Model for road network equilibrium bi-level programming based on rough genetic algorithm

In the traffic system with flow guidance, travelers can choose their paths according to both guidance information and their experience. In this paper, based on the relationship between the effects of traffic guidance and the construction cost, the optimization plan is established as a bi-level program. The objective function at the upper level is defined as the total travel time on the network, plus total investment costs of link capacity expansions. The lower level problem is formulated as a stochastic user equilibrium model. General genetic algorithm combined with rough set theory is used to find the optimal solution. Attributes reduction of rough set is adopted to filtrate new chromosome comes from crossover operation of GA, so as to increasing seeking speed. The result of a road network example verified high efficient of the rough genetic algorithm.