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
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  • 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 exhaus...

Method used

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  • 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...

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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

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Application Information

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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
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