The effects of model parameter uncertainty on traffic flow control problems have recently drawn research attention. While the uncertainty in fundamental diagram related parameters has been investigated in the past, few articles have focused on network parameters uncertainty, including turning ratio uncertainty. To fill this gap, this article proposes a robust control model to deal with the uncertainties in the turning ratio by using distributionally robust chance constraints. The model allows one to compute the optimal control action that maximizes some objective, under all possible distributions of network parameters. We then apply this robust control framework to both a freeway network and an urban network, and evaluate the impact of uncertainty on optimal control inputs, over the test networks. The case studies show that compared to non-robust control, the proposed robust model can reduce congestion brought by the uncertainties and improve the overall throughput.

A parallel rendering environment is being developed to utilize the IBM Scaleable Graphics Engine (SGE), a hardware frame buffer for parallel computers. Goals of this software development effort include finding efficient ways of producing and displaying graphics generated on IBM SP nodes and of assisting programmers in adapting or creating scientific simulation applications to use the SGE. Four software development phases discussed utilize the SGE: tunneling, SMP rendering, development of an OpenGL API implementation which utilizes the SGE in parallel environments, and additions to the SGE-enabled OpenGL implementation that uses threads. The performance observed in software tests show that programmers would be able to utilize the SGE to output interactive graphics in a parallel environment.