Optimizing Road Networks: A Graph-Based Analysis with Path-finding and Learning Algorithms

This paper describes a graph-based methodology for analysing and optimising road networks that combines traditional algorithms, AI search, and a learning algorithm. The primary goal is to find the shortest and most efficient paths between vertices in a road network while accounting for variables such as road conditions, length-to-width ratio, and traffic conditions.The study emphasizes the importance of including real-world variables such as road length, width, traffic, and overall road conditions in road network analysis in order to optimize travel time between two points. Taking these factors into account, the paper hopes to identify more accurate and efficient routes, thereby improving traffic flow and transportation planning and management.Dijkstra's algorithm is used to determine the shortest paths through a road network. Furthermore, the A* algorithm and Q-learning, a reinforcement learning technique, are used to optimize routing decisions by considering dynamic factors such as traffic and road conditions. The study's findings demonstrate the effectiveness of the proposed approach. By incorporating Q-learning, the algorithm can adjust its routing decisions over time-based on experience, increasing efficiency and responsiveness to changing traffic conditions. The findings indicate that this approach has the potential to significantly improve transportation system performance while also facilitating better route planning and traffic management decisions. The optimized path in scenario emphasizes the importance of variables like traffic, road conditions, and the length/width ratio. The findings demonstrate that although the A* algorithm finds a route that is comparable to scenario one, the cost rises significantly from 0.452 to 1.5314 as a result of altered traffic and road dynamics. These results show that by combining Dijkstra, A*, and Q-learning algorithms, the hybrid approach can manage complex scenarios and guarantee optimal performance even in a range of conditions.