IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i16p8327-d609543.html
   My bibliography  Save this article

Analysis and Prediction of Vehicle Kilometers Traveled: A Case Study in Spain

Author

Listed:
  • Paúl Narváez-Villa

    (University Institute for Automobile Research Francisco Aparicio Izquierdo (INSIA-UPM), Universidad Politécnica de Madrid (UPM), 28006 Madrid, Spain
    Transportation Engineering Research Group, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador)

  • Blanca Arenas-Ramírez

    (University Institute for Automobile Research Francisco Aparicio Izquierdo (INSIA-UPM), Universidad Politécnica de Madrid (UPM), 28006 Madrid, Spain)

  • José Mira

    (Statistics Department, Escuela Técnica Superior de Ingenieros Industriales (ETSII-UPM), Universidad Politécnica de Madrid (UPM), 28006 Madrid, Spain)

  • Francisco Aparicio-Izquierdo

    (University Institute for Automobile Research Francisco Aparicio Izquierdo (INSIA-UPM), Universidad Politécnica de Madrid (UPM), 28006 Madrid, Spain)

Abstract

Knowledge of the kilometers traveled by vehicles is essential in transport and road safety studies as an indicator of exposure and mobility. Its application in the determination of user risk indices in a disaggregated manner is of great interest to the scientific community and the authorities in charge of ensuring road safety on highways. This study used a sample of the data recorded during passenger vehicle inspections at Vehicle Technical Inspection stations and housed in a data warehouse managed by the General Directorate for Traffic of Spain. This study has three notable characteristics: (1) a novel data source is explored, (2) the methodology developed applies to other types of vehicles, with the level of disaggregation the data allows, and (3) pattern extraction and the estimate of mobility contribute to the continuous and necessary improvement of road safety indicators and are aligned with goal 3 (Good Health and Well-Being: Target 3.6) of The United Nations Sustainable Development Goals of the 2030 Agenda. An Operational Data Warehouse was created from the sample received, which helped in obtaining inference values for the kilometers traveled by Spanish fleet vehicles with a level of disaggregation that, to the knowledge of the authors, was unreachable with advanced statistical models. Three machine learning methods, CART, random forest, and gradient boosting, were optimized and compared based on the performance metrics of the models. The three methods identified the age, engine size, and tare weight of passenger vehicles as the factors with greatest influence on their travel patterns.

Suggested Citation

  • Paúl Narváez-Villa & Blanca Arenas-Ramírez & José Mira & Francisco Aparicio-Izquierdo, 2021. "Analysis and Prediction of Vehicle Kilometers Traveled: A Case Study in Spain," IJERPH, MDPI, vol. 18(16), pages 1-21, August.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:16:p:8327-:d:609543
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/16/8327/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/16/8327/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Duncan, Michael, 2019. "Would the replacement of park-and-ride facilities with transit-oriented development reduce vehicle kilometers traveled in an auto-oriented US region?," Transport Policy, Elsevier, vol. 81(C), pages 293-301.
    2. Assemi, Behrang & Hickman, Mark, 2018. "Relationship between heavy vehicle periodic inspections, crash contributing factors and crash severity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 441-459.
    3. Dongkwan Lee & Jean-Michel Guldmann & Choongik Choi, 2019. "Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
    4. Keiko Hirota, 2010. "Comparative Studies on Vehicle Related Policies for Air Pollution Reduction in Ten Asian Countries," Sustainability, MDPI, vol. 2(1), pages 1-18, January.
    5. Zolnik, Edmund J., 2018. "Effects of additional capacity on vehicle kilometers of travel in the U.S.: Evidence from National Household Travel Surveys," Journal of Transport Geography, Elsevier, vol. 66(C), pages 1-9.
    6. Jinhyun Hong & Qing Shen & Lei Zhang, 2014. "How do built-environment factors affect travel behavior? A spatial analysis at different geographic scales," Transportation, Springer, vol. 41(3), pages 419-440, May.
    7. Cao, Xinyu (Jason) & Xu, Zhiyi & Fan, Yingling, 2010. "Exploring the connections among residential location, self-selection, and driving: Propensity score matching with multiple treatments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 797-805, December.
    8. R.E. Wilson & S. Cairns & S. Notley & J. Anable & T. Chatterton & F. McLeod, 2013. "Techniques for the inference of mileage rates from MOT data," Transportation Planning and Technology, Taylor & Francis Journals, vol. 36(1), pages 130-143, February.
    9. Malik, Leeza & Tiwari, Geetam, 2017. "Assessment of interstate freight vehicle characteristics and impact of future emission and fuel economy standards on their emissions in India," Energy Policy, Elsevier, vol. 108(C), pages 121-133.
    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. Miguel Santolino & Luis Céspedes & Mercedes Ayuso, 2022. "The Impact of Aging Drivers and Vehicles on the Injury Severity of Crash Victims," IJERPH, MDPI, vol. 19(24), pages 1-16, December.
    2. Juan Pablo Montero-Salgado & Jose Muñoz-Sanz & Blanca Arenas-Ramírez & Cristina Alén-Cordero, 2022. "Identification of the Mechanical Failure Factors with Potential Influencing Road Accidents in Ecuador," IJERPH, MDPI, vol. 19(13), pages 1-27, June.

    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. Hong, Jinhyun & Thakuriah, Piyushimita Vonu, 2018. "Examining the relationship between different urbanization settings, smartphone use to access the Internet and trip frequencies," Journal of Transport Geography, Elsevier, vol. 69(C), pages 11-18.
    2. Faan Chen & Adriano Borges Costa, 2024. "Exploring the causal effects of the built environment on travel behavior: a unique randomized experiment in Shanghai," Transportation, Springer, vol. 51(1), pages 215-245, February.
    3. Guan, Xiaodong & Wang, Donggen, 2020. "The multiplicity of self-selection: What do travel attitudes influence first, residential location or work place?," Journal of Transport Geography, Elsevier, vol. 87(C).
    4. Chowdhury, Tufayel & Scott, Darren M., 2020. "An analysis of the built environment and auto travel in Halifax, Canada," Transport Policy, Elsevier, vol. 94(C), pages 23-33.
    5. 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.
    6. Jinhyun Hong & David Philip McArthur & Mark Livingston, 2019. "Can Accessing the Internet while Travelling Encourage Commuters to Use Public Transport Regardless of Their Attitude?," Sustainability, MDPI, vol. 11(12), pages 1-10, June.
    7. Watanabe, Hajime & Maruyama, Takuya, 2024. "A Bayesian sample selection model with a binary outcome for handling residential self-selection in individual car ownership," Journal of choice modelling, Elsevier, vol. 51(C).
    8. 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.
    9. Lee, Shin, 2018. "Transport policies, induced traffic and their influence on vehicle emissions in developed and developing countries," Energy Policy, Elsevier, vol. 121(C), pages 264-274.
    10. Masato Abe, 2011. "Achieving a sustainable automotive sector in Asia and the Pacific: Challenges and opportunities for the reduction of vehicle CO2 emissions," Working Papers 10811, Asia-Pacific Research and Training Network on Trade (ARTNeT), an initiative of UNESCAP and IDRC, Canada..
    11. Pot, Felix Johan & Koster, Sierdjan & Tillema, Taede, 2023. "Perceived accessibility and residential self-selection in the Netherlands," Journal of Transport Geography, Elsevier, vol. 108(C).
    12. Lin, Tao & Wang, Donggen & Guan, Xiaodong, 2017. "The built environment, travel attitude, and travel behavior: Residential self-selection or residential determination?," Journal of Transport Geography, Elsevier, vol. 65(C), pages 111-122.
    13. Xingang Zhou & Anthony G. O. Yeh, 2021. "Understanding the modifiable areal unit problem and identifying appropriate spatial unit in jobs–housing balance and employment self-containment using big data," Transportation, Springer, vol. 48(3), pages 1267-1283, June.
    14. Faizeh Hatami & Jean-Claude Thill, 2022. "Spatiotemporal Evaluation of the Built Environment’s Impact on Commuting Duration," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    15. Zhong Wang & Rui Xu, 2022. "Price Controls and Platform Ecosystem: A Comparative Analysis of Parking Applications between Beijing and London," Sustainability, MDPI, vol. 14(9), pages 1-17, May.
    16. Bereitschaft, Bradley, 2020. "Gentrification and the evolution of commuting behavior within America's urban cores, 2000–2015," Journal of Transport Geography, Elsevier, vol. 82(C).
    17. Akbari Ahmadabadi, Ali & Heravi, Gholamreza, 2019. "Risk assessment framework of PPP-megaprojects focusing on risk interaction and project success," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 169-188.
    18. 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.
    19. Hong, Jinhyun & Philip McArthur, David & Stewart, Joanna L., 2020. "Can providing safe cycling infrastructure encourage people to cycle more when it rains? The use of crowdsourced cycling data (Strava)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 109-121.
    20. Zhang, Zhaolin & Zhai, Guocong & Xie, Kun & Xiao, Feng, 2022. "Exploring the nonlinear effects of ridesharing on public transit usage: A case study of San Diego," Journal of Transport Geography, Elsevier, vol. 104(C).

    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:jijerp:v:18:y:2021:i:16:p:8327-:d:609543. 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.