We extend a `surrogate problem' approach that is developed for a class of stochastic discrete optimization problems so as to tackle the global signal settings and traffic assignment combined problem. We compare a stochastic method based on the surrogate approach, called Surrogate Method (SM), with a Projected Gradient Algorithm (PGA), which uses the Armijo rule for the step size estimation routine. Numerical experiments conducted on a test network show that the surrogate method converges to a really small area and it finds much more efficient solutions.
Adacher, L., Cipriani, E. (2010). A surrogate approach for the global optimization of signal settings and traffic assignment problem. In IEEE Conference on Intelligent Transportation Systems, 13th IEEE ITSC, 2010 (ISSN: 2153-0009) (pp.60-65) [10.1109/ITSC.2010.5625295].
A surrogate approach for the global optimization of signal settings and traffic assignment problem
ADACHER, LUDOVICA;CIPRIANI, ERNESTO
2010-01-01
Abstract
We extend a `surrogate problem' approach that is developed for a class of stochastic discrete optimization problems so as to tackle the global signal settings and traffic assignment combined problem. We compare a stochastic method based on the surrogate approach, called Surrogate Method (SM), with a Projected Gradient Algorithm (PGA), which uses the Armijo rule for the step size estimation routine. Numerical experiments conducted on a test network show that the surrogate method converges to a really small area and it finds much more efficient solutions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
