Capacity optimization and allocation of an urban rail transit network based on multi-source data

Abstract

This study establishes a multi-objective optimization model for the capacity allocation of an urban rail transit network based on multi-source data on the Beijing metro passenger flow. The model considers the operating costs of trains and the expenses related to the waiting time of transferring passengers. The model constraints include the distribution characteristics of passenger flow, headway, load factor, and available trains. The capacity allocation scheme for 16 railway lines was obtained by adopting a model of the Beijing rail transit network and its passenger flow. We also analyzed the frequency at which Line 4 is reduced from 55 to 45 and the frequency at which Line 10 is reduced from 60 to 52 if a short turn is adopted. In addition, when the upper limit of the load factor increased from 60 to 70%, the operational costs were reduced by 4.6%, while the total passenger waiting time increased by 1%. The transfer costs changed the capacity optimization and allocation scheme, and the proportion of the transfer cost among the total costs increased when the time value increased.