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The influence of travel mode choice on subjective well-being - a case study

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Języki publikacji
EN
Abstrakty
EN
The correlation between mobility and subjective well-being (SWB) has received much attention lately. Previous researchers have studied the effect of health parameters or SWB on transport mode; however, there is a lack of study on the influence of travel mode choice (TMC) for daily activities on SWB. Besides, the prediction of TMC is critical for transport planning. Therefore, the current study aims to study the TMC and its influence on overall SWB. Data from 732 individuals and 191 households are collected using random sampling techniques, which represents 0.029% of the total population. 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 a significant correlation among the variables (p < 0.05, R2 > 0.20). Besides, those who are exposed to public transport and tend to use non- motorized transport modes engage in more physical activities than those who use a private vehicle, which has a negative impact on SWB. The outcome of current research helps policymakers build policies to achieve a sustainable transportation system.
Czasopismo
Rocznik
Strony
5--17
Opis fizyczny
Bibliogr. 29 poz.
Twórcy
autor
  • Silesian University of Technology, Faculty of Transport and Aviation Engineering; Krasińskiego 8, 40-019 Katowice, Poland
  • Silesian University of Technology, Faculty of Transport and Aviation Engineering; Krasińskiego 8, 40-019 Katowice, Poland
Bibliografia
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  • 3. Handy, S. & Weston, L. & Mokhtarian, P.L. Driving by choice or necessity? Transportation Research Part A: Policy and Practice. 2005. Vol. 39(2-3). P. 183-203.
  • 4. Macioszek, E. & Jurdana, I. Bicycle traffic in the cities. Scientific Journal of Silesian University of Technology. Series Transport. 2022. Vol. 117. P. 115-127.
  • 5. Mouratidis, K. Urban planning and quality of life: A review of pathways linking the built environment to subjective well-being. Cities. 2021. Vol. 115. P. 103229. DOI: 10.1016/j.cities.2021.103229.
  • 6. Fauzi, N.F.M. & Dharmowijoyo, D.B. Activity-travel participation, multitasking in travel and daily well-being. In The 2nd Conference for Civil Engineering Research Networks (ConCERN-2 2018), MATEC Web of Conferences: EDP Sciences. 2019. DOI: 10.1051/matecconf/201927003014.
  • 7. Macioszek, E. Analysis of the Rail Cargo Transport Volume in Poland in 2010-2021. Journal of Silesian University of Technology. Series Transport. 2023. Vol. 119. P. 125-140.
  • 8. Singleton, P.A.Walking (and cycling) to well-being: Modal and other determinants of subjective well-being during the commute. Travel Behaviour and Society. 2019. Vol. 16. P. 249-261. DOI: 10.1016/j.tbs.2018.02.005.
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  • 10. Ohta, M. & Mizoue, T. & Mishima, N. & et al., Effect of the Physical Activities in Leisure Time and Commuting to Work on Mental Health. Journal of Occupational Health. 2007. 49(1). P. 46-52. DOI: 10.1539/joh.49.46.
  • 11. Martín-Baos, J.Á. & López-Gómez, J.A. & Rodriguez-Benitez, L. & et al., A prediction and behavioural analysis of machine learning methods for modelling travel mode choice. Transportation Research Part C: Emerging Technologies. 2023. Vol. 156. 44 p. DOI: 10.1016/j.trc.2023.104318.
  • 12. Koushik, A.N.P. & Manoj, M. & Nezamuddin, N. & et al. Testing and enhancing spatial transferability of artificial neural networks based travel behavior models. Transportation Letters. 2023. Vol. 15(9). P. 1083-1094. DOI: 10.1080/19427867.2022.2130150.
  • 13. Xu, Z. & Aghaabbasi, M. & Ali, M. & et al. Targeting Sustainable Transportation Development: The Support Vector Machine and the Bayesian Optimization Algorithm for Classifying Household Vehicle Ownership. Sustainability. 2022. Vol. 14(17). No. 11094.
  • 14. De Vos, J. & Schwanen, T. & Van Acker, V. & et al., Travel and Subjective Well-Being: A Focus on Findings. Methods and Future Research Needs. Transport Reviews. 2013. Vol. 33(4). P. 421-442. DOI: 10.1080/01441647.2013.815665.
  • 15. Assi, K.J. & Shafiullah, M. & Nahiduzzaman, K.M. & et al. Travel-to-school mode choice modelling employing artificial intelligence techniques: A comparative study. Sustainability (Switzerland). 2019. Vol. 11(16). DOI: 10.3390/su11164484.
  • 16. McMillan, T.E. The relative influence of urban form on a child’s travel mode to school. Transportation Research Part A: Policy and Practice. 2007. Vol. 41(1). P. 69-79 2007/01/01/ DOI: 10.1016/j.tra.2006.05.011.
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  • 18. Hislop, D. Driving, Communicating and Working: Understanding the Work-related Communication Behaviours of Business Travellers on Work-related Car Journeys. Mobilities. 2013. 8(2). P. 220-237. DOI: 10.1080/17450101.2012.655972.
  • 19. Susilo, Y.O. & Dijst, M. How Far is Too Far?: Travel Time Ratios for Activity Participation in the Netherlands. Transportation Research Record. 2009. Vol. 2134(1). P. 89-98. DOI: 10.3141/2134-11.
  • 20. Susilo, Y.O. & Axhausen, K.W. Repetitions in individual daily activity-travel-location patterns: a study using the Herfindahl-Hirschman Index. Transportation. 2014. Vol. 41(5). P. 995-1011. DOI: 10.1007/s11116-014-9519-4.
  • 21. Madhuwanthi, R. & Marasinghe, A. & RPC, J. & et al. Factors influencing to travel behavior on transport mode choice-A Case of Colombo Metropolitan Area in Sri Lanka. International Journal of Affective Engineering. 2016. Vol. 15(2). P. 63-72.
  • 22. Vaitsis, P. & Basbas, S. & Nikiforiadis, A. How eudaimonic aspect of subjective well-being affect transport mode choice? The case of Thessaloniki, Greece. Social Sciences. 2019. Vol. 8(1). 9 p.
  • 23. Macioszek, E. & Granà, A. & Krawiec, S. Identification of factors increasing the risk of pedestrian death in road accidents involving a pedestrian with a motor vehicle. Archives of Transport. 2023. Vol. 65(1). P. 7-25.
  • 24. Singleton, P.A. Chapter 14 - Exploring the positive utility of travel and mode choice: Subjective well-being and travel-based multitasking during the commute, ed. K.G. Goulias and A.W. Davis. 2020: Elsevier: Mapping the Travel Behavior Genome. P. 259-277. DOI: 10.1016/B978-0-12-817340-4.00014-0.
  • 25. Hamadneh, J. & Esztergár-Kiss, D. Modeling of Onboard Activities: Public Transport and Shared Autonomous Vehicle. In HCI in Mobility, Transport, and Automotive Systems: Springer International Publishing. Cham. 2021
  • 26. Mokhtarian, P.L. Subjective well-being and travel: retrospect and prospect. Transportation. 2019. Vol. 46(2). P. 493-513. DOI: 10.1007/s11116-018-9935-y.
  • 27. Noorbakhsh, P. & Khademi, N. & Chaiyasarn, K. Exploration of women cyclists’ perceived security using tree-based machine learning algorithms. In: Procedia Computer Science: Elsevier B. V. 2023. DOI: 10.1016/j.procs.2023.03.079.
  • 28. Liu, L. & Wang, Y. & Hickman, R. How Rail Transit Makes a Difference in People’s Multimodal Travel Behaviours: An Analysis with the XGBoost Method. Land. 2023. Vol. 12(3) DOI: 10.3390/land12030675.
  • 29. Dharmowijoyo, D.B. & Susilo, Y.O. & Karlström, A. & et al. Collecting a multi-dimensional three-weeks household time-use and activity diary in the Bandung Metropolitan Area, Indonesia. Transportation Research Part A: Policy Practice. 2013. Vol. 80. P. 231-246. DOI: 10.1016/j.tra.2015.08.001.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-789dc9d9-32b1-4698-b62a-98faeb4d11ca
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