Details:

Creators:
Salim, M. D. ; Timmerman, Marc A. ; Strauss, Tim ; Emch, Michael E. ;
Salim, M. D. ; Timmerman, Marc A. ; Strauss, Tim ; Emch, Michael E. ; Less -
Corporate Creators:
University of Northern Iowa ;
Corporate Contributors:
United States. Dept. of Transportation. Research and Special Programs Administration ; University Transportation Centers Program (U.S.) ;
United States. Dept. of Transportation. Research and Special Programs Administration ; University Transportation Centers Program (U.S.) ; Less -
Subject/TRT Terms:
[+]
Artificial Intelligence
Asset Management
Geographic Information Systems
Heuristic Algorithms
Knowledge Engineering
Snow Removal
Computer Algorithms
Knowledge Acquisition (Expert Systems)
Optimization
Snow
Snow And Ice Control

Publication/ Report Number:
G/C No. Y00-162 ;
Resource Type:
Tech Report ;
Geographical Coverage:
Iowa ;
Edition:
Final report; June 2000¿Sept. 2002.
Corporate Publisher:
Midwest Transportation Consortium ;
NTL Classification:
NTL-ENERGY AND ENVIRONMENT-Environment Impacts ; NTL-GEOGRAPHIC INFORMATION SYSTEMS-GEOGRAPHIC INFORMATION SYSTEMS ; NTL-SAFETY AND SECURITY-Highway Safety ; NTL-PLANNING AND POLICY-PLANNING AND POLICY ;
Abstract:
Geographic information systems (GIS) and artificial intelligence (AI) techniques were used to develop an intelligent snow removal asset management system (SRAMS). The system has been evaluated through a case study examining snow removal from the roads in Black Hawk County, Iowa, for which the Iowa Department of Transportation (Iowa DOT) is responsible. The SRAMS is comprised of an expert system that contains the logical rules and expertise of the Iowa DOT’s snow removal experts in Black Hawk County, 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 GIS package), Visual Rule Studio 2.2 (an AI shell), and Visual Basic 6.0 (a programming tool). The system could efficiently be used to generate prioritized snowplowing routes in visual format, to optimize the allocation of assets for plowing, and to track materials (e.g., salt and sand). 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.
Format:
PDF ;
Funding:
MTC 3-A ;
Collection(s):
US Transportation Collection
Main Document Checksum:
[+]
urn:sha256:be5c3e5cc3cac2feaebd268d5808edfd8703da204f258a809447f9aa5a4b1cef
File Type:

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