Abstract
In this paper, we consider dynamic congestion pricing in the presence of demand uncertainty. In particular, we apply a robust optimization (RO) approach based on a bi-level cellular particle swarm optimization (BCPSO) to optimal congestion pricing problems when flows correspond to dynamic user equilibrium on the network of interest. Such a formulation is recognized as a second-best pricing problem, and we refer to it as the dynamic optimal toll problem with equilibrium constraints (DOTPEC). We then present numerical experiments in which BCPSO is compared with two alternative robust dynamic solution approaches: bi-level simulated annealing (BSA) and cutting plane-based simulated annealing (CPSA), as well as a nominal dynamic solution and a robust static solution. We show that robust dynamic solutions improve the worst case, average, and stability of total travel cost in comparison with the nominal dynamic and the robust static solutions. The numerical results also show that BCPSO outperforms BSA and CPSA in terms of solution quality and computational efficiency.
| Original language | English |
|---|---|
| Pages (from-to) | 1504-1518 |
| Number of pages | 15 |
| Journal | Transportation Research Part B: Methodological |
| Volume | 46 |
| Issue number | 10 |
| DOIs | |
| Publication status | Published - 2012 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Transportation
- Management Science and Operations Research
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Dynamic congestion pricing with demand uncertainty : A robust optimization approach. / Chung, Byung Do; Yao, Tao; Friesz, Terry L.; Liu, Hongcheng.
In: Transportation Research Part B: Methodological, Vol. 46, No. 10, 01.01.2012, p. 1504-1518.Research output: Contribution to journal › Article
TY - JOUR
T1 - Dynamic congestion pricing with demand uncertainty
T2 - A robust optimization approach
AU - Chung, Byung Do
AU - Yao, Tao
AU - Friesz, Terry L.
AU - Liu, Hongcheng
PY - 2012/1/1
Y1 - 2012/1/1
N2 - In this paper, we consider dynamic congestion pricing in the presence of demand uncertainty. In particular, we apply a robust optimization (RO) approach based on a bi-level cellular particle swarm optimization (BCPSO) to optimal congestion pricing problems when flows correspond to dynamic user equilibrium on the network of interest. Such a formulation is recognized as a second-best pricing problem, and we refer to it as the dynamic optimal toll problem with equilibrium constraints (DOTPEC). We then present numerical experiments in which BCPSO is compared with two alternative robust dynamic solution approaches: bi-level simulated annealing (BSA) and cutting plane-based simulated annealing (CPSA), as well as a nominal dynamic solution and a robust static solution. We show that robust dynamic solutions improve the worst case, average, and stability of total travel cost in comparison with the nominal dynamic and the robust static solutions. The numerical results also show that BCPSO outperforms BSA and CPSA in terms of solution quality and computational efficiency.
AB - In this paper, we consider dynamic congestion pricing in the presence of demand uncertainty. In particular, we apply a robust optimization (RO) approach based on a bi-level cellular particle swarm optimization (BCPSO) to optimal congestion pricing problems when flows correspond to dynamic user equilibrium on the network of interest. Such a formulation is recognized as a second-best pricing problem, and we refer to it as the dynamic optimal toll problem with equilibrium constraints (DOTPEC). We then present numerical experiments in which BCPSO is compared with two alternative robust dynamic solution approaches: bi-level simulated annealing (BSA) and cutting plane-based simulated annealing (CPSA), as well as a nominal dynamic solution and a robust static solution. We show that robust dynamic solutions improve the worst case, average, and stability of total travel cost in comparison with the nominal dynamic and the robust static solutions. The numerical results also show that BCPSO outperforms BSA and CPSA in terms of solution quality and computational efficiency.
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UR - http://www.scopus.com/inward/citedby.url?scp=84867597274&partnerID=8YFLogxK
U2 - 10.1016/j.trb.2012.07.007
DO - 10.1016/j.trb.2012.07.007
M3 - Article
VL - 46
SP - 1504
EP - 1518
JO - Transportation Research, Series B: Methodological
JF - Transportation Research, Series B: Methodological
SN - 0191-2615
IS - 10
ER -