Robust route optimization for gritting/salting trucks: a CERCIA experience

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.

Digital Object Identifier: 10.1109/MCI.2006.1597056
Published with permission from the copyright holder. This is the institute's copy, as published in Computational Intelligence Magazine, IEEE, Feb. 2006, Vol. 1, Issue 1, Pages 6-9.
Publisher URL:http://dx.doi.org/10.1109/MCI.2006.1597056
Copyright © 2006 IEEE. All rights reserved.