This paper reports on a successful determination of the train travel schedule parameters for a rail system based on limited data, and thus provides a verification of the ASCAP, a rail system simulator developed at the Center of Rail Safety-Critical Excellence at the University of Virginia. The train system considered is a corridor encompassing a territory of over 127 miles. It is divided into 37 train speed zones, with 9 sidings. The only data available are the actual trip times of 171 trains dispatched over a period of 14 days. The problem of determining the 37 train-zone-average-speeds and 9 siding delay times was formulated as a constrained optimization problem. The cost to be minimized is the cumulated errors between the actual train trip times and the ASCAP simulated trip times resulting from a particular set of train-zone-average-speeds and siding delay times. The constraints include allowable siding delays, permissible train-zone-average-speeds and prohibition of southbound trains from entering the sidings. This large scale nonlinear optimization problem was then solved by a genetic algorithm developed by the authors and referred to as the intelligent genetic algorithm. Simulation results demonstrate the effectiveness of our approach.


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    Title :

    ASCAP parameter determination by an intelligent genetic algorithm


    Contributors:
    Weidong Ruan, (author) / Giras, T.C. (author) / Zongli Lin, (author) / Yong Ou, (author)


    Publication date :

    2003-01-01


    Size :

    556980 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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