Blockchain-Based Traffic Management System (Concept Prototype)

Project Overview

This project proposes a forward-thinking approach to traffic management using blockchain and DAO (Decentralized Autonomous Organization) principles. Instead of static, fixed speed limits, this concept introduces a dynamic, collective speed threshold—determined in real-time by the average speed of vehicles on the road.

The current code serves as pseudocode, showcasing the core logic and flow of this concept. It’s designed to illustrate the potential of the system, laying the groundwork for future development.

Core Concepts

• Dynamic Speed Thresholds: Rather than relying on unchanging speed limits, the system would temporarily adjust to match the collective driving speed. For example, if most drivers are moving at around 55 km/h in a 50 km/h zone, the baseline could be adjusted. Fines would only apply to those who greatly exceed this collective threshold, maintaining safety while allowing flexibility.

• Community-Driven Influence: Every driver’s speed contributes to the collective threshold, fostering a real-time, community-driven system that promotes road safety while aligning with actual driving conditions.

• Security and Transparency with Blockchain: By leveraging blockchain, data remains encrypted and immutable, ensuring no entity can alter or misuse speed data. This provides both drivers and authorities with a secure, reliable traffic management system.

Project Structure

Getting Started

To explore this concept further, clone the repository and run main.py to simulate the system’s core functionality. The code will simulate recording car speeds, calculate the day’s average speed, and list violators based on the collective speed threshold.

Note: This project remains a prototype focused on conceptual logic, without full blockchain or database functionality.

Future Development Goals

• Implement a blockchain-based storage and encryption mechanism.
• Investigate the feasibility of DAO-driven governance, allowing community input on traffic rules and thresholds.
• Introduce machine learning to improve traffic safety by predicting congestion patterns or potential hazards.

Contributing

This project is open to contributions! If you have ideas, please feel free to submit a pull request or open an issue to discuss potential enhancements or new features.