This study presents a methodology for freeway travel time prediction that uses only count data. The proposed models include the generalized N-curve method in conjunction with the k nearest neighbor (kNN) method so that the travel time predicted for traversing a defined freeway segment at a certain departure time is similar to what a driver actually experiences. A real-world traffic network and demand are replicated in a traffic simulation model in which several scenarios are produced to serve as the test bed for evaluation and validation of the proposed algorithms. The proposed single-NN algorithm best predicts travel times for light, free-flow traffic conditions, and the multiple-NN algorithm best predicts travel times for congested traffic conditions. The hybrid-NN algorithm merges the single-NN and multiple-NN algorithms, exploiting each one where most suitable. A numerical analysis concludes the potential of the proposed models.


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    Title :

    Real-Time Freeway-Experienced Travel Time Prediction Using N-Curve and k Nearest Neighbor Methods


    Additional title:

    Transportation Research Record


    Contributors:


    Publication date :

    2011-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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