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Hadoop-based shared bicycle parking site selection method

A technology of sharing bicycles and basic bicycles, applied in the field of location selection of shared bicycle parking spots, can solve problems such as management confusion, unreasonable resource allocation, and low accuracy, and achieve standardized management, scientific parking location selection, and improved rationality and accuracy effects

Active Publication Date: 2018-11-06
ZHEJIANG UNIV CITY COLLEGE
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  • Abstract
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AI Technical Summary

Problems solved by technology

At present, the location selection of shared bicycle parking spots has low accuracy, which leads to unreasonable supply and demand relationships, unreasonable resource allocation, and chaotic management.

Method used

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  • Hadoop-based shared bicycle parking site selection method
  • Hadoop-based shared bicycle parking site selection method
  • Hadoop-based shared bicycle parking site selection method

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Embodiment Construction

[0062] The present invention will be further described below in conjunction with the examples. The description of the following examples is provided only to aid the understanding of the present invention. It should be pointed out that for those skilled in the art, without departing from the principle of the present invention, some improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

[0063] As a Hadoop-based shared bicycle parking point site selection method of an embodiment, the specific implementation steps are as follows:

[0064] Step 1. Prediction of shared bicycle demand points based on distributed clustering algorithm

[0065] This patent adopts Hadoop-based parallelization method to realize Canopy-Kmeans algorithm pair image 3 The model is solved. Under the framework of MapReduce, we can split the solution method of the model into ...

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Abstract

The invention relates to a Hadoop-based shared bicycle parking site selection method which comprises 1) shared bicycle demand point prediction based on a distributed clustering algorithm; 2) a sharedbicycle parking site selection model based on multi-objective optimization; and 3) a model solution algorithm based on a NSGA-II algorithm; the algorithm is implemented by Hadoop, and after a population is initialized, the evolution process of each generation is completed by MapReduce. The Hadoop-based shared bicycle parking site selection method is expected to improve the rationality and the accuracy of the shared bicycle parking site selection, and to achieve standardized shared bicycle management. For a large amount of generated shared bicycle travel data, the method establishes a demand prediction model based on travel data, and predicts a shared bicycle demand point through a Hadoop framework and a clustering algorithm.

Description

technical field [0001] The present invention relates to the technical field of computer information processing, more specifically, it relates to a method for selecting the location of shared bicycle parking spots based on Hadoop. Background technique [0002] In recent years, along with the social development and the improvement of people's living standards, people's travel awareness has also changed, and low-carbon travel has become the theme of people's travel. The fusion product of modern technology and public bicycles—shared bicycles came out and quickly occupied the core position of the market. Shared bicycles make up for the inherent shortcomings of fixed-point borrowing and returning of public bicycles, and inconvenient refunds of deposits. Shared bicycles are more in line with people's travel routes, and it is convenient for people to travel. The characteristics of parking everywhere lead a large number of users to choose shared bicycles. For the "last mile" solutio...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/30
CPCG06Q10/04G06Q10/06315G06Q50/40
Inventor 陈观林史豫坤徐煌
Owner ZHEJIANG UNIV CITY COLLEGE
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