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The cross-section passenger flow of urban rail transit has a large difference in space during the peak times and the operation mode of full and part routes should be adopted. By analyzing the passenger flow in the peak times, aiming at the minimum cost of the enterprise and the passenger waiting time, the multi-objective optimization model can be constructed. By introducing the time value coefficient, the passenger waiting time will be converted into the cost of generalized waiting time. The multi-objective optimization mode is transformed into a single-objective optimization model. The genetic algorithm is used to solve this problem and calculates the frequency of trains. Finally, the validity of the model is verified by an example.

An Analysis on Optimizing Full and Part Routes of Urban Rail Transit under Peak Times