IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i6p3395-d770792.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/6/3395/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/6/3395/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vahid Najafi Moghaddam Gilani & Seyed Mohsen Hosseinian & Meisam Ghasedi & Mohammad Nikookar, 2021. "Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, May.
    2. Rahul, T.M. & Verma, Ashish, 2017. "The influence of stratification by motor-vehicle ownership on the impact of built environment factors in Indian cities," Journal of Transport Geography, Elsevier, vol. 58(C), pages 40-51.
    3. Sabouri, Sadegh & Tian, Guang & Ewing, Reid & Park, Keunhyun & Greene, William, 2021. "The built environment and vehicle ownership modeling: Evidence from 32 diverse regions in the U.S," Journal of Transport Geography, Elsevier, vol. 93(C).
    4. Song, Siqi & Diao, Mi & Feng, Chen-Chieh, 2021. "Effects of pricing and infrastructure on car ownership: A pseudo-panel-based dynamic model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 115-126.
    5. Brownstone, David & Golob, Thomas F., 2009. "The impact of residential density on vehicle usage and energy consumption," Journal of Urban Economics, Elsevier, vol. 65(1), pages 91-98, January.
    6. Zhao, Pengjun & Zhang, Yixue, 2018. "Travel behaviour and life course: Examining changes in car use after residential relocation in Beijing," Journal of Transport Geography, Elsevier, vol. 73(C), pages 41-53.
    7. Hong Sok Kim & Eungcheol Kim, 2004. "Effects Of Public Transit On Automobile Ownership And Use In Households Of The Usa," Review of Urban & Regional Development Studies, Wiley Blackwell, vol. 16(3), pages 245-262, November.
    8. Yibin Ao & Chuan Chen & Dujuan Yang & Yan Wang, 2018. "Relationship between Rural Built Environment and Household Vehicle Ownership: An Empirical Analysis in Rural Sichuan, China," Sustainability, MDPI, vol. 10(5), pages 1-18, May.
    9. Yang, Zhenshan & Jia, Peng & Liu, Weidong & Yin, Hongchun, 2017. "Car ownership and urban development in Chinese cities: A panel data analysis," Journal of Transport Geography, Elsevier, vol. 58(C), pages 127-134.
    10. Wang, Xiaoquan & Yin, Chaoying & Zhang, Junyi & Shao, Chunfu & Wang, Shengyou, 2021. "Nonlinear effects of residential and workplace built environment on car dependence," Journal of Transport Geography, Elsevier, vol. 96(C).
    11. Potoglou, Dimitris & Kanaroglou, Pavlos S., 2008. "Modelling car ownership in urban areas: a case study of Hamilton, Canada," Journal of Transport Geography, Elsevier, vol. 16(1), pages 42-54.
    12. Matas, Anna & Raymond, José-Luis & Roig, José-Luis, 2009. "Car ownership and access to jobs in Spain," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(6), pages 607-617, July.
    13. Xuefang Li & Chenhui Liu & Jianmin Jia, 2019. "Ownership and Usage Analysis of Alternative Fuel Vehicles in the United States with the 2017 National Household Travel Survey Data," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    14. Ding, Chuan & Cao, Xinyu (Jason) & Næss, Petter, 2018. "Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 107-117.
    15. John Holtzclaw & Robert Clear & Hank Dittmar & David Goldstein & Peter Haas, 2002. "Location Efficiency: Neighborhood and Socio-Economic Characteristics Determine Auto Ownership and Use - Studies in Chicago, Los Angeles and San Francisco," Transportation Planning and Technology, Taylor & Francis Journals, vol. 25(1), pages 1-27, January.
    16. Kim, Sung Hoo & Mokhtarian, Patricia L., 2018. "Taste heterogeneity as an alternative form of endogeneity bias: Investigating the attitude-moderated effects of built environment and socio-demographics on vehicle ownership using latent class modelin," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 130-150.
    17. Dargay, Joyce & Hanly, Mark, 2007. "Volatility of car ownership, commuting mode and time in the UK," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(10), pages 934-948, December.
    18. Wenlong Tao & Mahdi Aghaabbasi & Mujahid Ali & Abdulrazak H. Almaliki & Rosilawati Zainol & Abdulrhman A. Almaliki & Enas E. Hussein, 2022. "An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    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. 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.
    2. Chetan Doddamani & M. Manoj, 2023. "Analysis of the influences of built environment measures on household car and motorcycle ownership decisions in Hubli-Dharwad cities," Transportation, Springer, vol. 50(1), pages 205-243, February.
    3. Wang, Xiaoquan & Yin, Chaoying & Zhang, Junyi & Shao, Chunfu & Wang, Shengyou, 2021. "Nonlinear effects of residential and workplace built environment on car dependence," Journal of Transport Geography, Elsevier, vol. 96(C).
    4. Laviolette, Jérôme & Morency, Catherine & Waygood, E.O.D., 2022. "A kilometer or a mile? Does buffer size matter when it comes to car ownership?," Journal of Transport Geography, Elsevier, vol. 104(C).
    5. Delbosc, Alexa, 2013. "Household composition and within-household car saturation in Melbourne," Transport Policy, Elsevier, vol. 25(C), pages 94-100.
    6. 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.
    7. Mitra, Suman K. & Saphores, Jean-Daniel M., 2017. "Carless in California: Green choice or misery?," Journal of Transport Geography, Elsevier, vol. 65(C), pages 1-12.
    8. Robert Kudłak & Wojciech Kisiała & Bartłomiej Kołsut, 2023. "Determinanty posiadania samochodu w Polsce: wyniki modelowania w ujęciu przestrzennym w latach 2005 i 2019," Ekonomista, Polskie Towarzystwo Ekonomiczne, issue 2, pages 152-173.
    9. Bindong Sun & Tinglin Zhang & Zhou He & Rui Wang, 2017. "Urban Spatial Structure And Motorization In China," Journal of Regional Science, Wiley Blackwell, vol. 57(3), pages 470-486, June.
    10. Lara Al Otary & Maya Abou-Zeid & Isam Kaysi, 2022. "Modeling car ownership and use in a developing country context with informal public transportation," Transportation, Springer, vol. 49(1), pages 1-36, February.
    11. Sabreena Anowar & Naveen Eluru & Luis F. Miranda-Moreno, 2014. "Alternative Modeling Approaches Used for Examining Automobile Ownership: A Comprehensive Review," Transport Reviews, Taylor & Francis Journals, vol. 34(4), pages 441-473, July.
    12. Ding, Chuan & Cao, Xinyu, 2019. "How does the built environment at residential and work locations affect car ownership? An application of cross-classified multilevel model," Journal of Transport Geography, Elsevier, vol. 75(C), pages 37-45.
    13. He, Mingwei & He, Chengfeng & Shi, Zhuangbin & He, Min, 2022. "Spatiotemporal heterogeneous effects of socio-demographic and built environment on private car usage: An empirical study of Kunming, China," Journal of Transport Geography, Elsevier, vol. 101(C).
    14. Jou, Rong-Chang & Huang, Wen-Hsiu & Wu, Yuan-Chan & Chao, Ming-Che, 2012. "The asymmetric income effect on household vehicle ownership in Taiwan: A threshold cointegration approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(4), pages 696-706.
    15. Xiaoquan Wang & Chunfu Shao & Chaoying Yin & Chengxiang Zhuge & Wenjun Li, 2018. "Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun," Sustainability, MDPI, vol. 10(2), pages 1-15, February.
    16. Seya, Hajime & Nakamichi, Kumiko & Yamagata, Yoshiki, 2016. "The residential parking rent price elasticity of car ownership in Japan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 123-134.
    17. Franco Chingcuanco & Eric Miller, 2014. "A meta-model of vehicle ownership choice parameters," Transportation, Springer, vol. 41(5), pages 923-945, September.
    18. Abu Toasin Oakil & Dorien Manting & Hans Nijland, 2018. "The role of individual characteristics in car ownership shortly after relationship dissolution," Transportation, Springer, vol. 45(6), pages 1871-1882, November.
    19. Kunbo Shi & Long Cheng & Jonas De Vos & Yongchun Yang & Wanpeng Cao & Frank Witlox, 2021. "How does purchasing intangible services online influence the travel to consume these services? A focus on a Chinese context," Transportation, Springer, vol. 48(5), pages 2605-2625, October.
    20. Ding, Chuan & Cao, Xinyu (Jason) & Næss, Petter, 2018. "Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 107-117.

    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:14:y:2022:i:6:p:3395-:d:770792. 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.