Implementing k-means clustering algorithm in collaborative trip advisory and planning system

Abstract

Fueled by the help of the Internet, more and more tourists nowadays tend to plan trips by themselves in order to have trip plans that meet their preferences and convenience perfectly. However, tourists may face some problems when they plan a trip by themselves, which makes the whole trip planning process challenging and tiring. These problems include extensive tourism information, manually constructing an itinerary for a trip and difficulty in satisfying the needs of all trip participants. Therefore, a web-based trip planning system is proposed in this project to solve all these problems in order to help tourists in planning their desired trips more effectively and efficiently. This system will help users to search for attractions and restaurants in Southeast Asian countries faster by providing filtering and prioritizing features. This system also facilitates the decision-making process of tourists when choosing places to visit and restaurants by utilizing the power of word-of-mouth (reviews). Besides, this system will aid tourists by reducing the need to manually construct the itinerary for a trip. The k-means clustering algorithm will be used to auto-arrange the trip itinerary to ensure places close to each other are arranged to be visited on the same day so that tourists can save on unnecessary transport costs and time. Lastly, this system promotes collaborative trip planning by providing a platform for all the participants in a trip to discuss and plan their trip together.