DocumentCode
833504
Title
Robust route optimization for gritting/salting trucks: a CERCIA experience
Author
Handa, Hisashi ; Chapman, Lee ; Yao, Xin
Author_Institution
Okayama Univ., Japan
Volume
1
Issue
1
fYear
2006
Firstpage
6
Lastpage
9
Abstract
Highway authorities in marginal winter climates are responsible for the precautionary gritting/salting of the road network in order to prevent frozen roads. For efficient and effective road maintenance, accurate road surface temperature prediction is required. However, this information is useless if an effective means of utilizing this information is unavailable. This is where gritting route optimization plays a crucial role. The decision whether to grit the road network at marginal nights is a difficult problem. The consequences of making a wrong decision are serious, as untreated roads are a major hazard. However, if grit/salt is spread when it is not actually required, there are unnecessary financial and environmental costs. The goal here is to minimize the financial and environmental costs while ensuring roads that need treatment will. In this article, a salting route optimization (SRO) system that combines evolutionary algorithms with the neXt generation Road Weather Information System (XRWIS) is introduced. The synergy of these methodologies means that salting route optimization can be done at a level previously not possible.
Keywords
evolutionary computation; information systems; public administration; road safety; weather forecasting; evolutionary algorithms; gritting route optimization; neXt generation Road Weather Information System; road gritting; road maintenance; road salting; salting route optimization; Costing; Costs; Economic forecasting; Globalization; Information systems; Road safety; Road vehicles; Robustness; Temperature; Weather forecasting;
fLanguage
English
Journal_Title
Computational Intelligence Magazine, IEEE
Publisher
ieee
ISSN
1556-603X
Type
jour
DOI
10.1109/MCI.2006.1597056
Filename
1597056
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