This project explores the development and optimization of algorithms for matching riders with passengers in a ride-sharing platform. The objective is to create an efficient, dynamic matching system that optimizes for factors such as pickup and drop-off locations, time preferences, and overall travel efficiency.
Algorithm Iterations: The project involves three iterations of algorithm development, starting from a basic assignment approach to more advanced graph-based matching techniques.
Dynamic Optimization: The final solution employs state-of-the-art graph theory algorithms to find the optimal matches, minimizing total travel distance and time deviations.
Data Visualization: To illustrate the algorithm's effectiveness, Visual representations of the matching process, including graph-based visualizations of rider-passenger connections, are provided.
Complexity Handling: The project handles increasing complexity by scaling the number of riders and passengers, ensuring the algorithm's robustness and scalability.
The project is implemented in Python, with all code and documentation provided in Jupyter notebooks for clarity and reproducibility.