Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A tourist flow prediction method based on machine learning

A traffic forecasting and machine learning technology, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as the large influence of a single factor and the need to improve the accuracy, and achieve the effect of improving the accuracy

Inactive Publication Date: 2018-12-18
成都中科大旗软件股份有限公司
View PDF7 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

There are complex internal correlations among various factors that affect the flow of tourists in tourist attractions. Simply relying on the exhaustive calculation method of weighting certain factors, the influence of a single factor is too large, and the accuracy of prediction needs to be improved.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A tourist flow prediction method based on machine learning
  • A tourist flow prediction method based on machine learning
  • A tourist flow prediction method based on machine learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0027] A method for predicting tourist flow based on machine learning, comprising the following steps:

[0028] A. Collect historical tourist flow data of tourist attractions, and sort the data by year, month, and day;

[0029] B. Obtain the associated data of the corresponding time period of the above-mentioned historical tourist flow data, the associated data includes at least one of the highest temperature, the lowest temperature, weather, wind direction, wind force, and working day conditions, and the historical tourist flow in units of days Aggregation of data and linked data;

[0030] C. Convert associated data into numerical values ​​and integrate them with historical tourist flow data;

[0031] D. Input associated data and historical tourist flow into the learner for training to realize tourist flow prediction.

Embodiment 2

[0033] Based on the principles of the foregoing embodiments, this embodiment discloses a detailed embodiment solution.

[0034] A. Collect historical tourist flow data of tourist attractions. The source of the data can be historical tourist flow data from tourist attractions, historical ticket sales data, or tourist reception data from tourism authorities. Sort and organize.

[0035] B. Obtain the associated data of the above-mentioned historical tourist flow data corresponding to the time period. The associated data includes at least one of the highest temperature, the lowest temperature, weather, wind direction, wind force, and working day conditions, and compare the historical tourist flow data with the day. Linked Data Summary.

[0036] Taking the historical tourist flow data of a tourist attraction in 2016 as an example, the integration of historical tourist flow data and related data is as follows:

[0037] Table Tourist scenic spot historical tourist flow data and rel...

Embodiment 3

[0063] Based on the principle of Embodiment 1, this embodiment discloses a detailed embodiment solution.

[0064] A. Collect historical tourist flow data of tourist attractions. The source of the data can be historical tourist flow data from tourist attractions, historical ticket sales data, or tourist reception data from tourism authorities. Sort and organize.

[0065] B. Obtain the associated data of the above-mentioned historical tourist flow data corresponding to the time period. The associated data includes at least one of the highest temperature, the lowest temperature, weather, wind direction, wind force, and working day conditions, and compare the historical tourist flow data with the day. Linked Data Summary.

[0066] Taking the historical tourist flow data of a tourist attraction in 2016 as an example, the integration of historical tourist flow data and related data is as follows:

[0067] Table 3 Examples of historical tourist flow data and associated data in tour...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a tourist flow prediction method based on machine learning, which comprises the following steps: collecting historical tourist flow data of tourist scenic spots and sorting thedata according to year, month and day; acquiring correlation data corresponding to the period of time of the historical tourist flow data, wherein the correlation data comprises at least one of a maximum temperature, a minimum temperature, a weather, a wind direction, a wind force and a working day, and summarizing the historical tourist flow data and the correlation data in a unit of days; converting the associated data into numerical values and fusing them with historical tourist flow data; inputting the related data and historical tourist flow into the learner for training to realize the prediction of tourist flow. The technical scheme utilizes machine learning method, comprehensively considers the intrinsic correlation of various factors affecting tourist flow in scenic spots, and assists the weighting calculation method, in order to improve the accuracy, scientificity and convenience of tourist flow prediction.

Description

technical field [0001] The present invention relates to the field of computer data processing and analysis, in particular to a method for predicting tourist flow based on machine learning. Background technique [0002] Tourist flow forecasting has always been a hot and difficult issue in tourism research. At present, the main method used is to predict tourist flow based on historical tourist flow data and considering the weighting method of influencing factors. For example, the invention patent with the publication number CN106779247A discloses a forecasting method based on the entropy value method for combined optimization of tourism demand, which determines the index weight according to the size of the information provided by the observed value of each index, and predicts according to the secondary factors. The value is corrected; the invention patent with the publication number CN106779196A discloses a tourist flow prediction and peak regulation method based on tourism bi...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06Q10/04G06Q50/14
CPCG06Q10/04G06Q50/14
Inventor 周道华古鹏飞曾俊
Owner 成都中科大旗软件股份有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com