Machine Learning-Based Route Optimization for Smart Urban Transportation Systems

Urban transportation systems face increasing challenges due to rapid population growth, traffic congestion, and unpredictable road conditions. Traditional routing algorithms like Dijkstra and A* are limited in their ability to respond to real-time events such as accidents, roadwork, or weather disruptions. This study aims to develop a smarter, more adaptive route optimization system using machine learning techniques. The goal is to enhance travel time accuracy, reduce congestion, and improve commuter satisfaction through intelligent, data-driven decision-making. The proposed method integrates supervised learning for travel time prediction and reinforcement learning for real-time route selection, using data from GPS trajectories, traffic flow, weather reports, and user behaviors. A grid-based environment is used for reinforcement learning simulations, while OpenStreetMap data supports city-level route optimization. Results show that the machine learning-enhanced model significantly outperforms traditional algorithms in terms of adaptability, responsiveness, and reliability. In particular, reinforcement learning proved effective in dynamic environments, learning optimal routes over time and adjusting to disruptions. This research contributes to the development of intelligent transportation systems and supports the broader vision of smart cities, where mobility is safer, faster, and more efficient through the power of AI and real-time data integration.