The precautionary salting of the road network is an important maintenance issue for countries with a marginal winter climate. On many nights, not all the road network will require treatment as the local geography will mean some road sections are warmer than others. Hence, there is a logic to optimising salting routes based on known road surface temperature distributions. In this paper, a robust solution of Salting Route Optimisation using a training dataset of daily predicted temperature distributions is proposed. Evolutionary Algorithms are used to produce salting routes which group together the colder sections of the road network. Financial savings can then be made by not treating the warmer routes on the more marginal of nights. Experimental results on real data also reveal that the proposed methodology reduced total distance traveled on the new routes by around lOconventional salting routes. © 2006 IEEE.
|Title of host publication
|2006 IEEE Congress on Evolutionary Computation, CEC 2006
|Number of pages
|Published - 2006