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Journals / European Journal of Technique (EJT) / 2023 / Cilt: 13 Sayı: 2

Capacitated Network Traffic Assignment using Lagrange Neural Networks

Hasan Dalman(BATMAN ÜNİVERSİTESİ)0009-0008-6574-3215
Pages
81–87
Publication Date
2023-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The effectiveness of the proposed neural network approach is demonstrated through a numerical example.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, we utilize a neural network methodology to obtain user equilibrium for network traffic assignment problems with capacity constraints.
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Key Findings & Data:The optimization problem associated with network traffic assignment is first transformed into a Lagrange problem.
💡
Conclusion & Impact:The effectiveness of the proposed neural network approach is demonstrated through a numerical example.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Nonlinear Optimization · Neural Dynamics · Traffic assignment

Abstract

In this study, we utilize a neural network methodology to obtain user equilibrium for network traffic assignment problems with capacity constraints. The optimization problem associated with network traffic assignment is first transformed into a Lagrange problem. By considering the gradient method, a system of differential equations is obtained. Subsequently, the system of differential equations is solved using the Runge-Kutta method. The effectiveness of the proposed neural network approach is demonstrated through a numerical example.

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Cite This Article

Dalman, H. (2023). Capacitated Network Traffic Assignment using Lagrange Neural Networks. European Journal of Technique (EJT), Cilt: 13 Sayı: 2, 81–87. https://doi.org/10.36222/ejt.1320824

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