v0.5.6

UrbanFlow Release Notes - Version v0.5.6

Release Date: 2024-07-28

Overview:

Version v0.5.6 of UrbanFlow focuses on laying the foundational framework for the main hub server, preparing the system for future enhancements. This release includes security improvements and a comprehensive flowchart to clearly illustrate the entire algorithm, setting the stage for upcoming features and functionalities.

Key Features and Improvements:

1. Main Hub Server Framework:

   • Initial Setup: The main hub server has been established to serve as the central point for future system functionalities, allowing for efficient data flow and processing.

2. Algorithm Visualization:

   • Comprehensive Flowchart: A complete flowchart of the UrbanFlow algorithm is introduced, providing a clear and detailed representation of the entire system. This visualization aids in understanding the workflow and serves as a guide for future development.

3. Authentication System:

   • API Key and Token-Based Authentication: Basic implementations of API key generation and token-based authentication have been introduced, setting up the groundwork for secure, programmatic access to the system.

Notable Exclusions:

• Vehicle Route Prediction AI: No updates or modifications were made to the vehicle route prediction AI in this version. Future updates will focus on integrating and enhancing AI models for route prediction.

Commits of Interest

• 74d8577: Restricted register endpoint to LocalHost only.
• 7f52112: Recreated server system using asynchronous DB and enhanced auth with API keys and tokens.
• 065ba31: Restrict user registration to localhost and refine authentication.
• b0ad43b: Enhance authentication and add API endpoints for autonomous access.
• 9ffb563: Update flowchart image to show the entire algorithm.
• 425b337: Add flowchart image.
• 571fa58: Add traffic signals to flowchart.
• c25fba5: Add priority algorithm to flowchart.

Future Plans:

• The next phases will concentrate on implementing the detailed features outlined in the flowchart, optimizing the server capabilities, and integrating machine learning models for improved route prediction.

Conclusion:

Version v0.5.6 establishes the core infrastructure for UrbanFlow's main hub server, alongside improvements in security and algorithm visualization. This release is a preparatory step towards a fully functional and integrated traffic management system.

Full Changelog: https://github.com/hilfing/UrbanFlow/commits/v0.5.6

v0.5.1

Release Notes for v0.5.1

Release Date: 2024-07-23

Overview

This version introduces significant enhancements to the vehicle route prediction system. A total of 12 models were trained and evaluated, focusing on diverse machine learning approaches for predicting vehicle paths. Key improvements include refined data preparation, optimized model training, enhanced logging, and improved code structure for readability and maintainability.

Key Features

1. Data Preparation:

   • Improved data organization into training and testing sets for better model evaluation.
   • Enhanced data cleaning to remove noise and improve prediction accuracy.

2. Model Training:

   • Trained 12 different models, including GRU, Random Forest, and XGBoost, among others.
   • Introduced hyperparameter tuning for each model to optimize performance.
   • Removed redundant LSTM optimizations.

3. Model Evaluation:

   • Comprehensive evaluation using metrics like MSE, RMSE, MAE, R², EVS, and MAPE.
   • GRU, Random Forest, and XGBoost identified as top performers with low error rates.

4. Prediction and Visualization:

   • Visual representation of model predictions compared to actual vehicle routes.
   • Improved visualization for easier comparison of model performance.

5. Codebase Improvements:

   • Restructured code into distinct modules for data handling, models, and evaluation.
   • Added detailed logging and fixed all warnings for better debugging and analysis.
   • Updated documentation with detailed docstrings for clarity.

6. File Management:

   • Added support for Git Large File Storage (LFS) to handle large model files.
   • Updated project structure for improved organization and accessibility.

Commits of Interest

• c33f564: Updated results and finalized changes for the current release.
• a9d9244: Enhanced logging and addressed all code warnings.
• 67e9744: Modularized the codebase into separate data and model evaluation modules.
• ce218fe: Introduced distinct data and hyperparameter modules for better code readability.
• a1d80a6: Version 2 of the models, incorporating full optimization and custom tuning.
• 6d02cd8: Optimization of models with hyperparameter tuning and XGBoost integration.

Conclusion

Version v0.5.1 brings significant advancements in prediction accuracy and system reliability, making the vehicle route prediction models more effective. Future updates will focus on further enhancing model efficiency and expanding functionality.

Full Changelog: https://github.com/hilfing/UrbanFlow/commits/v0.5.1