基于客流博弈均衡的旅客列车双层开行方案优化OA

To improve the efficiency of passenger train operation and passengers'satisfaction,this paper built a bi-level programming model of pure integer nonlinear passenger train operation plan for high speed railway.It took the net profit of the railway operation department and the minimization of empty seat kilometers as the upper 0-1 programming model,and the maximization of passengers'satisfaction as the lower passenger flow equilibrium model.For the lower-level passenger flow equilibrium model,the paper selected the homogeneous group of passengers as the game players to construct the reasonable winning function of each player by dividing the game environment and analyzing the passengers'travel choice behavior.The non-cooperative game theory was applied to the balanced distribution of passenger flow in each game environment.After analyzing the characteristics of the model,the paper adopted the non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)to solve the upper-level stopping plan model and the quantum-behaved particle swarm optimization(QPSO)algorithm to solve the lower-level passenger flow game equilibrium model.With Lanzhou-Xinjiang High Speed Railway as an example,the feasibility of the model and algorithm was verified.The results show that the algorithm can obtain a Nash equilibrium solution that satisfies both railway operators and passengers,and the loss rate of railway passengers is 0%.