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Nonlinear Relationships between Vehicle Ownership and Household Travel Characteristics and Built Environment Attributes in the US Using the XGBT Algorithm

Author

Listed:
  • Te Ma

    (School of Tourism, Dalian University, Dalian 116000, 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, Malaysia)

  • Mujahid Ali

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

  • Rosilawati Zainol

    (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, Malaysia)

  • Amin Jan

    (Faculty of Hospitality, Tourism and Wellness. Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa 16100, 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

In the United States, several studies have looked at the association between automobile ownership and sociodemographic factors and built environment qualities, but few have looked at household travel characteristics. Their interactions and nonlinear linkages are frequently overlooked in existing studies. Utilizing the 2017 US National Household Travel Survey, the authors employed an extreme gradient boosting tree model to evaluate the nonlinear and interaction impacts of household travel characteristics and built environment factors on vehicle ownership in three states of the United States (California, Missouri, and Kansas) that are different in population size. To develop these models, three main XGBT parameters, including the number of trees, maximal depth, and minimum rows, were optimized using a grid search technique. In California, the predictability of vehicle ownership was driven by household travel characteristics (cumulative importance: 0.62). Predictions for vehicle ownership in Missouri and Kansas were dominantly influenced by sociodemographic factors (cumulative importance: 0.53 and 0.55, respectively). In all states, the authors found that the number of drivers in a household plays a vital role in the vehicle ownership decisions of households. Regarding the built environment attributes, deficiencies in cycling infrastructure were the most prominent attribute in predicting household vehicle ownership in California. This variable, however, has threshold connections with vehicle ownership, but the magnitude of these relationships is small. The outcomes imply that improving the condition of cycling infrastructure will help reduce the number of vehicles. In addition, incentives that encourage the households’ drivers not to buy new vehicles are helpful. The outcomes of this study might aid policymakers in developing policies that encourage sustainable vehicle ownership in the United States.

Suggested Citation

  • Te Ma & Mahdi Aghaabbasi & Mujahid Ali & Rosilawati Zainol & Amin Jan & Abdeliazim Mustafa Mohamed & Abdullah Mohamed, 2022. "Nonlinear Relationships between Vehicle Ownership and Household Travel Characteristics and Built Environment Attributes in the US Using the XGBT Algorithm," Sustainability, MDPI, vol. 14(6), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3395-:d:770792
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    References listed on IDEAS

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    Cited by:

    1. Zhiqiang Xu & Mahdi Aghaabbasi & Mujahid Ali & Elżbieta Macioszek, 2022. "Targeting Sustainable Transportation Development: The Support Vector Machine and the Bayesian Optimization Algorithm for Classifying Household Vehicle Ownership," Sustainability, MDPI, vol. 14(17), pages 1-17, September.
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