In this paper, we introduce the problem of mining frequent sub-routes for the use of platoon driving. The problem is related to the problem of mining frequent sequences. While the problem of mining frequent sequences normally focuses on patterns with a large support, the problem of mining frequent sub-routes has to deal with a small absolute support of two or more. We will present our algorithm to find truck platoon sequential pattern (TPSpan) which is based on pattern-growth methods and solves the problem of mining frequent sub-routes in an efficient way by using so called pruning parameters (the amount of platoons increase exponentially with the amount of participating trucks). We also present experimental results about the amount of possible truck platoons based on synthetic data and about the possibility of finding truck platoons with the use of pruning parameters in context of efficiency, flexibility and reliability. Road freight transport rose by 28 % between 1995 and 2008, until 2020 an increase of 41 % in road transportation is expected. Experiments have shown that the best values for the pruning parameters are a waiting time of max. 3000 s, a minimal distance of 100 km and a minimal profit of 1 Euro. One future topic for evaluations is the validation of the results with experiments based on real data.


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    Title :

    A data-mining technique for the planning and organization of truck platoons


    Contributors:


    Publication date :

    2008


    Size :

    14 Seiten, 4 Bilder, 4 Tabellen, 12 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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