IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i23p16338-d1288671.html
   My bibliography  Save this article

Influence of Activity-Travel Participation, Travel Mode Choice, and Multitasking Activities on Subjective Well-Being Using R

Author

Listed:
  • Mujahid Ali

    (Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8 Street, 40-019 Katowice, Poland)

  • Elżbieta Macioszek

    (Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8 Street, 40-019 Katowice, Poland)

  • Dimas Bayu Endrayana Dharmowijoyo

    (National Delivery System, Melbourne, VIC 3000, Australia
    School of Architecture, Planning and Policy Development, Institute Teknologi Bandung, Kota Bandung 40132, Indonesia
    Faculty of Engineering, Universitas Janabadra, Yogyakarta 55231, Indonesia
    Department of Civil Engineering, Universitas Muhammadiyah Yogyakarta, Yogyakarta 55183, Indonesia)

Abstract

Multitasking activities (MTA) are typically thought to enhance general subjective well-being (SWB). However, performing MTA while operating a private vehicle is frequently challenging. Public transportation (PT) can provide an additional option to engage in more pleasurable activities while traveling. Several studies have been conducted on the engagement of different activities while using different transport modes and its influence on physical, social, and mental health. Moreover, numerous studies have been carried out on motorized transport and MTA that resulted in accidents, fatalities, injuries, and even disasters. In addition, several experts studied the influence of health parameters on daily activities. There have, however, only been a few studies on MTA while on PT and its influence on SWB. Therefore, the current study aims to investigate the travel mode choice, the performance of onboard MTA, and its influence on overall SWB. Using random sampling techniques, data on 732 individuals and 191 households—representing 0.029% of the overall population of Bandung, Indonesia—were gathered. Two different models were developed between independent, intermediate, and dependent variables. Statistical Package for Social Sciences (SPSS) was used for descriptive statistics, whereas R software was used for the multilevel linear regression analysis. The model estimation results show that MTA mediates the relationship among socio-demographic and economic variables, built environment, trip and travel parameters, and SWB. A unit increase in PT lines can provide a 1.5% greater opportunity to participate in more onboard MTA; however, a unit increase in MTA can enhance SWB by 5.1% where both the models show satisfactory coefficient of determination (R 2 ). A unit increase in motorized transport caused a 12.9% negative association with MTA and 10.9% with SWB. A unit increase in NMT and PT are 21.7% and 10.2% positively associated with MTA and 19.2% and 13.1% positively associated with SWB. The current study helps policymakers to develop a policy based on PT which allows the individuals to engage in more MTA that enhance SWB and target sustainable transportation system.

Suggested Citation

  • Mujahid Ali & Elżbieta Macioszek & Dimas Bayu Endrayana Dharmowijoyo, 2023. "Influence of Activity-Travel Participation, Travel Mode Choice, and Multitasking Activities on Subjective Well-Being Using R," Sustainability, MDPI, vol. 15(23), pages 1-22, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16338-:d:1288671
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/23/16338/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/23/16338/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Olga Bachmann & Carola Grunschel & Stefan Fries, 2019. "Multitasking and Feeling Good? Autonomy of Additional Activities Predicts Affect," Journal of Happiness Studies, Springer, vol. 20(3), pages 899-918, March.
    2. Yusak Susilo & Kay Axhausen, 2014. "Repetitions in individual daily activity–travel–location patterns: a study using the Herfindahl–Hirschman Index," Transportation, Springer, vol. 41(5), pages 995-1011, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Heinen, Eva & Chatterjee, Kiron, 2015. "The same mode again? An exploration of mode choice variability in Great Britain using the National Travel Survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 266-282.
    2. Chunguang Liu & Xinyu Zuo & Xiaoning Gu & Mengru Shao & Chao Chen, 2023. "Activity Duration under the COVID-19 Pandemic: A Comparative Analysis among Different Urbanized Areas Using a Hazard-Based Duration Model," Sustainability, MDPI, vol. 15(12), pages 1-28, June.
    3. Scheiner, Joachim & Chatterjee, Kiron & Heinen, Eva, 2016. "Key events and multimodality: A life course approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 148-165.
    4. Xia Zhao & Mengying Cui & David Levinson, 2023. "Exploring temporal variability in travel patterns on public transit using big smart card data," Environment and Planning B, , vol. 50(1), pages 198-217, January.
    5. Klinger, Thomas, 2017. "Moving from monomodality to multimodality? Changes in mode choice of new residents," Transportation Research Part A: Policy and Practice, Elsevier, vol. 104(C), pages 221-237.
    6. Liu, Chengxi & Susilo, Yusak O. & Dharmowijoyo, Dimas B.E., 2018. "Investigating intra-household interactions between individuals' time and space constraints," Journal of Transport Geography, Elsevier, vol. 73(C), pages 108-119.
    7. Eisenmann, Christine & Buehler, Ralph, 2018. "Are cars used differently in Germany than in California? Findings from annual car-use profiles," Journal of Transport Geography, Elsevier, vol. 69(C), pages 171-180.
    8. Dharmowijoyo, Dimas B.E. & Susilo, Yusak O. & Karlström, Anders, 2017. "Analysing the complexity of day-to-day individual activity-travel patterns using a multidimensional sequence alignment model: A case study in the Bandung Metropolitan Area, Indonesia," Journal of Transport Geography, Elsevier, vol. 64(C), pages 1-12.
    9. Rui Qu & Zaewoong Rhee & Seung-Jong Bae & Sang-Hyun Lee, 2022. "Analysis of Industrial Diversification Level of Economic Development in Rural Areas Using Herfindahl Index and Two-Step Clustering," Sustainability, MDPI, vol. 14(11), pages 1-19, May.
    10. 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.
    11. Oscar Egu & Patrick Bonnel, 2020. "Investigating day-to-day variability of transit usage on a multimonth scale with smart card data. A case study in Lyon," Post-Print halshs-03148937, HAL.
    12. WooKeol Cho & Jinhee Kim & Jin-Hyuk Chung, 2023. "A Need-Based Approach for Modeling Recurrent Discretionary Activity Participation Patterns for Travel Demand Analysis," Sustainability, MDPI, vol. 15(21), pages 1-20, October.
    13. Xiu Chen & Fuhai Hong & Xiaojian Zhao, 2016. "Concentration and Unpredictability of Forecasts in Artificial Investment Games," Economic Growth Centre Working Paper Series 1608, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    14. Pizzol, Bruna & Strambi, Orlando & Giannotti, Mariana & Arbex, Renato Oliveira & Alves, Bianca Bianchi, 2021. "Activity behavior of residents of Paraisópolis slum: Analysis of multiday activity patterns using data collected with smartphones," Journal of choice modelling, Elsevier, vol. 39(C).
    15. Klepikov, Vladimir Pavlovich & Klepikov, Vladimir Vladimirovich, 2020. "Quantitative approach to estimating crude oil supply in Southern Europe," Resources Policy, Elsevier, vol. 69(C).
    16. Xiu Chen & Fuhai Hong & Xiaojian Zhao, 2020. "Concentration and variability of forecasts in artificial investment games: an online experiment on WeChat," Experimental Economics, Springer;Economic Science Association, vol. 23(3), pages 815-847, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16338-:d:1288671. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.