A hybrid cross entropy algorithm for solving dynamic transit network design problem

Tai-Yu Ma (1)

1 LET - Laboratoire d'économie des transports

This paper proposes a hybrid multiagent learning algorithm for solving the dynamic simulation-based bilevel network design problem. The objective is to determine the op-timal frequency of a multimodal transit network, which minimizes total users' travel cost and operation cost of transit lines. The problem is formulated as a bilevel programming problem with equilibrium constraints describing non-cooperative Nash equilibrium in a dynamic simulation-based transit assignment context. A hybrid algorithm combing the cross entropy multiagent learning algorithm and Hooke-Jeeves algorithm is proposed. Computational results are provided on the Sioux Falls network to illustrate the perform-ance of the proposed algorithm.

Mots clés: multiagent; learning; network design; transit system; simulation

Domaines: Intelligence artificielle [cs.AI]

Tai-Yu Ma. A hybrid cross entropy algorithm for solving dynamic transit network design problem. 2012. ⟨hal-00755983⟩