Parameter determination for a rail system simulator by an intelligent genetic algorithm

Abstract

Abstract

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 axiomatic safety-critical assessment process (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 nine 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-speed-zone average speeds and nine siding delay times was formulated as a constrained optimization problem. The cost to be minimized is the accumulated errors between the actual train trip times and the ASCAP simulated trip times resulting from a particular set of train-speed-zone average speeds and siding delay times. The constraints include allowable siding delays, permissible train-speed-zone average speeds and prohibition of southbound trains from entering the sidings. This large-scale non-linear optimization problem was then solved by a genetic algorithm developed by the present authors and referred to as the intelligent genetic algorithm. Simulation results demonstrate the effectiveness of this intelligent genetic algorithm.