Rail transit station classification method based on big data

Abstract

The invention discloses a rail transit station classification method based on big data. The method comprises the following steps: determining an average travel distance of shared bicycles; constructing a peripheral space buffer area of the rail transit station; constructing thiessen polygons around the rail transit stations; determining a rail transit station space influence range; obtaining interest points in the research range; counting the number of various interest points in the space influence range of the rail transit station; calculating an interest point dominance index and a uniformity index in the space influence range of the rail transit station; determining rail transit station classification indexes; and determining the type of each rail transit station according to the interest point dominance index and the interest point uniformity index of each rail transit station and the proportion of each interest point. According to the method, firstly, the space influence ranges ofdifferent rail transit stations are determined. The station types are determined based on actual rail transit station elements in the influence ranges. The high innovation and applicability are achieved. The problem of rail transit station classification can be reasonably and effectively solved.

Description

technical field [0001] The invention relates to a method for classifying rail transit stations. In particular, it involves a classification method for rail transit stations based on big data. Background technique [0002] Urban rail transit station is not only an important node and distribution point of urban transportation network, but also a regional place in a city. Due to the influence of different locations along the rail transit route, urban functions, and existing land use patterns, in the research on the development and utilization of space around urban rail transit stations, infrastructure connection planning, and passenger flow attraction characteristics, there are often differences between different types of stations. There are large differences. Therefore, it is necessary to classify the types of rail transit stations according to different functions and locations, and to discuss the differences between different types of stations. [0003] The two basic types...

AI Technical Summary

Problems solved by technology

[0006] Through the analysis of the background technology above, we can see that most of the existing researches in China classify stations based on field survey data or land use around the stations; foreign classification methods combine urban characteristic factors with traffic characteristic factors to comprehensively evaluate rail transit stations and classify them. Its classification is not enough for the use of various actual land use data around the site, so the classification of the site is not objective enough

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