An interactive Python game that challenges players to predict the maximum traffic flow in a randomly generated network using Edmonds–Karp and Dinic’s algorithms. The project benchmarks algorithm performance and stores results in a normalized SQLite database.
This project combines algorithmic problem-solving, game-based learning, and performance analysis to help users understand maximum flow algorithms in a practical way.
Both algorithms are implemented from scratch, without using external graph libraries.
- BFS-based augmenting path method
- Guaranteed polynomial time complexity
- Tracks flow on each edge
- Constructs a level graph using BFS
- Uses DFS to push blocking flows
- Faster and more scalable than Edmonds–Karp
- Random-capacity directed traffic network (capacities 5–15)
- Player guesses the maximum flow from A → T
- Two independent max-flow algorithms
- Execution time recorded for each algorithm
- Results stored in a normalized SQLite database (3NF)
- Unit tests for correctness verification
- Learn maximum flow algorithms intuitively
- Compare Edmonds–Karp vs Dinic performance
- Practice algorithm benchmarking
- Demonstrates clean database design
- Ideal for CS students and interview preparation
- Computer Science & Software Engineering students
- Algorithm and data-structure learners
- Python developers
- Game-based learning enthusiasts
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
⭐ Support If you find this project useful or interesting, please consider giving it a ⭐ on GitHub. 🔗 Repository https://github.com/gamikapunsisi/Traffic_Simulation