Analysis of the Superposition Effect of Passenger Flow at New Rail Transit Stations Based on Causal Inference

Reasonable construction of new urban rail transit stations will help attract more passengers and improve traffic congestion. In this study, the causal inference method and Lotka–Volterra model are used to analyze the superposition effect of passenger flow after the addition of new stations and quantify it from two perspectives—that is, the attraction and sharing effect. A case study of new Harbin rail transit stations shows that after the addition of new stations, the passenger flow in existing stations increased, except for a few stations with a sharing effect, which means that the superposition effect is reflected primarily in the attraction effect. The sharing effect of stations is related to the actual average value of the passenger flow with a negative logarithmic distribution. For transfer stations, the superposition effect of transfer stations that are on the same line as new stations is correlated with the number of intervening stations between them and has a logarithmic distribution. The passenger flow at transfer stations increases from 2.88% to 9.24%, and the superposition effect of transfer stations is negatively correlated with the mean value of the passenger flow at transfer stations and has an exponential distribution.