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Method and system for identifying influencing factors of shared bicycle travel, and storage medium

A technology of sharing bicycles and influencing factors, applied in image data processing, image enhancement, 3D modeling, etc., can solve problems such as ignoring spatial non-stationarity

Active Publication Date: 2019-03-29
SOUTH CHINA UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The parameters estimated by the traditional global regression model are global and stationary, but ignore the spatial non-stationarity

Method used

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  • Method and system for identifying influencing factors of shared bicycle travel, and storage medium
  • Method and system for identifying influencing factors of shared bicycle travel, and storage medium
  • Method and system for identifying influencing factors of shared bicycle travel, and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0063] This embodiment provides a method for identifying influencing factors of shared bicycle travel. This method uses a mixed geographically weighted regression model to identify the impact of built environment factors in different spatial units on shared bicycle travel, thereby being able to identify different built environment characteristics. It provides the basis for the launch and deployment of shared bicycles in space units, and provides a technical basis for the operation of shared bicycle companies, urban slow traffic planning and intelligent transportation system construction.

[0064] like figure 1 As shown, the method for identifying influencing factors of shared bicycle travel in this embodiment includes the following steps:

[0065] S1. Acquire raw data.

[0066] The raw data in this embodiment includes shared bicycle data, point of interest (POI) data and urban road data, and the point of interest data can be obtained from the API (Application Program Interfac...

Embodiment 2

[0100] In order to verify the implementation effect of the method for identifying the influencing factors of shared bicycle travel in the above-mentioned embodiment 1, this embodiment collects the distribution data of Mobike bicycles for 24 hours a day on August 13, 2018 in Liwan District, Guangzhou City as an application example for verification.

[0101] 1) The distribution data of shared bicycles is collected every 10 minutes, including 144 collection time points throughout the day, with a total of 1,899,267 rows of original collected data, and 85,791 trajectories are generated after shared bicycle data preprocessing. The built environment data includes two parts: POI data and urban road data. Among them, the POI data is 21,276 POIs crawled in Liwan District, Guangzhou in April 2018, which are screened and integrated into residence, office, life services, medical care, and catering. Eight categories of shopping, sports and leisure, culture and education, and transportation f...

Embodiment 3

[0116] like Figure 7 As shown, the identification system of influencing factors of shared bicycle travel includes a data acquisition module, a preprocessing module, a grid division module and an identification module. The specific functions of each module are as follows:

[0117] The data acquisition module is used to acquire shared bicycle data, point of interest data and urban road data.

[0118] The preprocessing module is used to preprocess the acquired shared bicycle data, point of interest data and urban road data.

[0119] The grid division module is used to divide the sample unit grid according to the preprocessed data, and count the number of starting and ending points, the number of points of interest and the length of urban roads of shared bicycles.

[0120] The identification module is used to construct a mixed geographic weighted regression model based on the sample unit grid to identify built environment factors that affect the distribution of starting points a...

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Abstract

The invention discloses a method and system for identifying influencing factors of shared bicycle travel, and a storage medium. The method comprises the following steps: obtaining shared bicycle data,interest point data and urban road data; The shared bicycle data, point of interest data and urban road data are preprocessed. According to the pre-processed data, the sample cells are divided into grids, and the number of starting and ending points, the number of interest points and the length of urban road are counted. Based on the grid of sample units, a mixed geographically weighted regression model is constructed to identify the built-up environmental factors that affect the distribution of starting and ending points of shared bicycle travel. The invention combines the mixed geographically weighted regression model, can identify the impact of built-up environment factors of different spatial units on shared bicycle travel, so as to provide a basis for the launch and deployment of shared bicycles with different built-up environment characteristics, and provide a reference for the operation strategy of shared bicycle enterprises, traffic planning and related public policies.

Description

technical field [0001] The invention relates to a method, system and storage medium for identifying influencing factors of shared bicycle travel, and belongs to the field of built environment and traffic behavior. Background technique [0002] The construction of a smart society. As one of China's "Four New Inventions", shared bicycles have been rapidly popularized due to the advantages of pick-and-place, convenience, environmental protection, and the ability to solve the "first / last mile" traffic problems. Phenomena such as random placement and excessive delivery also have a negative impact on urban traffic order and city image. Therefore, based on intelligent technology, the accurate identification of the impact of the urban built environment on the travel characteristics of shared bicycles, the rational deployment and allocation of shared bicycles in different regions, and the matching of product supply and user demand are the current focus of the government, enterprises...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T17/30G06F16/29
CPCG06T17/30G06T2207/20104
Inventor 莫海彤魏宗财陈婷婷
Owner SOUTH CHINA UNIV OF TECH
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