The assessment of real-time intelligent transportation system (ITS) applications, such as traffic management and adaptive route guidance systems, requires the use of fast and near real-time dynamic traffic simulation models. Even off-line applications, used for testing planning scenarios, often require fast-enough traffic simulation models that enable the required repetitive simulations. This is even more critical for large-scale networks with millions of vehicles. This paper investigates the speedup of DTA simulation models, using compiler optimizations and parallelism. DynusT as a widely used DTA model was evaluated as a test case, while its results could be generalized because we have used real-networks and calibrated them using real data sets in the Greater Toronto and Hamilton Area (GTHA). Extensive testing is performed to evaluate various dimensions for speed-up including: network size, number of processors, various optimization levels and operating systems. The performance results show that compiler optimizations and parallelism allow to: 1) double the speed required for a 4-hour simulation after 12 iterations to reach equilibrium, and 2) bring down the initial simulation time (required for network loading) by 2.5 times, enabling the testing of various real-time ITS applications.