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Utilizing data mining techniques to predict expected freeway travel time from experienced travel time

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  • Moonam, Hasan M.
  • Qin, Xiao
  • Zhang, Jun

Abstract

As the most important real-time traveler information, travel time can be either experienced or expected (i.e. to be experienced). When a vehicle completes a trip, the travel time refers to the experienced travel time. In contrast, when a vehicle starts its journey, the travel time is unknown but can be predicted, which is the expected travel time. Although the experienced travel time is termed as the real-time travel time, a traveler may encounter a somewhat different travel time (from expected travel time) due to the changing traffic conditions. Therefore, expected travel time needs to be predicted. In this study, the expected travel time was predicted from the experienced travel time using the data mining techniques such as k-nearest neighbor (k-NN), least squares regression boosting (LSBoost) and Kalman filter (KF) methods. After comparing the performances of KF to corresponding modeling techniques from both link and corridor perspectives, it is concluded that the KF method offers superior prediction accuracy in a link-based model. Moreover, the effect of different noise assumptions was examined and it is found that the steady noise computed from the full-dataset had the most accurate prediction. A data processing algorithm, which processed more than a hundred million records reliably and efficiently was also introduced.

Suggested Citation

  • Moonam, Hasan M. & Qin, Xiao & Zhang, Jun, 2019. "Utilizing data mining techniques to predict expected freeway travel time from experienced travel time," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 154-167.
    • Handle: RePEc:eee:matcom:v:155:y:2019:i:c:p:154-167
    DOI: 10.1016/j.matcom.2018.01.006
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    References listed on IDEAS

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    1. Dion, Francois & Rakha, Hesham, 2006. "Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates," Transportation Research Part B: Methodological, Elsevier, vol. 40(9), pages 745-766, November.
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    4. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, January.
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    Cited by:

    1. Zhen Chen & Wei Fan, 2021. "A Freeway Travel Time Prediction Method Based on an XGBoost Model," Sustainability, MDPI, vol. 13(15), pages 1-15, July.
    2. Fang Zhao & Bingfeng Si & Zhenlin Wei & Tianwei Lu, 2023. "Time-dependent vehicle routing problem of perishable product delivery considering the differences among paths on the congested road," Operational Research, Springer, vol. 23(1), pages 1-23, March.
    3. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha, 2019. "Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm," Energies, MDPI, vol. 12(6), pages 1-13, March.

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