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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
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    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.
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