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A Bio-Inspired Approach to Traffic Network Equilibrium Assignment Problem.

Xiaoge Zhang, Sankaran Mahadevan

    IEEE Transactions on Cybernetics
    |April 20, 2017
    PubMed
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    This study introduces a novel slime mold-inspired algorithm for solving traffic assignment problems. The Physarum solver efficiently finds user-optimized equilibrium in transportation networks, outperforming existing methods.

    Area of Science:

    • Computational Mathematics
    • Transportation Engineering
    • Network Science

    Background:

    • Traffic assignment is crucial for transportation network design, aiming to find equilibrium states.
    • Existing methods for traffic equilibrium assignment can be computationally intensive and complex.

    Purpose of the Study:

    • To adapt the Physarum polycephalum mathematical model for solving the traffic equilibrium assignment problem.
    • To develop a generalized Physarum model for shortest path problems in directed, asymmetric graphs.
    • To extend the model for network design with multiple sources and sinks.

    Main Methods:

    • A generalized slime mold (Physarum polycephalum) mathematical model was developed.
    • The model was extended to handle network design problems with multiple source and sink nodes.

    Related Experiment Videos

  • Dynamic updating of link costs was employed to achieve user-optimized (Wardrop) equilibrium.

    • Main Results:

    • The proposed Physarum solver converges to the user-optimized equilibrium.
    • Convergence of the developed algorithm is mathematically proven.
    • Numerical examples demonstrate the algorithm's efficiency and superiority over Frank-Wolfe and gradient projection algorithms.

    Conclusions:

    • The Physarum-based approach offers an efficient and effective method for traffic equilibrium assignment.
    • The generalized model successfully addresses shortest path and network design challenges.
    • This biologically inspired algorithm presents a promising alternative to traditional traffic assignment methods.