TrafficFlowAI

v1.2

In this version (v1.2), the following enhancements have been made:

• Added the calculation of the F1-score as an additional evaluation metric to assess the model's performance on both precision and recall.
• No other changes have been made from version 1.0.

v1.0

Traffic Data Analysis and Prediction. This project focuses on analyzing real-world traffic data to understand, model, and predict human driving trajectories. The goal is to contribute to autonomous driving technologies and intelligent transportation research.

Table of Contents

• Project Overview
• Features
• Requirements
• Usage
• Contributing
• License

Project Overview

In this project, we leverage traffic data to gain insights into traffic patterns and develop models for predicting traffic scenarios. We employ machine learning techniques, specifically Support Vector Machines (SVM) and advanced data preprocessing techniques, to analyze and model the traffic data.

The project covers the following main steps:

1. Data Preparation: Preprocessing and cleaning of the traffic dataset.
2. Feature Extraction: Identification and extraction of relevant features from the data.
3. Model Training: Training an SVM model to predict traffic scenarios based on the extracted features.
4. Model Evaluation: Evaluating the performance of the trained model using various metrics.
5. Prediction: Applying the trained model to predict traffic scenarios on new, unseen data.

Features

• Data preprocessing and cleaning to ensure data quality and integrity.
• Feature extraction to identify key factors affecting traffic scenarios.
• Model training using Support Vector Machines (SVM) to predict traffic scenarios.
• Model evaluation using performance metrics such as accuracy, precision, and recall.
• Prediction of traffic scenarios on new, unseen data.

Requirements

• MATLAB (version X.X or higher)
• MATLAB Statistics and Machine Learning Toolbox

Usage

1. Clone the repository to your local machine.
2. Install MATLAB and ensure you have the Statistics and Machine Learning Toolbox installed.
3. Prepare your traffic dataset in CSV format and update the file path in the code.
4. Customize the code as needed, such as data preprocessing steps or model configuration.
5. Run the MATLAB script "traffic_analysis_advanced_v1_0.m" to perform data analysis, modeling, and prediction.
6. Interpret and analyze the results. Additional steps for visualization or further analysis can be added based on your requirements.

Contributing

Contributions to this project are welcome. If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request.

License

This project is licensed under the Apache-2.0 license.