Corresponding Author : Mansi

Co-Authors : Dr. Ritika Chopra

Abstract :

Traffic management in cities has become extremely complex due to rapid urbanization, higher number of vehicles, and increased network of roads. Traditional methods that control traffic using fixed algorithms and scheduling models fail to adapt to changes in traffic. Although neural networks can predict and take decisions within intelligent traffic management systems, their unpredictability makes it difficult for them to be employed in situations when safety plays a critical role. This paper proposes the mathematical model for modeling neural networks based on quivers, where layers would be the vertices while connections would be the directed edges of the quiver, with a weight. This mathematical model provides a clear picture regarding how information flows through neural networks. The current research demonstrates the application of our method for classification of data and its stability. The reason for this is that in order to prove it, this research uses a feature contribution analysis of the inputs to the output as well as the stability analysis using the spectral norm to find out whether the model reacts to any perturbation in the input and parameters. The proposed method provides the researchers with a mathematical representation of the functioning of neural networks. The proposed framework is evaluated on the German Traffic Sign Recognition Benchmark (GTSRB). In the future, it is planned to generalize our framework for other network structures and learning algorithms.

Keywords :

Stability Analysis, Feature Contribution Analysis, Linear Transformations, Vector Spaces

Article Details

Unique Paper ID: 61007

Publication Volume & Issue: VOLUME 6 - 2026, ISSUE 1

Page(s): 69-80

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