Parallel implementations of Dynamic Traffic Assignment models

Dynamic traffic management methods constitute the intelligent core of Intelligent Transportation Systems (ITS). In order for these methods to be effective and deployable in real-time, there is a need to develop models that predict future traffic conditions in a computational time much less than real time. In this paper we report on parallel implementations for a class of Dynamic Traffic Assignment (DTA) models, known as macroscopic DTA models. This class of models possess mathematical formulations which are solved using various algorithms. Two parallel decomposition strategies based on network topology and time are investigated and implemented in a distributed memory environment. Numerical results show that for the network topology based decomposition strategy, a speed-up of 5 is observed when the number of processors is 10 and the asymptotic speed-up is about 10. For the time-based decomposition strategy a speed-up of 6.5 is observed when the number of processors is 10 and the asymptotic speed-up is about 25.