Short-term prediction of travel time using neural networks on an interurban highway

    Research output: Contribution to journalArticleScientificpeer-review

    64 Citations (Scopus)

    Abstract

    The main purpose of this study was to investigate the predictability of travel time with a model based on travel time data measured in the field on an interurban highway. Another purpose was to determine whether the forecasts would be accurate enough to implement the model in an actual online travel time information service. The study was carried out on a 28-kilometre-long rural two-lane road section where traffic congestion was a problem during weekend peak hours. The section was equipped with an automatic travel time monitoring and information system. The prediction models were made as feedforward multilayer perceptron neural networks. The main results showed that the majority of the forecasts were close to the actual measured values. Consequently, use of the prediction model would improve the quality of travel time information based directly on the sum of the latest measured travel times.
    Original languageEnglish
    Pages (from-to)649 - 669
    Number of pages21
    JournalTransportation
    Volume32
    Issue number6
    DOIs
    Publication statusPublished - 2005
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    Travel time
    neural network
    travel time
    travel
    road
    Neural networks
    prediction
    traffic congestion
    Traffic congestion
    weekend
    Information services
    Multilayer neural networks
    information service
    monitoring system
    time
    information system
    Information systems
    monitoring
    Monitoring
    Values

    Keywords

    • neural networks
    • prediction
    • travel time
    • two-lane highway
    • road traffic
    • traffic
    • traffic congestion

    Cite this

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    title = "Short-term prediction of travel time using neural networks on an interurban highway",
    abstract = "The main purpose of this study was to investigate the predictability of travel time with a model based on travel time data measured in the field on an interurban highway. Another purpose was to determine whether the forecasts would be accurate enough to implement the model in an actual online travel time information service. The study was carried out on a 28-kilometre-long rural two-lane road section where traffic congestion was a problem during weekend peak hours. The section was equipped with an automatic travel time monitoring and information system. The prediction models were made as feedforward multilayer perceptron neural networks. The main results showed that the majority of the forecasts were close to the actual measured values. Consequently, use of the prediction model would improve the quality of travel time information based directly on the sum of the latest measured travel times.",
    keywords = "neural networks, prediction, travel time, two-lane highway, road traffic, traffic, traffic congestion",
    author = "Satu Innamaa",
    year = "2005",
    doi = "10.1007/s11116-005-0219-y",
    language = "English",
    volume = "32",
    pages = "649 -- 669",
    journal = "Transportation",
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    }

    Short-term prediction of travel time using neural networks on an interurban highway. / Innamaa, Satu.

    In: Transportation, Vol. 32, No. 6, 2005, p. 649 - 669.

    Research output: Contribution to journalArticleScientificpeer-review

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