Optimal dwelling time prediction for package tour using K-nearest neighbor classification algorithm

被引：0

作者：

Wahyutama, Aria Bisma ^{[1
]}

Hwang, Mintae ^{[1
]}

机构：

[1] Changwon Natl Univ, Dept Informat & Commun Engn, Chang Won, South Korea

来源：

ETRI JOURNAL | 2024年 / 46卷 / 03期

关键词：

dwelling time prediction; K-nearest neighbor classification; machine learning; optimized schedule; web application;

D O I：

10.4218/etrij.2022-0454

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We introduce a machine learning-based web application to help travel agents plan a package tour schedule. K-nearest neighbor (KNN) classification predicts the optimal tourists' dwelling time based on a variety of information to automatically generate a convenient tour schedule. A database collected in collaboration with an established travel agency is fed into the KNN algorithm implemented in the Python language, and the predicted dwelling times are sent to the web application via a RESTful application programming interface provided by the Flask framework. The web application displays a page in which the agents can configure the initial data and predict the optimal dwelling time and automatically update the tour schedule. After conducting a performance evaluation by simulating a scenario on a computer running the Windows operating system, the average response time was 1.762 s, and the prediction consistency was 100% over 100 iterations.

引用

页码：473 / 484

页数：12

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