Application of master/slave PGA to dynamic traffic assignment based on optimal control approach

Currently, the optimal method to solve the local congestion problem in transportation networks is route guidance, i.e., traffic assignment, which is a key issue in intelligent transportation system (ITS). Until now, numerous formulations and solutions approaches have been introduced ranging from mathematical programming to variational inequality, optimal control, and simulation-based. Static traffic assignment model has some shortcomings, for example, it's not capable of describing the dynamic traffic flow in urban transportation networks. So, promoted by the research work in ITS, dynamic traffic assignment (DTA) theory became a focus research area. Genetic algorithm (GA), as an excellent global optimization algorithm, has been applied extensively. With the development of high performance computing technology, parallel system and parallel computing theory became another hotspot. Based on above theoretical bases, in this paper we propose an optimal control model of Master/Slave parallel genetic algorithm to solve DTA problem. Based on the Drawing cluster with distributed storage and message passing system, our algorithm is implemented in the Master/Slave mode of the PVM parallel platform. We test our model by applying it on example transportation networks. The experimental results showed excellent system operation indices and parallel efficiency, which proves the validity of our model.