Suburban Rail Passenger Flow Forecasting Based On Spatial Interaction Theory

After years of development,China's urbanization has entered a new stage of development.The cultivation and development of the metropolitan area has become the main trend of China's urbanization development in the future.At the same time,under the policy guidance of building the “urban circle on the track”,The rail transit system that serves the metropolitan area will also be rapidly developed.However,the current passenger flow forecasting work for the metropolitan area has not yet formed a certain method system.Therefore,it is urgent to establish a passenger flow forecasting method for the metropolitan area.Combining with the reality of Chongqing urban rapid rail passenger flow forecasting work,this paper firstly analyzes the concepts of metropolitan area and urban agglomeration,pointing out that urban agglomeration focuses on economic connection,while metropolitan circles focus more on social relations,that is,metropolitan area is a concept of living circle and commuting circle,therefore,the root cause of passenger flow in the metropolitan area lies in the economic and social relations between different regions within the metropolitan area.And it sorts out the development process and development law of domestic and international metropolitan circles,and points out that the metropolitan area is the inevitable development direction after urban development to advanced stage,and also an important carrier for China's participation in international economic competition and an important means to optimize urban spatial layout.The developed metropolitan areas abroad all have developed rail transit systems.Combined with China's high population density and the requirements of sustainable development,the construction and development of rail transit systems are irreplaceable for the economic and social development of the metropolitan area.In order to support the construction and development of the metropolitan area,Chongqing proposed to build a metropolitan rail transit service system serving the metropolitan area.It is characterized by filling in the gaps of the rail transit mode that does not specifically serve the region,and directly interoperating with the urban rail transit system serving the main urban area.While serving the commuter flow in the peripheral districts and counties,it can also alleviate the passenger flow pressure of the urban rail transit system and greatly improve the economic and social integration of the metropolitan area.In the study of the passenger flow forecasting method for the Chongqing metropolitan area,this paper first reviews the existing passenger flow forecasting methods of the national railway,intercity railway and urban rail transit system,and analyzes the flaw of the existing passenger flow forecasting methods.In this paper,the spatial interaction theory of regional economics is studied to study the intensity of economic and social relations between different regions,and the urban fast-track passenger flow prediction method based on spatial interaction strength is proposed in the framework of traditional passenger four-stage flow prediction method.In the traffic generation stage,BP artificial neural network technology was used to predict the passenger flow in Chongqing metropolitan area.In the stage of traffic distribution,combined with many economic and social factors that affect the distribution of passenger flow in the metropolitan area,a structural equation model is established to quantitatively calculate the spatial interaction potential of different regions of the metropolitan area,and the economic gravity model is used to calculate the different Spatial interaction strength between different regions.Based on the spatial interaction intensity of economic and social connections,the spatial interaction intensity is substituted into the traffic distribution model as a parameter to correct the distributed passenger flow,and finally the passenger flow forecast results of the urban fast track serving the Chongqing metropolitan area are obtained.