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

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