Function clustering method for public bicycle system's rental spots

Abstract

The invention discloses a function clustering method for a public bicycle system's rental spots, and the method comprises the following steps: step 1) for the public bicycle system, first, utilizing the position information of the rental spots and using the Tyson polygon algorithm to perform regional division to a city with each rental spot covering a certain region; and regarding each rental spot as a text to be mined and analyzed; step 2) converting the record of historical use of the public bicycle system into the words contained by the rental spots; step 3) using the LDA algorithm and the K-means algorithm to identify the functions of the rental spots; and step 4) making an analysis of the spatial and temporal characteristics of each clustering result; and defining the functions represented by the rental spots in each kind of clustering. According to the method of the invention, it is possible to provide basis for future planning of public bicycles, to guide users to use the public bicycles, and to guide the establishments of commercials and advertisements. Therefore, the method has a good prospect for industrial applications.

Description

technical field [0001] The invention belongs to the field of public bicycle system planning in traffic planning, and in particular relates to a functional clustering method of public bicycle system rental points. Background technique [0002] Functional identification of urban areas refers to the use of urban operating data and dynamic features to identify and label different areas of the city according to their functions in the city. It is the main component of urban planning, which can help people understand the internal development law of the city, carry out urban planning, and guide the development of urban construction. However, it is very difficult to find this kind of research on urban functional areas: the activities of urban residents are complex, and urban functional areas are not single, so it is difficult to accurately define the specific functions of an area; traditional methods are mostly user research and field statistics. It is very time-consuming and labor-...

AI Technical Summary

Problems solved by technology

However, it is very difficult to find this kind of research on urban functional areas: the activities of urban residents are complex, and urban functional areas are not single, so it is difficult to accurately define the specific functions of an area; traditional methods are mostly user research and field statistics. It is very time-consuming and labor-intensive; and the recently adopted research object - urban data has a wide variety, large scale, multi-granularity, multi-dimensional and other characteristics, it is very difficult to find the law from it; because the urban functional area itself cannot directly Observation, so it is difficult to verify the validity and correctness of the algorithm

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