A Method for Passenger Flow Demand Prediction of Urban Rail Transit Based on the Incremental Division Model

This paper constructs an incremental model for to achieve short- and medium-term rail passenger flow forecasting based on current passenger flow. Factors affecting passenger flow demand are analyzed, and the trip decision-making and choice preference of rail transit travelers are obtained by an intention questionnaire (SP) for rail transit travelers. Factors that affect rail transit passenger flow are identified by linear regression. Utility change of rail transit caused by the change of travel time and cost is considered, and the passenger demand of rail transit is predicted by the incremental division model. The proposed method is used to predict passenger demand of phase Ⅰ of R4 line in Shunyi District. Results showed that the amount and proportion of public traffic increased by 46.46% and 21.62%, respectively. The proposed method can make full use of existing multi-source data to achieve the urban rail transit line passenger demand forecast.