We develop an algorithm aimed at estimating travel time on segments of a road network using a convex optimization framework. Sampled travel time from probe vehicles are assumed to be known and serve as a training set for a machine learning algorithm to provide an optimal estimate of the travel time for all vehicles. A kernel method is introduced to allow for a non-linear relation between the known entry times and the travel times that we want to estimate. To improve the quality of the estimate we minimize the estimation error over a convex combination of known kernels. This problem is shown to be a semi-definite program. A rank-one decomposition is used to convert it to a linear program which can be solved efficiently.


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    Titel :

    Kernel regression for travel time estimation via convex optimization


    Beteiligte:


    Erscheinungsdatum :

    2009


    Format / Umfang :

    6 Seiten, 30 Quellen




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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