Dynamic salting route optimisation using evolutionary computation

On marginal winter nights, highway authorities face a difficult decision as to whether or not to salt the road network. The consequences of making a wrong decision are serious, as an untreated network is a major hazard. However, if salt is spread when it is not actually required, there are unnecessary financial and environmental consequences. In this paper, a new salting route optimisation system is proposed which combines evolutionary computation (EC) with the next generation road weather information systems (XRWIS). XRWIS is a new high resolution forecast system which predicts road surface temperature and condition across the road network over a 24 hour period. ECs are used to optimise a series of salting routes for winter gritting by considering XRWIS temperature data along with treatment vehicle and road network constraints. This synergy realises daily dynamic routing and it will yield considerable benefits for areas with a marginal ice problem.

Digital Object Identifier: 10.1109/CEC.2005.1554680
Published with permission from the copyright holder. This is the institute's copy, as published in Evolutionary Computation, 2005. The 2005 IEEE Congress on, 2-5 sept. 2005, Vol. 1, Pages 158-165.
Publisher URL:http://dx.doi.org/10.1109/CEC.2005.1554680
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