Research On Bus Passenger Flow Forecasting Model Based On Rough Set And Multiple Regression

This paper made a depth analysis and research in the process of public transport passenger flow forecasting and intelligent scheduling refer to the intelligent transportation system(ITS)research.Passenger flow is the basic basis of the bus intelligent scheduling process,and an important prerequisite for public transportation network intelligent planning,it can be obtained through analysis and mining from mass historical data.Because of the passenger flow data has the character of time sequence and mass.Firstly,this paper introduced the current situation of bus passenger flow forecasting and bus intelligent dispatching,and also analysis the key and difficult points in the process of bus passenger flow prediction.Gives a detailed discusses to the advantages and disadvantages of rough set in dealing with massive data and the related basic theory,and discusses the development of rough set in intelligent transportation system.As the core content of rough set theory research,attribute reduction is the premise and key to deal with massive data.This paper defined a new attribute importance function and the membership function according to the characteristics of the historical data to attribute extraction and attribute value merging for public transportation passenger flow data based on rough set theory attribute reduction algorithm.The simulation experiment method and result are given in this paper.It is proved that the function can effectively extract the basic data and merge the attribute value.Finally,this paper proposes a combined forecasting model based on rough set and multiple linear regression model,defined a model accuracy discriminant function,compared with the accuracy of the bus passenger flow of multiple linear regression model,It is concluded that the combination forecasting model based on rough set and multiple linear regression can get more accurate forecast result and the forecasting result could carry out the intelligent planning of the bus line.