  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.
