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A Hybrid Multiagent Learning Algorithm for Solving the Dynamic Simulation-Based Continuous Transit Network Design Problem

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1 Author(s)
Tai-Yu Ma ; Transp. Econ. Lab., Univ. Lyon, Lyon, France

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 a small network to illustrate the performance of the proposed algorithm.

Published in:

Technologies and Applications of Artificial Intelligence (TAAI), 2011 International Conference on

Date of Conference:

11-13 Nov. 2011

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