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Snow-plow route planning using AI search

Snow plowing comprises a major portion of the total municipal budget in many communities. In these times of severe budget stress, it is vital to find ways to perform this essential service in an efficient manner. Optimizing the routes travelled by plows is one way to reduce costs. This problem, however, can be shown to be an NP-Hard problem at its core, with several additional complications. In this paper, we demonstrate how this problem can be formulated as a state-space search problem, and how one can employ AI techniques such as A-Star (A*) search to compute optimal or close-to-optimal routes. Specifically, we have developed A*-SnowPlowRouter (A*SPR), a Java software application that uses a “roads data set” of a town and generates efficient route plans. This data set is generally obtained from the municipal Geographical Information System (GIS). Our procedure transforms this data into a directed graph representation, augmented with road priority data. We then employ the A* technique to generate routes which minimize travel distance, avoid U-Turns, and reach higher-priority roads before lower-priority roads. The route plans computed by A*SPR are then exported into a format that can be displayed on the GIS. We have experimented with road networks in several local municipalities, and obtained routes that are significantly better than the ones currently used.