att_abstract={{One of the main goals in transportation planning is to achieve solutions
for two classical problems, the traffic assignment and toll pricing
problems. The traffic assignment problem aims to minimize total travel
delay among all travelers.  Based on data derived from the first problem,
the toll pricing problem determines the set of tolls and corresponding
tariffs that would collectively benefit all travelers and would lead to
a user equilibrium solution.  Obtaining high-quality solutions for this
framework is a challenge for large networks. In this paper, we propose
an approach to solve the two problems jointly, making use of a biased
random-key genetic algorithm for the optimization of transportation
network performance by strategically allocating tolls on some of the
links of the road network.  Since a transportation network may have
thousands of intersections and hundreds of road segments, our algorithm
takes advantage of mechanisms for speeding up shortest-path algorithms.
	att_categories={C_CCF.1, C_CCF.2, C_CCF.7, C_CCF.8},
	att_copyright_notice={{The definitive version was published in Optimization Letters . {{, 2010-12-15}}{{, http://dx.doi.org/10.1007/s11590-010-0226-6}}}},
	att_tags={transportation planning,  system optimal,  user equilibrium,  traffic assignment,  toll booth placement,  dynamic shortest paths,  genetic algorithm},
	author={Mauricio Resende and Luciana S. Buriol and Michael J. Hirsch, Raytheon and Panos M. Pardalos and Tania Querido and Marcus Ritt},
	institution={{Optimization Letters (Springer)}},
	title={{A biased random-key genetic algorithm for road congestion minimization}},