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How Sustainable Is People’s Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships

Author

Listed:
  • Panyu Tang

    (ChengDu Jincheng College, Chengdu 611731, China)

  • Mahdi Aghaabbasi

    (Centre for Sustainable Urban Planning and Real Estate (SUPRE), Department of Urban and Regional Planning, Faculty of Built Environment, University of Malaya, Kuala Lumpur 50603, Wilayah Persekutuan Kuala Lumpur, Malaysia)

  • Mujahid Ali

    (Department of Civil and Environmental Engineering, Universiti Teknologi Petronas, Seri Iskandar 32610, Perak, Malaysia)

  • Amin Jan

    (Faculty of Hospitality, Tourism and Wellness, Universiti Malaysia Kelantan, City Campus, Kota Bharu 16100, Kelantan, Malaysia)

  • Abdeliazim Mustafa Mohamed

    (Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj 16273, Saudi Arabia
    Building & Construction Technology Department, Bayan College of Science and Technology, Khartoum 210, Sudan)

  • Abdullah Mohamed

    (Research Centre, Future University in Egypt, New Cairo 11745, Egypt)

Abstract

Several previous studies examined the variables of public-transit-related walking and privately owned vehicles (POVs) to go to work. However, most studies neglect the possible non-linear relationships between these variables and other potential variables. Using the 2017 U.S. National Household Travel Survey, we employ the Bayesian Network algorithm to evaluate the non-linear and interaction impacts of health condition attributes, work trip attributes, work attributes, and individual and household attributes on walking and privately owned vehicles to reach public transit stations to go to work in California. The authors found that the trip time to public transit stations is the most important factor in individuals’ walking decision to reach public transit stations. Additionally, it was found that this factor was mediated by population density. For the POV model, the population density was identified as the most important factor and was mediated by travel time to work. These findings suggest that encouraging individuals to walk to public transit stations to go to work in California may be accomplished by adopting planning practices that support dense urban growth and, as a result, reduce trip times to transit stations.

Suggested Citation

  • Panyu Tang & Mahdi Aghaabbasi & Mujahid Ali & Amin Jan & Abdeliazim Mustafa Mohamed & Abdullah Mohamed, 2022. "How Sustainable Is People’s Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:3989-:d:781400
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    References listed on IDEAS

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