An Improved A* Search Algorithm for Road Networks Using New Heuristic Estimation

Finding the shortest path between two points in a graph is a fundamental problem that has been well-studied over the past several decades. Shortest path algorithms are commonly applied to modern navigation systems, so our study aims to improve the efficiency of an existing algorithm on large-scale Euclidean networks. The current literature lacks a deep understanding of certain algorithms' performance on these types of networks. Therefore, we incorporate a new heuristic function, called the $k$-step look-ahead, into the A* search algorithm and conduct a computational experiment to evaluate and compare the results on road networks of varying sizes. Our main findings are that this new heuristic yields a significant improvement in runtime, particularly for larger networks when compared to standard A*, as well as that a higher value of $k$ is needed to achieve optimal efficiency as network size increases. Future research can build upon this work by implementing a program that automatically chooses an optimal $k$ value given an input network. The results of this study can be applied to GPS routing technologies or other navigation devices to speed up the time needed to find the shortest path from an origin to a destination, an essential objective in daily life.