C.G. Xiao, H. Zhang

Pages: 3-14

Abstract
From the perspective of public policy, it is crucial to accurately predict the congestion situation of urban rail transit. In this paper, a new predicting method for the congestion situation of urban rail transit is provided. Firstly, analyze three topological structural features and determine the characteristics of urban rail transit from the perspective of public policy by calculating the degree value, average value, actual number of edges between adjacent stations, and connectivity. Secondly, determine the key parameters of urban rail transit congestion situation and determine the impact parameters of urban rail transit congestion situation; Finally, combining the genetic algorithm and association rule algorithm, the operator is assigned congestion situation impact parameters, and precise prediction rules for urban rail transit congestion situation are set. With the congestion situation impact parameters as input and traffic congestion situation as output, a congestion situation prediction model for urban rail transit is constructed. The test results indicate that the proposed method can improve the accuracy of predicting urban rail transit congestion situation and effectively improve rail transit accessibility.
Keywords: public policy perspective; urban rail transit; congestion status; connectivity; genetic network planning algorithm; association rules