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Real-time prediction of bus travel speeds using traffic shockwaves and machine learning algorithms

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  • Julio, Nikolas
  • Giesen, Ricardo
  • Lizana, Pedro

Abstract

Most transit agencies are trying to increase their ridership. To achieve this goal, they are looking to maintain or even improve their level of service. This is very hard, since traffic congestion is normally increasing. As a result, bus travel times are higher and less reliable, which makes harder to predict travel times and avoid bunching. Being able to accurately predict bus travel speeds and update this prediction with real-time information could improve the quality and reliability of the information given to users, and increase the effectiveness of control schemes.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:retrec:v:59:y:2016:i:c:p:250-257
    DOI: 10.1016/j.retrec.2016.07.019
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    References listed on IDEAS

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    1. Salvo, G. & Amato, G. & Zito, Pietro, 2007. "Bus speed estimation by neural networks to improve the automatic fleet management," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 37, pages 93-104.
    2. Castillo, Enrique & Menéndez, José María & Sánchez-Cambronero, Santos, 2008. "Predicting traffic flow using Bayesian networks," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 482-509, June.
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    Cited by:

    1. Liping Ge & Malek Sarhani & Stefan Voß & Lin Xie, 2021. "Review of Transit Data Sources: Potentials, Challenges and Complementarity," Sustainability, MDPI, vol. 13(20), pages 1-37, October.
    2. 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.
    3. Bisheng He & Yongjun Zhu & Andrea D’Ariano & Keyu Wen & Lufeng Chen, 2023. "Dynamic Relational Graph Convolutional Network for Metro Passenger Flow Forecasting," SN Operations Research Forum, Springer, vol. 4(4), pages 1-27, December.
    4. Zbigniew Czapla & Grzegorz Sierpiński, 2023. "Driving and Energy Profiles of Urban Bus Routes Predicted for Operation with Battery Electric Buses," Energies, MDPI, vol. 16(15), pages 1-19, July.

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