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User-oriented shared bicycle prediction method

A technology of shared bicycles and prediction methods, which is applied in the field of user-oriented shared bicycle predictions, can solve problems such as shared bicycles that have not yet been raised, and achieve the effect of improving the car rental experience and solving the difficulty of finding a car

Pending Publication Date: 2022-02-25
CHANGZHOU INST OF TECH
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  • Application Information

AI Technical Summary

Problems solved by technology

[0005] At present, the scheme of predicting the demand for shared bicycles from the perspective of users has not yet been proposed.

Method used

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  • User-oriented shared bicycle prediction method
  • User-oriented shared bicycle prediction method
  • User-oriented shared bicycle prediction method

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Embodiment Construction

[0024] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0025] Such as figure 1 As shown, firstly, the shared bicycle big data is processed, and then the random forest prediction model is constructed and trained to obtain the user's current time and location for prediction, and the prediction results are visualized.

[0026] Let's take a specific example as a demonstration: the latitude and longitude of the user's location are 121.49, 31.29, and the current time is 17:54.

[0027] Step 1, shared bicycle big data processing.

[0028] Data collection: Collect local big data sets of shared bicycles. Each original data record includes: order number, vehicle number, user number, departure time, departure longitude, departure latitude, end time, end long...

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Abstract

The invention relates to application of big data and machine learning technologies in the field of shared bicycles, in particular to a user-oriented shared bicycle prediction method. From the perspective of bicycle users, when a user cannot find shared bicycles, the bicycle coming conditions around the user in a period of time in the future are predicted, the user is helped to find the shared bicycle, and the bicycle renting experience of the user is enhanced. The method comprises a shared bicycle big data processing module which can perform big data processing on an original data set based on a prediction task and convert the original data set into a required data set; a prediction model building and training module which is used for building a random forest prediction model and training the prediction model by using the data set obtained by the big data processing module; a coming vehicle prediction module which is used for acquiring time and place information of the user and predicting the number of coming vehicles around the user in a future period of time by applying a prediction model; and a prediction result visualization module which is used for displaying the prediction result to the user in a visual chart.

Description

technical field [0001] The invention relates to the application of big data and machine learning technology in the field of shared bicycles, specifically a user-oriented shared bicycle prediction method. Background technique [0002] As a new type of short-distance commuting, dockless shared bicycles have effectively alleviated urban traffic congestion and have been deployed on a large scale in many cities. However, the problem of finding a car during peak hours still exists. To this end, various demand forecasting methods for shared bicycles are studied to predict the demand of a certain location in the future time period, and to optimize the allocation and scheduling of shared bicycles at each site in advance to alleviate the contradiction between supply and demand of shared bicycles. [0003] At present, the Chinese invention patent CN112734101 A discloses an intelligent allocation method for shared bicycles based on vehicle demand forecasting, which can predict the vehi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06F16/9038G06N20/00
CPCG06Q30/0205G06F16/9038G06N20/00
Inventor 曾友董世权黄闽江夏莉丽耿思远陈勇徐则中
Owner CHANGZHOU INST OF TECH
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