

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