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Journal of Information Science and Engineering, Vol. 29 No. 4, pp. 631-645 (July 2013)


A Hybrid Cross Entropy Algorithm for Solving Dynamic Transit Network Design Problem


TAI-YU MA
Transport Economics Laboratory
University Lyon 2 - CNRS
Lyon, 69007 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 the Sioux Falls network to illustrate the performance of the proposed algorithm.

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

Full Text () Retrieve PDF document (201307_02.pdf)

Received February 5, 2012; accepted June 17, 2012.
Communicated by Toyoaki Nishida.