Name
Abstract | ||
|---|---|---|
AbstractIn 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1287/trsc.2019.0910 | Periodicals |
Keywords | Field | DocType |
Markovian traffic equilibrium,distributionally robust,convex optimization | Mathematical optimization,Markov process,Traffic equilibrium,Mathematics | Journal |
Volume | Issue | ISSN |
53 | 6 | 1526-5447 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Selin Damla Ahipaşaoğlu | 1 | 29 | 5.50 |
| Ugur Arikan | 2 | 0 | 0.34 |
| Karthik Natarajan | 3 | 407 | 31.52 |