Online prediction of travel time: Experience from a pilot trial

    Research output: Contribution to journalArticleScientific

    1 Citation (Scopus)

    Abstract

    This study was designed to present an online model which predicted travel times on an interurban two-lane two-way highway section on the basis of field measurements.
    The study included two parts: an evaluation of the performance of the model, and an examination of the possibility to improve the model in case of unsatisfactory performance. The model was based on MLP neural networks. The main results of the evaluation showed that the prediction model outperformed a non-predictive system.
    However, the model for one section had not performed as well during the trial period as was expected. This might be due to a slight change in the congestion phenomenon. After further development, the findings showed that the model could be improved considerably with new data.
    The main implication was that even a simple prediction model improves the quality of travel time information substantially, compared to estimates based directly on the latest measurements.
    Original languageEnglish
    Pages (from-to)271-287
    JournalTransportation Planning and Technology
    Volume30
    Issue number2-3
    DOIs
    Publication statusPublished - 2007
    MoE publication typeB1 Article in a scientific magazine

    Fingerprint

    time experience
    Travel time
    travel time
    travel
    prediction
    trial
    congestion
    evaluation
    neural network
    performance
    road
    Neural networks
    examination

    Keywords

    • Prediction
    • travel time
    • information
    • neural networks
    • highway

    Cite this

    @article{4de98aec33354b73995bfa0a25ddc109,
    title = "Online prediction of travel time: Experience from a pilot trial",
    abstract = "This study was designed to present an online model which predicted travel times on an interurban two-lane two-way highway section on the basis of field measurements. The study included two parts: an evaluation of the performance of the model, and an examination of the possibility to improve the model in case of unsatisfactory performance. The model was based on MLP neural networks. The main results of the evaluation showed that the prediction model outperformed a non-predictive system. However, the model for one section had not performed as well during the trial period as was expected. This might be due to a slight change in the congestion phenomenon. After further development, the findings showed that the model could be improved considerably with new data. The main implication was that even a simple prediction model improves the quality of travel time information substantially, compared to estimates based directly on the latest measurements.",
    keywords = "Prediction, travel time, information, neural networks, highway",
    author = "Satu Innamaa",
    year = "2007",
    doi = "10.1080/03081060701395535",
    language = "English",
    volume = "30",
    pages = "271--287",
    journal = "Transportation Planning and Technology",
    issn = "0308-1060",
    publisher = "Taylor & Francis",
    number = "2-3",

    }

    Online prediction of travel time : Experience from a pilot trial. / Innamaa, Satu.

    In: Transportation Planning and Technology, Vol. 30, No. 2-3, 2007, p. 271-287.

    Research output: Contribution to journalArticleScientific

    TY - JOUR

    T1 - Online prediction of travel time

    T2 - Experience from a pilot trial

    AU - Innamaa, Satu

    PY - 2007

    Y1 - 2007

    N2 - This study was designed to present an online model which predicted travel times on an interurban two-lane two-way highway section on the basis of field measurements. The study included two parts: an evaluation of the performance of the model, and an examination of the possibility to improve the model in case of unsatisfactory performance. The model was based on MLP neural networks. The main results of the evaluation showed that the prediction model outperformed a non-predictive system. However, the model for one section had not performed as well during the trial period as was expected. This might be due to a slight change in the congestion phenomenon. After further development, the findings showed that the model could be improved considerably with new data. The main implication was that even a simple prediction model improves the quality of travel time information substantially, compared to estimates based directly on the latest measurements.

    AB - This study was designed to present an online model which predicted travel times on an interurban two-lane two-way highway section on the basis of field measurements. The study included two parts: an evaluation of the performance of the model, and an examination of the possibility to improve the model in case of unsatisfactory performance. The model was based on MLP neural networks. The main results of the evaluation showed that the prediction model outperformed a non-predictive system. However, the model for one section had not performed as well during the trial period as was expected. This might be due to a slight change in the congestion phenomenon. After further development, the findings showed that the model could be improved considerably with new data. The main implication was that even a simple prediction model improves the quality of travel time information substantially, compared to estimates based directly on the latest measurements.

    KW - Prediction

    KW - travel time

    KW - information

    KW - neural networks

    KW - highway

    U2 - 10.1080/03081060701395535

    DO - 10.1080/03081060701395535

    M3 - Article

    VL - 30

    SP - 271

    EP - 287

    JO - Transportation Planning and Technology

    JF - Transportation Planning and Technology

    SN - 0308-1060

    IS - 2-3

    ER -