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.
ASCAP parameter determination by an intelligent genetic algorithm
2003-01-01
556980 byte
Conference paper
Electronic Resource
English
ASCAP Parameter Determination by an Intelligent Genetic Algorithm [IEEE]
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