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

TL;DR

This paper introduces a hybrid multiagent learning algorithm combining cross entropy and Hooke-Jeeves methods to optimize transit network frequency, reducing travel and operation costs in a dynamic, simulation-based bilevel problem.

Contribution

It presents a novel hybrid algorithm for dynamic transit network design, integrating multiagent learning with classical optimization techniques, applied to a complex bilevel problem.

Findings

The hybrid algorithm effectively optimizes transit frequencies in the Sioux Falls network.

Results show improved cost minimization compared to traditional methods.

The approach demonstrates robustness in dynamic, simulation-based environments.

Abstract

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.