Analysis of the correlation between rail transit policies and traffic congestion consider urban scale

Reasonable transportation planning policies can effectively reduce traffic congestion, improve traffic operation efficiency, and improve the quality of residents' travel. The paper studied the correlation analysis between rail transit policies and traffic congestion under different urban scales. Firstly, construct a system of factors affecting rail transit congestion and implement clustering analysis of rail transit data based on an improved k-means clustering algorithm. Then, normalize the factors that affect rail transit congestion, and use the normalized traffic congestion coefficient factors as a reference sequence. Finally, the grey entropy correlation analysis method is used to calculate the entropy correlation between urban scale factors, rail transit policy factors, and traffic congestion. The research results of correlation analysis indicate that urban scale factors and rail transit policy factors are highly correlated with traffic congestion; The biggest factor affecting traffic congestion is the comprehensive density distribution policy of population and jobs, while the smallest factor is the population density of employees.