Robust Dynamic Traffic Assignment under Demand and Capacity Uncertainty

In this note we discuss the system optimum dynamic traffic assignment (SO-DTA) problem in the presence of time-dependent uncertainty on the demands and link capacities. We start from the deterministic linear programming formulation of the SO-DTA problem introduced by Ziliaskopoulos in [14], and then consider a robust version of the decision problem where the total travel time must be minimized under a worst-case scenario of demand and capacity configurations. When uncertain demands and capacities are modeled as unknown-but-bounded quantities restricted in intervals, the resulting robust decision problem can still be formulated as a linear program and solved at the same computational cost as its nominal counterpart. Worst-case solutions to assignment problems appear to be useful for establishing routing policies that are resilient to possibly large variations of network parameters and for computing lower bounds on network performance in extreme situations.