In our project, we focused on improving the efficiency and management of a four-way traffic intersection using an advanced AI algorithm. We identified the challenge posed by variable and unpredictable traffic patterns from each road, which sometimes lead to congestion and inefficiency in the existing systems. To address this problem, we applied the Monte Carlo Tree Search (MCTS) algorithm to develop a smart traffic control system.
Our system dynamically controls the traffic lights at the intersection by making intelligent decisions about when to switch lights based on real-time observations. MCTS, a proven decision-making algorithm, enables our artificial intelligence model to balance efficiently between exploration of different traffic flow options and exploitation of known strategies.
By simulating many traffic scenarios and iteratively refining its decision-making process, our traffic control system, based on artificial intelligence, optimizes the traffic flow, reduces congestion and minimizes waiting times.
Through our project, we demonstrated the potential of artificial intelligence-driven solutions in improving urban traffic management. Our smart traffic control system showcases the successful integration of advanced technology to create a more efficient and responsive traffic intersection, which ultimately contributes to a smarter and more sustainable urban environment.