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Introduction

Traffic congestion is a major issue faced by metropolitan cities around the world, leading to wasted time, increased carbon emissions, and decreased productivity. Smart city planning aims to utilize technology to optimize urban systems, including transportation networks. In recent years, the integration of deep learning algorithms and IoT sensor data has shown promising results in predicting traffic flow patterns, allowing for more efficient traffic management and city planning.

1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Introduction to Traffic flow prediction
2.2 Deep learning algorithms for traffic prediction
2.3 IoT sensor data in transportation
2.4 Smart city planning for traffic management
2.5 Previous studies on traffic flow prediction
2.6 Challenges in traffic flow prediction
2.7 Integration of deep learning and IoT in city planning
2.8 Benefits of accurate traffic flow prediction
2.9 Comparison of different traffic prediction models
2.10 Future trends in traffic flow prediction

Chapter 3: Research Methodology
3.1 Introduction
3.2 Data collection process
3.3 Data preprocessing techniques
3.4 Feature selection and engineering
3.5 Deep learning model selection
3.6 Training and evaluation process
3.7 Performance metrics
3.8 Validation techniques

Chapter 4: Discussion of Findings
4.1 Overview of data analysis results
4.2 Comparison of different deep learning models
4.3 Impact of IoT sensor data on prediction accuracy
4.4 Real-world implications for smart city planning
4.5 Limitations and future research directions

Chapter 5: Conclusion
5.1 Summary of findings
5.2 Implications for smart city planning
5.3 Recommendations for policymakers
5.4 Concluding remarks

Thesis Overview:
Traffic flow prediction using deep learning and IoT sensor data has emerged as a crucial component in smart city planning. By accurately forecasting traffic patterns, city planners can optimize transportation networks, reduce congestion, and improve overall urban sustainability. This thesis aims to investigate the efficacy of deep learning algorithms in predicting traffic flow, utilizing data collected from IoT sensors.

The literature review will provide a comprehensive overview of previous studies on traffic flow prediction, deep learning algorithms, IoT sensor data, and their applications in smart city planning. The research methodology will outline the data collection process, preprocessing techniques, model selection, and evaluation metrics used in the study.

The discussion of findings will present the results of the data analysis, comparing the performance of different deep learning models and evaluating the impact of IoT sensor data on prediction accuracy. The conclusion will summarize the key findings, discuss their implications for smart city planning, and provide recommendations for future research in this area.

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