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

Hima Shaji, Lelitha Vanajakshi () and Arun Tangirala
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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

Sustainability, 2023, vol. 15, issue 6, 1-17

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.

Keywords: travel time data analysis; bus travel time; clustering; prediction; machine learning techniques (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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