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### Traffic Prediction Using Meta-Learning

#### Abstract

The increasing demand for accurate traffic prediction has necessitated the development of sophisticated models capable of handling the dynamic and complex nature of traffic systems. Traditional methods often struggle with adaptability and generalization across diverse scenarios. This research explores the application of meta-learning, an advanced machine learning paradigm, to enhance traffic prediction accuracy. By employing meta-learning techniques such as model-agnostic meta-learning (MAML) and recurrent neural networks (RNNs), this study proposes a novel framework that improves the adaptability and efficiency of traffic prediction models. The framework is evaluated in various traffic environments, demonstrating its capability to deliver robust and reliable traffic predictions.

#### Introduction

Traffic congestion is a critical issue in urban areas, leading to significant economic losses, environmental pollution, and reduced quality of life. Accur

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