A dynamic route assignment model for guided and unguided vehicles with a massively parallel computing architecture

Abstract

This article presents a dynamic assignment model which has been developed primarily for ATMS/ATIS real-time applications. In order to satisfy the real-time computational requirements, the proposed model has been developed on the Connection Machine, CM-2, a massively parallel computer. Its implementation on the Connection Machine is aimed to exploit the parallel nature of the problem and to take advantage of the underlying computing architecture. The model follows an integrated assignment-simulation framework which assigns both guided and unguided vehicles to the network dynamically in both spatial and temporal dimensions. It uses a learning process in which the new route assignment uses information gained from previous iteration in computation of new paths. During each iteration, guided vehicles follow routes according to time-dependent shortest paths while unguided vehicles follow static shortest paths. Several numerical examples have been carried out to illustrate the potential applications in analyzing the network performance under various ATMS scenarios.