Prediction of Urban Rail Transit Scale Based on Cluster Analysis

In order to determine the scale of urban rail transit line network more reasonably, this paper establishes the rail transit scale prediction model under different city categories. Firstly, the standardized influencing factors are selected as the classification indexes of 20 cities, and the cities are divided into four tiers through cluster analysis; then the most important influencing factors of the cities in different tiers are analyzed through principal component analysis, and combined with this result, the quantitative relationship between the scale of rail transit development under each tier and the principal components is established by using multivariate step-by-step regression method, which analyzes the direction and degree of influencing factors on the scale to Finally, the relevant data of four types of cities in 2021 and 2022 are selected to predict the scale of rail transit line network in each city and conduct error analysis. The results show that the regression based on principal component analysis is more reliable than ordinary multiple linear regression, and the principal component regression can effectively avoid the influence of multicollinearity on the results.