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TAI-YU MA

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Transport Economics Laboratory
University Lyon 2 - CNRS
Lyon, 69007 France
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This paper proposes a hybrid multiagent learning algorithm for solving the dynamic simulation-based bilevel network design problem. The objective is to determine the optimal 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 performance of the proposed algorithm.

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Keywords:
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multiagent, learning, network design, transit system, simulation

Retrieve PDF document (**201307_02.pdf**)

Received February 5, 2012; accepted June 17, 2012.

Communicated by Toyoaki Nishida.