Second-best Congestion Pricing Model in a Stochastic Road Network Based on Genetic Algorithm

In view of travelers’ risk aversive route choice behaviors under a stochastic road network,a second-best congestion pricing bi-level programming model considering both the reliability and the unreliability of travel time is proposed.In the upper level model,the optimization objective is to maximize the social welfare of the road network in the presence of congestion pricing,while the lower objective is an elastic demand mean-excess traffic equilibrium model.In consideration of the complexity of bi-level programming model,Genetic Algorithm(GA) is presented to solve the proposed model.Simulation results show that,it is feasible to solve the proposed model using GA,which can converge to the target value after 50 iterations.