IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i6p4731-d1090160.html
   My bibliography  Save this article

Effects of Data Characteristics on Bus Travel Time Prediction: A Systematic Study

Author

Listed:
  • Hima Shaji

    (Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600036, India)

  • Lelitha Vanajakshi

    (Department of Civil Engineering/Robert Bosch Centre for Data Science and Artificial Intelligence, Indian Institute of Technology Madras, Chennai 600036, India)

  • Arun Tangirala

    (Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai 600036, India)

Abstract

The prediction of bus travel time with accuracy is a significant step toward improving the quality of public transportation. Drawing meaningful inferences from the data and using these to aid in prediction tasks is always an area of interest. Earlier studies predicted bus travel times by identifying significant regressors, which were identified based on chronological factors. However, travel time patterns may vary depending on time and location. A related question is whether the prediction accuracy can be improved with the choice of input variables. The present study analyzes this question systematically by presenting the input data in different ways to the prediction algorithm. The prediction accuracy increased when the dataset was grouped, and separate models were trained on them, the highest accurate case being the one where the data-derived clusters were considered. This demonstrates that understanding patterns and groups within the dataset helps in improving prediction accuracy.

Suggested Citation

  • Hima Shaji & Lelitha Vanajakshi & Arun Tangirala, 2023. "Effects of Data Characteristics on Bus Travel Time Prediction: A Systematic Study," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:4731-:d:1090160
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/6/4731/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/6/4731/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Julio, Nikolas & Giesen, Ricardo & Lizana, Pedro, 2016. "Real-time prediction of bus travel speeds using traffic shockwaves and machine learning algorithms," Research in Transportation Economics, Elsevier, vol. 59(C), pages 250-257.
    2. Robert Thorndike, 1953. "Who belongs in the family?," Psychometrika, Springer;The Psychometric Society, vol. 18(4), pages 267-276, December.
    Full references (including those not matched with items on IDEAS)

    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. Becken, Susanne & Stantic, Bela & Chen, Jinyan & Connolly, Rod M., 2022. "Twitter conversations reveal issue salience of aviation in the broader context of climate change," Journal of Air Transport Management, Elsevier, vol. 98(C).
    2. Archana R. Panhalkar & Dharmpal D. Doye, 2020. "An approach of improving decision tree classifier using condensed informative data," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 431-445, December.
    3. Michele Cincera, 2005. "Firms' productivity growth and R&D spillovers: An analysis of alternative technological proximity measures," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 14(8), pages 657-682.
    4. Horstmann, Felix, 2017. "Measuring the shopper's attitude toward the point of sale display: Scale development and validation," Journal of Retailing and Consumer Services, Elsevier, vol. 36(C), pages 112-123.
    5. Elizaveta Zinovyeva & Raphael C. G. Reule & Wolfgang Karl Hardle, 2021. "Understanding Smart Contracts: Hype or Hope?," Papers 2103.08447, arXiv.org.
    6. Chester Harris, 1955. "Characteristics of two measures of profile similarity," Psychometrika, Springer;The Psychometric Society, vol. 20(4), pages 289-297, December.
    7. Shahzad, Murtuza & Alhoori, Hamed & Freedman, Reva & Rahman, Shaikh Abdul, 2022. "Quantifying the online long-term interest in research," Journal of Informetrics, Elsevier, vol. 16(2).
    8. Boztug, Yasemin & Reutterer, Thomas, 2008. "A combined approach for segment-specific market basket analysis," European Journal of Operational Research, Elsevier, vol. 187(1), pages 294-312, May.
    9. Martin Kueppers & Christian Perau & Marco Franken & Hans Joerg Heger & Matthias Huber & Michael Metzger & Stefan Niessen, 2020. "Data-Driven Regionalization of Decarbonized Energy Systems for Reflecting Their Changing Topologies in Planning and Optimization," Energies, MDPI, vol. 13(16), pages 1-15, August.
    10. João Antunes Rodrigues & Alexandre Martins & Mateus Mendes & José Torres Farinha & Ricardo J. G. Mateus & Antonio J. Marques Cardoso, 2022. "Automatic Risk Assessment for an Industrial Asset Using Unsupervised and Supervised Learning," Energies, MDPI, vol. 15(24), pages 1-17, December.
    11. Chompoonut Kongphunphin & Manat Srivanit, 2021. "A Multi-Dimensional Clustering Applied to Classify the Typology of Urban Public Parks in Bangkok Metropolitan Area, Thailand," Sustainability, MDPI, vol. 13(20), pages 1-18, October.
    12. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    13. Isakov , Alexander, 2013. "Stress indicator construction for internal money market," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 30(2), pages 77-92.
    14. Aurelia Rybak & Aleksandra Rybak & Spas D. Kolev, 2021. "Analysis of the EU-27 Countries Energy Markets Integration in Terms of the Sustainable Development SDG7 Implementation," Energies, MDPI, vol. 14(21), pages 1-22, October.
    15. Mr. Emre Alper & Michal Miktus, 2019. "Digital Connectivity in sub-Saharan Africa: A Comparative Perspective," IMF Working Papers 2019/210, International Monetary Fund.
    16. Zlatana Nenova & Jennifer Shang, 2022. "Chronic Disease Progression Prediction: Leveraging Case‐Based Reasoning and Big Data Analytics," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 259-280, January.
    17. Yang, Zaili & Yang, Zhisen & Smith, John & Robert, Bostock Adam Peter, 2021. "Risk analysis of bicycle accidents: A Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    18. Achilleas Anastasiou & Peter Hatzopoulos & Alex Karagrigoriou & George Mavridoglou, 2021. "Causality Distance Measures for Multivariate Time Series with Applications," Mathematics, MDPI, vol. 9(21), pages 1-15, October.
    19. Aurelia Rybak & Aleksandra Rybak & Jarosław Joostberens & Spas D. Kolev, 2022. "Cluster Analysis of the EU-27 Countries in Light of the Guiding Principles of the European Green Deal, with Particular Emphasis on Poland," Energies, MDPI, vol. 15(14), pages 1-20, July.
    20. Mallory E. McMahon & Lyubov Doroshenko & Javad Roostaei & Hyunsoon Cho & Mansoor A. Haider, 2022. "Unsupervised learning methods for efficient geographic clustering and identification of disease disparities with applications to county-level colorectal cancer incidence in California," Health Care Management Science, Springer, vol. 25(4), pages 574-589, December.

    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:15:y:2023:i:6:p:4731-:d:1090160. 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.