Distributionally Robust Markovian Traffic Equilibrium

In a Markovian traffic equilibrium model, users move toward their destinations by a sequence of successive link choices using a discrete choice model at each node, taking congestion into account. Although a convex optimization formulation is available to compute the equilibrium flows for a continuous distribution of link utilities, practical applications have thus far been mainly restricted to the multinomial logit model and its variants. In this paper, we relax the assumption of a complete joint distribution of link utilities to only knowledge on the marginal distributions and propose a new convex optimization formulation for a distributionally robust Markovian traffic equilibrium. The formulation is provably efficiently solvable and has the flexibility of allowing for general marginal distributions, thus capturing different types of nonidentical, skewed, and heavy-tailed distributions at the link level.