An this paper the bi-level congestion pricing problemwas formulated usingGenetic Algorithm technique to solveop
In this paper the bi-level congestion pricing problemwas formulated usingGenetic Algorithm technique to solveoptimal toll locations and level setting problem for a road network. In upper level, congestion pricing problemis solved to maximize net social surplus and in lower leveluser equilibrium problem is solvedto assign the traffic flow on the network using Frank Wolfe algorithm. Assuming the travel demand is elastic, Genetic Algorithm is applied to the Sioux Falls network to find optimum toll locations and toll levels. Comparing the results with previous researches indicated thatGenetic Algorithm performed efficiently and enhanced the net social surplus. In addition, using Genetic Algorithm reduced the number of tolled links involved in the congestion pricing scheme while the average toll rates did not change significantly.
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