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Introduction:

Graph Neural Networks (GNNs) have gained popularity in recent years for their ability to effectively model graph-structured data. One of the emerging applications of GNNs is in traffic prediction, where they can be used to forecast future traffic conditions based on historical data. By leveraging the spatial and temporal relationships inherent in traffic networks, GNNs have the potential to outperform traditional forecasting methods. This thesis aims to explore the use of GNNs for traffic prediction and evaluate their performance in comparison to existing approaches.

Table of Contents:

Chapter 1: Introduction
1.1 Background
1.2 Problem Statement
1.3 Objectives of the Study
1.4 Limitations of the Study
1.5 Scope of the Study

Chapter 2: Literature Review
2.1 Overview of Traffic Prediction
2.2 Traditional Forecasting Models
2.3 Graph Neural Networks
2.4 Applications of GNNs in Traffic Prediction
2.5 Comparative Analysis of Forecasting Methods

Chapter 3: Research Methodology
3.1 Data Collection and Preprocessing
3.2 Graph Construction
3.3 GNN Model Architecture
3.4 Training and Evaluation
3.5 Performance Metrics

Chapter 4: Discussion of Findings
4.1 Experimental Results
4.2 Analysis of Model Performance
4.3 Interpretation of Results
4.4 Implications of Findings

Chapter 5: Conclusion and Summary
5.1 Summary of Key Findings
5.2 Contributions of the Study
5.3 Recommendations for Future Research
5.4 Conclusion

Thesis Overview:

Traffic prediction plays a crucial role in transportation planning and management, as accurate forecasts can help reduce congestion, improve safety, and optimize resource allocation. Traditional forecasting methods often rely on statistical models or machine learning algorithms, which may struggle to capture the complex spatial and temporal relationships present in traffic networks. Graph Neural Networks (GNNs) offer a promising alternative by leveraging the inherent structure of traffic data to make more accurate predictions.

This thesis explores the application of GNNs for traffic prediction, with a focus on evaluating their performance in comparison to traditional forecasting methods. The study aims to address the following objectives: (1) to investigate the effectiveness of GNNs in capturing spatial and temporal dependencies in traffic data, (2) to compare the predictive accuracy of GNNs against existing forecasting models, and (3) to identify the limitations and potential applications of GNNs in traffic prediction.

The research methodology involves collecting and preprocessing traffic data, constructing a graph representation of the network, designing and training a GNN model, and evaluating its performance using various metrics. The findings from the study are discussed in detail, including an analysis of the experimental results, interpretation of model performance, and implications for future research in the field of traffic prediction.

Overall, this thesis aims to contribute to the growing body of literature on GNNs and their applications in traffic forecasting. By providing a comprehensive evaluation of GNNs for traffic prediction, this study seeks to shed light on the potential benefits and challenges of using this innovative approach in transportation planning and management.

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