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Iowa State University--Becoming the Best

Artificial Intelligence-Based Optimization of Management of Snow Removal


Principal investigator:

Co-principal investigators:

Project status


Start date: 07/01/00
End date: 05/01/02


Report: Artificial Intelligence-Based Optimization of Management of Snow Removal (1.45 mb pdf) October 2002


Sponsor(s): U.S. Department of Transportation

About the research

Abstract: This report presents the results of research on the development of an intelligent system to integrate a generation of snowplowing routes and the optimization of resource/asset allocation for snow removal. The developed system, known as the snow removal asset management system (SRAMS), is an expert system containing the logical rules and expertise of the Iowa Department of Transportation?s snow removal experts in Black Hawk County, Iowa, and a geographic information system to access and manage road data. The system is implemented on a mid-range PC by integrating MapObjects 2.1 (a geographic information systems package), Visual Rule Studio 2.2 (an artificial intelligence shell), and Visual Basic 6.0 (a programming tool).

The main goal of the study was to build a knowledge-base that allows the Iowa Department of Transportation and other agencies to optimally manage snow removal assets and resources. The SRAMS was designed to be fully interactive and include provisions for entering meteorological observations and field data to refine the snow removal plan. The system is able to run various scenarios and generate prioritized snowplowing routes in visual format, and to optimize the allocation of assets and resources for snow removal. A test of the system reveals an improvement in snowplowing time by 1.9 percent for moderate snowfall and 9.7 percent for snowstorm conditions over the current manual system. Another major benefit of the system is its ability to track inventory of materials such as salt and sand.

This report also documents knowledge acquisition and system design, the algorithms used for optimization, and system validation and field testing. Several appendices with more detailed information are provided at the end of the report.