ASCAP parameter determination by an intelligent genetic algorithm

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.