Computational experience with an application of a simultaneous transportation equilibrium model to urban travel in Austin, Texas

Safwat and Magnanti have developed a combined trip generation, trip distribution, modal split, and traffic assignment model that can predict demand and performance levels on large-scale transportation networks simultaneously, i.e. a simultaneous transportation equilibrium model (STEM). The major objective of this paper is to assess the computational efficiency of the STEM approach when applied to an urban large-scale network, namely the urban transportation system of Austin, Texas. The Austin network consisted of 520 zones, 19214 origin-destination (O-D) pairs, 7096 links and 2137 nodes. The Central Processing Unit (CPU) time on an IBM 4381 mainframe computer was 430 seconds for a typical iteration and about 4734 seconds, less than 79 minutes, to arrive at a reasonably accurate solution in no more than 10 iterations. The computational time at any given iteration is comparable to that of the standard fixed demand traffic assignment procedure. These results encourage further applications of the STEM model to large urban areas.