/mta-gtfs-rl-irl

This project explores the application of Reinforcement Learning (RL) and Inverse Reinforcement Learning (IRL) to optimize bus transit schedules using real-world transit data. The primary objective is to develop a predictive model that can generate efficient bus schedules, reduce delays, and improve the reliability of public transportation services.

Primary LanguageJupyter Notebook

Stargazers