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Introduction

Real-time traffic prediction using big data has become a crucial area of research in the field of transportation engineering. With the increasing complexity and volume of traffic data generated from various sources such as GPS devices, traffic cameras, and sensors embedded in roads, there is a growing need for advanced techniques to accurately predict traffic conditions in real-time. This thesis aims to explore the use of big data analytics for real-time traffic prediction and provide insights into how these techniques can be utilized to improve traffic management and reduce congestion on road networks.

1.1 Introduction

1.2 Background of the Study

1.3 Problem Statement

1.4 Objective of the Study

1.5 Limitation of the Study

1.6 Scope of the Study

1.7 Significance of the Study

1.8 Structure of the Thesis

1.9 Definition of Terms

Chapter Two: Literature Review

2.1 Overview of Traffic Prediction

2.2 Big Data Analytics in Transportation

2.3 Machine Learning Techniques for Traffic Prediction

2.4 Real-Time Traffic Prediction Models

2.5 Challenges in Real-Time Traffic Prediction

2.6 Previous Studies on Real-Time Traffic Prediction

2.7 Current Trends in Big Data Analytics for Traffic Prediction

2.8 Potential Applications of Real-Time Traffic Prediction

2.9 Evaluation Metrics for Traffic Prediction Models

2.10 Summary of Literature Review

Chapter Three: Research Methodology

3.1 Data Collection and Preprocessing

3.2 Feature Selection and Engineering

3.3 Model Selection and Training

3.4 Evaluation of Prediction Models

3.5 Performance Metrics

3.6 Experimental Setup

3.7 Validation Techniques

3.8 Data Visualization Techniques

Chapter Four: Discussion of Findings

4.1 Analysis of Prediction Models

4.2 Comparison of Different Techniques

4.3 Impact of Data Preprocessing on Model Performance

4.4 Interpretation of Results

4.5 Insights into Real-Time Traffic Prediction

4.6 Future Research Directions

4.7 Recommendations for Practical Implementation

Chapter Five: Conclusion and Summary

5.1 Summary of Key Findings

5.2 Contributions to the Field

5.3 Implications for Traffic Management

5.4 Limitations of the Study

5.5 Future Research Directions

Thesis Overview on Real-Time Traffic Prediction Using Big Data

The thesis on Real-Time Traffic Prediction Using Big Data aims to address the increasing demand for advanced techniques to accurately predict traffic conditions in real-time by utilizing big data analytics. The introduction provides an overview of the background, problem statement, objectives, limitations, scope, significance of the study, structure of the thesis, and definition of key terms. The literature review explores the current state of traffic prediction, big data analytics in transportation, machine learning techniques, real-time traffic prediction models, challenges, previous studies, trends, applications, and evaluation metrics. The research methodology details the data collection, preprocessing, feature selection, model training, evaluation, performance metrics, experimental setup, and validation techniques. The discussion of findings includes the analysis of prediction models, comparison of techniques, impact of data preprocessing, interpretation of results, insights, future directions, and recommendations. The conclusion and summary highlight the key findings, contributions, implications for traffic management, limitations, and future research directions. This thesis aims to contribute to the field of transportation engineering by providing insights into the use of big data analytics for real-time traffic prediction and offering recommendations for practical implementation.

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