In order to better unfold the influences of related factors on railway passenger traffic volume to optimize the selection of factors influencing railway passenger traffic forecast modeling

this paper analyzes the influencing factors by the grey theory and introduces a novel grey correlation analysis approach to railway passenger traffic volume with dynamic resolution coefficients

considering the weaknesses of traditional correlation analysis embodied in specific applications such as relation appraisal tending to be equalized and sorting results subject to the impact of discrimination coefficient value.Hence the relations between diverse factors and passenger traffic volume are uncovered

which can help to optimize the selection of factors influencing railway traffic volume forecast modeling.With the railway passenger traffic volume in Henan Province taken for instance

the simulation results testify that the accuracy of forecast has been increased

proving that the approach adopted here is feasible and effective.