Subway station clustering and regression analysis method and system based on POI data and passenger flow data

Abstract

The invention provides a subway station clustering and regression analysis method and system based on POI data and passenger flow data. The method includes: collecting the quantity of POI with different functions around the subway station through the statistical software of GIS, counting the daily average passenger flow in and out of the subway station based on AFC card swiping data, and generating the data set to be processed. Based on the different POI quantity around the subway station, classifying the service intensity grade of subway station by a clustering method; taking the daily average passenger flow as dependent variable and the POI quantity as independent variable, through regression analysis, seeking the influence relationship between the POI types and quantity around the subway station and the passenger flow of the subway station. The method and the system provided by the invention adopt POI data and AFC card swiping data, combine the dynamic data and the static data, candirectly and effectively measure the scale of a subway station, and comprehensively analyze the functional positioning and service level of different subway stations. According to the different typesof subway stations, the invention puts forward some suggestions to deal with different passenger flow characteristics.

Description

technical field [0001] The present invention relates to the technical field of traffic information, more specifically, to a method and system for clustering and regression analysis of subway stations. Background technique [0002] Urban rail transit undertakes the important task of passenger transportation and is an important link in public transportation. To cope with the huge passenger flow of subway stations during peak hours, and to organize passengers to enter and exit stations and take rail transit in a reasonable and orderly manner, it is necessary to master the travel rules of subway passenger flow. Research the number and types of POI (point of interest) around the subway station, and analyze the historical data of the daily average passenger flow in and out of the subway station, combine static data with dynamic data, and comprehensively analyze the functional positioning of different subway stations in Beijing and service level. For different subway station type...

AI Technical Summary

Problems solved by technology

However, the prediction and analysis are usually only for the passenger flow information data of the station, and the analysis and mining of traffic points of interest around the subway station are relatively seldom

The combination of actual data and theoretical models is not enough, and the analysis and excavation of factors affecting passenger flow in subway stations is not deep enough

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