An Analysis of Informed Search Algorithms Applied on Road Networks

Pathfinding is a complex problem with high computational costs. Yet this process is frequently used and requires high accuracy. Therefore, it is important to be able to find a highly optimal path between two points with the smallest amount of work. In this paper, we present an analysis of informed search algorithms (with an emphasis on weighted A* search) applied to pathfinding on road networks. We compare these different informed search algorithms by using randomly generated maps that emulate road networks of cities with varying road conditions like road density, speed limits, number of roads between two points, and more. After gathering all the data and statistics on the performance of these different pathfinding methods, we review the results to infer which graph search algorithms perform best in specific scenarios and in general. Based on the simulation results, we concluded that the Weighted A* search had the best performance with extremely optimal paths and low computational cost.