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Influential Factors Affecting Travelers’ Mode Choice Behavior on Mass Transit in Bangkok, Thailand

Author

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  • Phattarasuda Witchayaphong

    (Transportation Engineering, School of Engineering and Technology, Asian Institute of Technology, Bangkok 12120, Thailand)

  • Surachet Pravinvongvuth

    (Transportation Engineering, School of Engineering and Technology, Asian Institute of Technology, Bangkok 12120, Thailand)

  • Kunnawee Kanitpong

    (Transportation Engineering, School of Engineering and Technology, Asian Institute of Technology, Bangkok 12120, Thailand)

  • Kazushi Sano

    (Department of Civil and Environmental Engineering, Nagaoka University of Technology, Niigata 940-2188, Japan)

  • Suksun Horpibulsuk

    (School of Civil Engineering, and Center of Excellence in Innovation for Sustainable Infrastructure Development, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
    Academy of Science, The Royal Society of Thailand, Bangkok 10300, Thailand)

Abstract

Increasing use of single or fewer occupant vehicles has increased traffic congestion and transport-related emissions. Public transport as mass transit options are increasingly being encouraged amongst travelers to use, as this is an influential strategy to improve the transport network performance. This paper presents a study based on a revealed preference survey conducted on a random sample of 4467 respondents to understand the influential factors affecting the users’ choice of mass transit in Bangkok, Thailand. This study identified an inversely proportional relationship of socio-economic and spatial attributes on public transport mode choice. The binary logit model was employed to compare the utility of private vehicles and mass transit modes. The results showed that gender, age, average income, auto ownership, total travel cost in private transport, total travel time in public transport and distance range from home to mass transit station were the factors that influenced travelers’ mode choice behavior. Moreover, to ascertain the effects of explanatory variables which influence the likelihood of Thai travelers, another binary logit model analysis was utilized by the four distance ranges condition. The studied results showed that there were few significant differences in the propensity to use mass transit. Due to the longer distance of the station, total travel time in public transport was not affected by the Thai travelers mode choice. This research will aid transport authorities and planners to gain knowledge on the impact of socio-economic and spatial behavior of public transport users on their mode choice, resulting in the development in sustainable transport in Bangkok, Thailand.

Suggested Citation

  • Phattarasuda Witchayaphong & Surachet Pravinvongvuth & Kunnawee Kanitpong & Kazushi Sano & Suksun Horpibulsuk, 2020. "Influential Factors Affecting Travelers’ Mode Choice Behavior on Mass Transit in Bangkok, Thailand," Sustainability, MDPI, vol. 12(22), pages 1-18, November.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:22:p:9522-:d:445683
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    as
    1. Sohani Liyanage & Hussein Dia, 2020. "An Agent-Based Simulation Approach for Evaluating the Performance of On-Demand Bus Services," Sustainability, MDPI, vol. 12(10), pages 1-20, May.
    2. Cristian Domarchi & Alejandro Tudela & Angélica González, 2008. "Effect of attitudes, habit and affective appraisal on mode choice: an application to university workers," Transportation, Springer, vol. 35(5), pages 585-599, August.
    3. Chalak, Ali & Al-Naghi, Hani & Irani, Alexandra & Abou-Zeid, Maya, 2016. "Commuters’ behavior towards upgraded bus services in Greater Beirut: Implications for greenhouse gas emissions, social welfare and transport policy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 88(C), pages 265-285.
    4. Rusul Abduljabbar & Hussein Dia & Sohani Liyanage & Saeed Asadi Bagloee, 2019. "Applications of Artificial Intelligence in Transport: An Overview," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
    5. Eboli, Laura & Forciniti, Carmen & Mazzulla, Gabriella, 2018. "Spatial variation of the perceived transit service quality at rail stations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PA), pages 67-83.
    6. Daniel Albalate & Xavier Fageda, 2019. "Congestion, Road Safety, and the Effectiveness of Public Policies in Urban Areas," Sustainability, MDPI, vol. 11(18), pages 1-21, September.
    7. Hakim Hammadou & Claire Papaix, 2015. "Policy packages for modal shift and CO2 reduction in Lille, France," Post-Print hal-01533557, HAL.
    8. Buehler, Ralph, 2011. "Determinants of transport mode choice: a comparison of Germany and the USA," Journal of Transport Geography, Elsevier, vol. 19(4), pages 644-657.
    9. Wanpen Charoentrakulpeeti & Edsel Sajor & Willi Zimmermann, 2006. "Middle‐class Travel Patterns, Predispositions and Attitudes, and Present‐day Transport Policy in Bangkok, Thailand," Transport Reviews, Taylor & Francis Journals, vol. 26(6), pages 693-712, April.
    10. Cynthia Chen & Hongmian Gong & Robert Paaswell, 2008. "Role of the built environment on mode choice decisions: additional evidence on the impact of density," Transportation, Springer, vol. 35(3), pages 285-299, May.
    11. Rebeca Fontanilla Andong & Edsel Sajor, 2017. "Urban sprawl, public transport, and increasing CO2 emissions: the case of Metro Manila, Philippines," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(1), pages 99-123, February.
    12. Santos, Georgina, 2017. "Road transport and CO2 emissions: What are the challenges?," Transport Policy, Elsevier, vol. 59(C), pages 71-74.
    13. Siavash Khalili & Eetu Rantanen & Dmitrii Bogdanov & Christian Breyer, 2019. "Global Transportation Demand Development with Impacts on the Energy Demand and Greenhouse Gas Emissions in a Climate-Constrained World," Energies, MDPI, vol. 12(20), pages 1-54, October.
    14. Kevin Washbrook & Wolfgang Haider & Mark Jaccard, 2006. "Estimating commuter mode choice: A discrete choice analysis of the impact of road pricing and parking charges," Transportation, Springer, vol. 33(6), pages 621-639, November.
    15. Sohani Liyanage & Hussein Dia & Rusul Abduljabbar & Saeed Asadi Bagloee, 2019. "Flexible Mobility On-Demand: An Environmental Scan," Sustainability, MDPI, vol. 11(5), pages 1-39, February.
    16. Chidambaram, Bhuvanachithra & Janssen, Marco A. & Rommel, Jens & Zikos, Dimitrios, 2014. "Commuters’ mode choice as a coordination problem: A framed field experiment on traffic policy in Hyderabad, India," Transportation Research Part A: Policy and Practice, Elsevier, vol. 65(C), pages 9-22.
    17. Fu, Xuemei & Juan, Zhicai, 2017. "Exploring the psychosocial factors associated with public transportation usage and examining the “gendered” difference," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 70-82.
    18. Frieden, T.R., 2010. "A framework for public health action: The health impact pyramid," American Journal of Public Health, American Public Health Association, vol. 100(4), pages 590-595.
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    6. Masanobu Kii & Yuki Goda & Varameth Vichiensan & Hiroyuki Miyazaki & Rolf Moeckel, 2021. "Assessment of Spatiotemporal Peak Shift of Intra-Urban Transportation Taking a Case in Bangkok, Thailand," Sustainability, MDPI, vol. 13(12), pages 1-16, June.

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