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.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Kernel regression for travel time estimation via convex optimization


    Contributors:


    Publication date :

    2009


    Size :

    6 Seiten, 30 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Color Constancy Via Convex Kernel Optimization

    Yuan, Xiaotong / Li, Stan Z. / He, Ran | Springer Verlag | 2007



    Local online kernel ridge regression for forecasting of urban travel times

    Haworth, James / Shawe-Taylor, John / Cheng, Tao et al. | Elsevier | 2014


    TRAVEL TIME ESTIMATION SYSTEM AND TRAVEL TIME ESTIMATION METHOD

    ISHISHIRO KENJI / KURISHIMA YUSUKE | European Patent Office | 2015

    Free access

    Travel time estimation

    RANDER PETER / STENTZ ANTHONY / NAGY BRYAN | European Patent Office | 2020

    Free access