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Computer Science and Education in Computer Science. 20th EAI International Conference, CSECS 2024, Sofia, Bulgaria, June 28–30, 2024, Proceedings

Research Article

A Predictive Model of Arrival Times for Smart Shuttle Buses in Astana, Kazakhstan

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-84312-9_2,
        author={Darya Taratynova and Assel Kassenova and Bissenbay Dauletbayev and Muammar Al-Shedivat and Eugene Pinsky},
        title={A Predictive Model of Arrival Times for Smart Shuttle Buses in Astana, Kazakhstan},
        proceedings={Computer Science and Education in Computer Science. 20th EAI International Conference, CSECS 2024, Sofia, Bulgaria, June 28--30, 2024, Proceedings},
        proceedings_a={CSECS},
        year={2025},
        month={3},
        keywords={ETA Prediction KNN Shuttle bus Transportation Clustering},
        doi={10.1007/978-3-031-84312-9_2}
    }
    
  • Darya Taratynova
    Assel Kassenova
    Bissenbay Dauletbayev
    Muammar Al-Shedivat
    Eugene Pinsky
    Year: 2025
    A Predictive Model of Arrival Times for Smart Shuttle Buses in Astana, Kazakhstan
    CSECS
    Springer
    DOI: 10.1007/978-3-031-84312-9_2
Darya Taratynova1, Assel Kassenova2, Bissenbay Dauletbayev3, Muammar Al-Shedivat, Eugene Pinsky2,*
  • 1: Department of Mathematics, Nazarbayev University, 53 Kabanbay Batyr Avenue
  • 2: Department of Computer Science, Metropolitan College, Boston University, 1010 Commonwealth Avenue, Boston
  • 3: Department of Computer Science, SDU University, 1/1 Abylai Khan Street, Kaskelen
*Contact email: epinsky@bu.edu

Abstract

Accurate prediction of the estimated time of arrival (ETA) for buses is crucial for shuttle companies aiming to enhance profitability and minimize costs. This paper proposes leveraging historical bus route data to predict estimated bus arrivals at specific stations, employing K-Nearest Neighbors (KNN) supervised machine learning (ML) techniques. We develop a simple and interpretable solution that abstains from complexity, as bus routes dynamically adapt to passenger demands.

Keywords
ETA Prediction KNN Shuttle bus Transportation Clustering
Published
2025-03-14
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-84312-9_2
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