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Modeling and Structuring of Activity Scheduling Choices with Consideration of Intrazonal Tours: A Case Study of Motorcycle-Based Cities

Author

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  • Thuy Linh Hoang

    (UHasselt—Hasselt University, Transportation Research Institute (IMOB), Agoralaan, 3590 Diepenbeek, Belgium
    Faculty of Civil Engineering, University of Transport and Communications, No. 3 Cau Giay Street, Lang Thuong Ward, Dong Da District, Hanoi 100000, Vietnam)

  • Muhammad Adnan

    (UHasselt—Hasselt University, Transportation Research Institute (IMOB), Agoralaan, 3590 Diepenbeek, Belgium)

  • Anh Tuan Vu

    (Vietnamese–German Transport Research Centre, Vietnamese–German University, Le Lai Street, Hoa Phu Ward, Thu Dau Mot City 820000, Binh Duong Province, Vietnam)

  • Nguyen Hoang-Tung

    (Faculty of Construction Management, University of Transport and Communications, No. 3 Cau Giay Street, Lang Thuong Ward, Dong Da District, Hanoi 100000, Vietnam)

  • Bruno Kochan

    (UHasselt—Hasselt University, Transportation Research Institute (IMOB), Agoralaan, 3590 Diepenbeek, Belgium)

  • Tom Bellemans

    (UHasselt—Hasselt University, Transportation Research Institute (IMOB), Agoralaan, 3590 Diepenbeek, Belgium)

Abstract

The travel demand prediction of an activity-based travel demand model (ABM) is based on a hierarchical structure of multiple choices related to an individual’s activity scheduling. This structure has, however, not been investigated for motorcycle-based cities. The coarseness of the traffic analysis zoning system combined with mixed land use results in a large proportion of intrazonal trips, which demands model enhancement in ABMs for these cities. Using large-scale household travel survey data from Ho Chi Minh City, a major motorcycle-based city in Vietnam, this study investigated the hierarchical structure for non-work activity scheduling, with consideration of three dimensions: (1) activity starting time, (2) travel mode, and (3) destination choices at the tour level with attention given to the impacts of intrazonal tours. Multinomial logit and nested logit models were adopted for model development. Results showed that work durations in the schedule strongly affected the scheduling of non-work activities. The estimated logsum parameters showed empirical evidence that hierarchy could be different for different activity types. Our findings also suggested a significant impact of intrazonal tours on the structuring and modeling of activity scheduling choices. The validation result indicated that our proposed models’ predictive capability is acceptable.

Suggested Citation

  • Thuy Linh Hoang & Muhammad Adnan & Anh Tuan Vu & Nguyen Hoang-Tung & Bruno Kochan & Tom Bellemans, 2022. "Modeling and Structuring of Activity Scheduling Choices with Consideration of Intrazonal Tours: A Case Study of Motorcycle-Based Cities," Sustainability, MDPI, vol. 14(10), pages 1-23, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6367-:d:822185
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    References listed on IDEAS

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    1. Soora Rasouli & Harry Timmermans, 2014. "Activity-based models of travel demand: promises, progress and prospects," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 18(1), pages 31-60, March.
    2. Keunhyun Park & Sadegh Sabouri & Torrey Lyons & Guang Tian & Reid Ewing, 2020. "Intrazonal or interzonal? Improving intrazonal travel forecast in a four-step travel demand model," Transportation, Springer, vol. 47(5), pages 2087-2108, October.
    3. Ouassim Manout & Patrick Bonnel, 2019. "The impact of ignoring intrazonal trips in assignment models: a stochastic approach," Transportation, Springer, vol. 46(6), pages 2397-2417, December.
    4. Sanjana Hossain & Md. Sami Hasnine & Khandker Nurul Habib, 2021. "A latent class joint mode and departure time choice model for the Greater Toronto and Hamilton Area," Transportation, Springer, vol. 48(3), pages 1217-1239, June.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    6. Ho, Chinh Q. & Hensher, David A. & Wang, Shangbo, 2020. "Joint estimation of mode and time of day choice accounting for arrival time flexibility, travel time reliability and crowding on public transport," Journal of Transport Geography, Elsevier, vol. 87(C).
    7. Auld, Joshua & Mohammadian, Abolfazl(Kouros), 2012. "Activity planning processes in the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1386-1403.
    8. Daly, Andrew, 1982. "Estimating choice models containing attraction variables," Transportation Research Part B: Methodological, Elsevier, vol. 16(1), pages 5-15, February.
    9. T. Abrahamsson & L. Lundqvist, 1999. "Formulation and Estimation of Combined Network Equilibrium Models with Applications to Stockholm," Transportation Science, INFORMS, vol. 33(1), pages 80-100, February.
    10. Cervero, Robert B., 2013. "Linking urban transport and land use in developing countries," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 6(1), pages 7-24.
    11. Bhatta, Bharat P. & Larsen, Odd I., 2011. "Are intrazonal trips ignorable?," Transport Policy, Elsevier, vol. 18(1), pages 13-22, January.
    12. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D. With contributions by-Name:Adamowicz,Wiktor, 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304, October.
    13. Bhat, Chandra R., 1998. "Analysis of travel mode and departure time choice for urban shopping trips," Transportation Research Part B: Methodological, Elsevier, vol. 32(6), pages 361-371, August.
    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. Susan Handy & Kelly Clifton, 2001. "Local shopping as a strategy for reducing automobile travel," Transportation, Springer, vol. 28(4), pages 317-346, November.
    16. Martin Dijst & Velibor Vidakovic, 2000. "Travel time ratio: the key factor of spatial reach," Transportation, Springer, vol. 27(2), pages 179-199, May.
    17. Dimas B. E. Dharmowijoyo & Yusak O. Susilo & Anders Karlström, 2016. "Day-to-day variability in travellers’ activity-travel patterns in the Jakarta metropolitan area," Transportation, Springer, vol. 43(4), pages 601-621, July.
    18. Jeffrey Newman & Vincent Bernardin, 2010. "Hierarchical ordering of nests in a joint mode and destination choice model," Transportation, Springer, vol. 37(4), pages 677-688, July.
    19. 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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