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Distributed-information neural control: the case of dynamic routing in traffic networks
M Baglietto1, T Parisini, R Zoppoli
1Department of Communications, Computer and System Sciences, DIST-University of Genoa, 16145 Genova, Italy. mbaglietto@dist.unige.it
IEEE Transactions on Neural Networks
|February 6, 2008
Summary
This study introduces a novel method for dynamic routing in traffic networks using feedforward neural networks. The approach optimizes traffic flow by enabling local decision-making and cooperation to minimize network costs.
Area of Science:
- Control Engineering
- Network Science
- Operations Research
Background:
- Traffic networks are complex systems with dynamic flows and capacity constraints.
- Decentralized decision-making based on local information is a realistic assumption for network nodes.
- Minimizing total traffic cost is a common objective in distributed network organizations.
Purpose of the Study:
- To address the dynamic routing problem in large-scale traffic networks.
- To develop an approximate method for team optimal control in traffic networks.
- To utilize feedforward neural networks for local routing function optimization.
Main Methods:
- Modeling traffic networks as graphs with nodes and capacity-limited links.
- Applying team organization principles for cooperative decision-making.
- Employing feedforward neural networks as fixed-structure routing functions with optimized parameters.
- Focusing on store-and-forward packet switching networks as a case study.
Main Results:
- Demonstrated the effectiveness of the proposed approximate resolutive method.
- Showcased the capability of feedforward neural networks for local routing function computation and adaptation.
- Validated the approach through simulations on complex communication networks.
Conclusions:
- The proposed method provides an effective solution for dynamic routing in traffic networks.
- Local computation and adaptation of routing functions using neural networks are feasible.
- The approach successfully minimizes total traffic cost in distributed network environments.
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