Intelligent Route Optimization Using Reinforcement Learning

Abstract

Route Optimization plays a crucial role in modern transportation,with the increasing demand for efficient and intelligent transportation systems, traditional route optimization methods cannot dynamically react to real-time traffic conditions. This paper proposes an Intelligent Route Optimization System using Reinforcement Learning (RL) to enhance travel efficiency by dynamically selecting the optimal route in real-time conditions. The system utilizes Deep Q-Networks (DQN) to monitor multiple route parameters, including traffic density, road condition, and estimated travel time, in order to make intelligent routing decisions. The model learns in real-time from the environment and enhances accuracy and responsiveness. Developed using Python, TensorFlow, and OpenAI Gym, the system demonstrates significant travel time and congestion compared to traditional shortest-path algorithms. Our experiments show that RL-based route optimization offers excellent adaptability, suggesting that it can be an excellent solution for advanced intelligent transportation systems. Additionally, the approach with the minimized fuel consumption and emissions by optimal routing provides sustainable urban mobility. The real-time learning ability of the system guarantees that it continues to adapt to real-time traffic conditions, weather, and road closures. The proposed framework can be integrated into smart city infrastructures to help urban planners and traffic management authorities make overall transportation more efficient. The predictive and adaptive nature to traffic anomalies further enhances the resilience of the system, and it is an ideal substitute for static navigation algorithms. Future research includes integrating multi-agent RL models with vehicular networking systems to improve collaborative decision-making of vehicles