A user‐optimum route navigation model with a massively parallel computing architecture

Abstract

This paper presents a user‐optimum route assignment model which has been developed primarily for ATMS/ ATIS real‐time applications. The model uses a learning process in which the new route assignment uses information gained from previous iterations in the computation of new paths. During each iteration, the vehicle equipped with in‐vehicle navigation systems are assigned routes according to time‐dependent shortest paths. These assigned paths will be used to predict future traffic flow patterns and link trip times. Differing from existing assignment‐simulation model frameworks which require an external simulation model to estimate link travel time, the proposed model consists of a simulation‐like loading mechanism to estimate link trip time so as to take into account signal control and queues. Furthermore, the route assignment model for a massively parallel computer has been developed for real‐time applications. Several numerical examples have been carried out to examine the properties of the proposed model and to compare the running time of the parallel model and the sequential model. The results indicate that the proposed model converges to a stable loading pattern. Other results show that the assignment based on time‐dependent shortest paths performs better than using static shortest paths, which indicates the benefit of a “look ahead” feature. Finally, the parallel model significantly outperforms the sequential model in the computing speed especially in a larger network.