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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Optimal dwelling time prediction for package tour using K-nearest neighbor classification algorithm

  • Aria Bisma Wahyutama (Department of Information and Communication Engineering, Changwon National University) ;
  • Mintae Hwang (Department of Information and Communication Engineering, Changwon National University)
  • Received : 2022.12.24
  • Accepted : 2023.03.30
  • Published : 2024.06.20
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Abstract

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.

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References

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