Metro Route Optimization for Urban Public Transportation Using Q-Learning

Nowadays, efficient public transport is crucial, particularly in those where pollution and traffic congestion are significant issues. This study introduces a great approach to using the learning algorithm like Q-learning for finding the best route for passengers. Metro networks, which link many towns and areas, are essential to the survival of many cities. We present an approach that treats the whole metro map as a graph with edges connecting nodes. We enable the system to learn the most efficient path for passengers based on the rewards and punishments associated with different behaviors through the use of a Q-learning algorithm, which is also a reinforcement learning algorithm. As we know, this learning is stateless, rule-free, and feedback-based, as opposed to traditional routing techniques; it can learn the best rules for urban routing. The goal is to shorten the overall time taken to travel by using fewer stops. Through simulations and use of real-time data, we demonstrate the effectiveness of our method for determining the best subway routes, helping to deliver better public transportation and availability for commuters in urban environments.