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A Methodological Approach for Enriching Activity–Travel Schedules with In-Home Activities

Author

Listed:
  • Feng Liu

    (Transportation Research Institute (IMOB), Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium)

  • Tom Bellemans

    (Transportation Research Institute (IMOB), Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium)

  • Davy Janssens

    (Transportation Research Institute (IMOB), Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium)

  • Geert Wets

    (Transportation Research Institute (IMOB), Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium)

  • Muhammad Adnan

    (Transportation Research Institute (IMOB), Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium)

Abstract

In-home activities are inevitably important parts of individuals’ daily schedules, as people spend more time working and doing various other activities (e.g., online shopping or banking) at home. However, conventional activity-based travel demand models (ABMs) only consider travel and travel-related out-of-home activities, ignoring the interaction between in-home and out-of-home activities. To fill in this gap and increase the understanding of what people do at home and how in-home and out-of-home activities affect each other, a new method is proposed in this study. The approach predicts the types and durations of in-home activities of daily schedules generated by ABMs. In model building, statistical methods such as multinomial logit, log-linear regression, and activity sequential information are utilized, while in calibration, the Simultaneous Perturbation Stochastic Approximation (SPSA) method is employed. The proposed method was tested using training data and by applying the approach to the schedules of 6.3 million people in the Flemish region of Belgium generated by a representative ABM. Based on the statistical methods, the mean absolute errors were 0.36 and 0.21 for predicting the number and sum of the durations of in-home activities (over all types) per schedule, respectively. The prediction obtained a 10% and 8% improvement using sequential information. After calibration, an additional 60% and 68% were gained regarding activity participation rates and time spent per day. The experimental results demonstrate the potential and practical ability of the proposed method for the incorporation of in-home activities in activity–travel schedules, contributing towards the extension of ABMs to a wide range of applications that are associated with individuals’ in-home activities (e.g., the appropriate evaluation of energy consumption and carbon emission estimation as well as sustainable policy designs for telecommuting).

Suggested Citation

  • Feng Liu & Tom Bellemans & Davy Janssens & Geert Wets & Muhammad Adnan, 2024. "A Methodological Approach for Enriching Activity–Travel Schedules with In-Home Activities," Sustainability, MDPI, vol. 16(22), pages 1-24, November.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:10086-:d:1524392
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    References listed on IDEAS

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    1. Deborah Salon & Laura Mirtich & Matthew Wigginton Bhagat-Conway & Adam Costello & Ehsan Rahimi & Abolfazl & Mohammadian & Rishabh Singh Chauhan & Sybil Derrible & Denise da Silva Baker & Ram M. Pendya, 2022. "The COVID-19 Pandemic and the Future of Telecommuting in the United States," Papers 2210.00067, arXiv.org.
    2. Ramin Shabanpour & Nima Golshani & Mehran Fasihozaman Langerudi & Abolfazl (Kouros) Mohammadian, 2018. "Planning in-home activities in the ADAPTS activity-based model: a joint model of activity type and duration," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 22(2), pages 236-254, April.
    3. Bhat, Chandra R. & Gossen, Rachel, 2004. "A mixed multinomial logit model analysis of weekend recreational episode type choice," Transportation Research Part B: Methodological, Elsevier, vol. 38(9), pages 767-787, November.
    4. Arentze, Theo A. & Timmermans, Harry J. P., 2004. "A learning-based transportation oriented simulation system," Transportation Research Part B: Methodological, Elsevier, vol. 38(7), pages 613-633, August.
    5. 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.
    6. Sean Doherty, 2006. "Should we abandon activity type analysis? Redefining activities by their salient attributes," Transportation, Springer, vol. 33(6), pages 517-536, November.
    7. Matthew A Cole & Ceren Ozgen & Eric Strobl, 2020. "Air Pollution Exposure and Covid-19," Discussion Papers 20-13, Department of Economics, University of Birmingham.
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