In view of the problems of large errors in the forecast of passenger flow and revenue in the current decision-making stage of high-speed railway,as well as the linear,two-dimensional and unreliable sources of historical data,on the basis of in-depth analysis of the evolution trend and mechanism of the non-linearity,non-certainty,coupling and non-balance of the influencing factors of passenger flow and revenue,the historical data was dug further and a multidi-mensional spatiotemporal basic historical database of passenger flow and income in high-speed railway operations was es-tablished,based on the theoretical methods of BPNN,improved FC,FIS and rough set.A nonlinear multidimensional intelligent forecasting system was constructed for passenger flow and revenue based on BIM and PYTHON to achieve ac-curate forecast and multi-dimensional presentation of passenger flow and revenue of high-speed railway operations.The results show that following the selection of the complex influencing factors related to passenger flow and revenue of high-speed railway,a multi-dimensional spatiotemporal data support system is constructed for historical data of passenger flow and income of high-speed railway.The establishment of BPNN,improved FC and FIS operating passenger flow and reve-nue forecasting models improves the method fit and forecast accuracy and effectiveness.The multi-dimensional intelligent display system designed and created for passenger flow and income of high-speed railway operations based on BIM and PYTHON provides technical and methodological support for improving the reliability,visualization and intelligence of high-speed railway investment decision.
维普
万方数据