Self-adapting traffic flow status forecasts using clustering

    Research output: Contribution to journalArticleScientificpeer-review

    14 Citations (Scopus)

    Abstract

    A key problem for the efficient use of stationary traffic prediction models is that for adaptation to new data they require human-made re-calibration with a new database. So far, there has been a lack of knowledge of how to develop a practical prediction model that would learn while working online. Anyone providing real-time traffic information and making forecasts of the traffic situation could benefit from such models. The aim of the study is to develop a method to make a self-adapting short-term prediction model for the status of traffic flow. The principles for such a model are described. The method is based on self-organising map and the model is implemented on a highway in the Helsinki Metropolitan Area. Specifically, the structure of the model makes it possible for the model to learn by itself without the need to save all the data into databases. Consequently, long-term online use of the model makes fewer demands on computers. The results indicated that the self-adapting principle improved the performance of the model. The principles of the model can also be applied in other locations.
    Original languageEnglish
    Pages (from-to)67-76
    Number of pages10
    JournalIET Intelligent Transport Systems
    Volume3
    Issue number1
    DOIs
    Publication statusPublished - 2009
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    traffic
    forecast
    prediction
    Self organizing maps
    metropolitan area
    agglomeration area
    Calibration
    road
    calibration
    lack
    performance

    Cite this

    @article{9def71a2f77a4c0fbf0fe573c3fa8e34,
    title = "Self-adapting traffic flow status forecasts using clustering",
    abstract = "A key problem for the efficient use of stationary traffic prediction models is that for adaptation to new data they require human-made re-calibration with a new database. So far, there has been a lack of knowledge of how to develop a practical prediction model that would learn while working online. Anyone providing real-time traffic information and making forecasts of the traffic situation could benefit from such models. The aim of the study is to develop a method to make a self-adapting short-term prediction model for the status of traffic flow. The principles for such a model are described. The method is based on self-organising map and the model is implemented on a highway in the Helsinki Metropolitan Area. Specifically, the structure of the model makes it possible for the model to learn by itself without the need to save all the data into databases. Consequently, long-term online use of the model makes fewer demands on computers. The results indicated that the self-adapting principle improved the performance of the model. The principles of the model can also be applied in other locations.",
    author = "Satu Innamaa",
    year = "2009",
    doi = "10.1049/iet-its:20070048",
    language = "English",
    volume = "3",
    pages = "67--76",
    journal = "IET Intelligent Transport Systems",
    issn = "1751-956X",
    publisher = "Institution of Engineering and Technology IET",
    number = "1",

    }

    Self-adapting traffic flow status forecasts using clustering. / Innamaa, Satu.

    In: IET Intelligent Transport Systems, Vol. 3, No. 1, 2009, p. 67-76.

    Research output: Contribution to journalArticleScientificpeer-review

    TY - JOUR

    T1 - Self-adapting traffic flow status forecasts using clustering

    AU - Innamaa, Satu

    PY - 2009

    Y1 - 2009

    N2 - A key problem for the efficient use of stationary traffic prediction models is that for adaptation to new data they require human-made re-calibration with a new database. So far, there has been a lack of knowledge of how to develop a practical prediction model that would learn while working online. Anyone providing real-time traffic information and making forecasts of the traffic situation could benefit from such models. The aim of the study is to develop a method to make a self-adapting short-term prediction model for the status of traffic flow. The principles for such a model are described. The method is based on self-organising map and the model is implemented on a highway in the Helsinki Metropolitan Area. Specifically, the structure of the model makes it possible for the model to learn by itself without the need to save all the data into databases. Consequently, long-term online use of the model makes fewer demands on computers. The results indicated that the self-adapting principle improved the performance of the model. The principles of the model can also be applied in other locations.

    AB - A key problem for the efficient use of stationary traffic prediction models is that for adaptation to new data they require human-made re-calibration with a new database. So far, there has been a lack of knowledge of how to develop a practical prediction model that would learn while working online. Anyone providing real-time traffic information and making forecasts of the traffic situation could benefit from such models. The aim of the study is to develop a method to make a self-adapting short-term prediction model for the status of traffic flow. The principles for such a model are described. The method is based on self-organising map and the model is implemented on a highway in the Helsinki Metropolitan Area. Specifically, the structure of the model makes it possible for the model to learn by itself without the need to save all the data into databases. Consequently, long-term online use of the model makes fewer demands on computers. The results indicated that the self-adapting principle improved the performance of the model. The principles of the model can also be applied in other locations.

    U2 - 10.1049/iet-its:20070048

    DO - 10.1049/iet-its:20070048

    M3 - Article

    VL - 3

    SP - 67

    EP - 76

    JO - IET Intelligent Transport Systems

    JF - IET Intelligent Transport Systems

    SN - 1751-956X

    IS - 1

    ER -