• Journal of Korea Society of Industrial Information Systems
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• v.25 no.1
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• pp.31-40
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• 2020

In this study, we developed an algorithm for automatic travel itinerary planning based on expert recommendation. The proposed algorithm generates an itinerary by patterning a number of travel routes based on the automatic itinerary generation method based on the routes recommended by travel experts. To evaluate the proposed algorithm, we generated 30 itinerary for Singapore, Bankok, and Da Nang using both algorithms and analyzed the mean difference of trip distances with t-test and interater reliability of those itineraries. The result shows that the itineraries based on the proposed algorithm is not different from that of VRP(Vehicle routing problem) algorithm and interater reliability is high enough to show that the proposed algorithm is effective enough for real-world usage.

• KIISE Transactions on Computing Practices
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• v.22 no.1
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• pp.38-43
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• 2016

Recent developments regarding transportation technology have positioned travel as a major leisure activity; however, trip-itinerary planning remains a challenging task for tourists due to the need to select Points of Interest (POI) for visits to unfamiliar cities. Meanwhile, due to the GPS functions on mobile devices such as smartphones and tablet PCs, it is now possible to collect a user's position in real time. Based on these circumstances, our research on an automatic itinerary-planning system to simplify the trip-planning process was conducted briskly. The existing studies that include research on itinerary schedules focus on an identification of the shortest path in consideration of cost and time constraints, or a recommendation of the most-popular travel route in the destination area; therefore, we propose a personalized itinerary-recommendation system for which the stay-time preference of the individual user is considered as part of the personalized service.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.117-125
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• 2022

Recently, as users' interest for travel increases, research on a travel route recommendation service that replaces the cumbersome task of planning a travel itinerary with automatic scheduling has been actively conducted. The most important and common goal of the itinerary recommendations is to provide the shortest route including popular tour spots near the travel destination. A number of existing studies focused on providing personalized travel schedules, where there was a problem that a survey was required when there were no travel route histories or SNS reviews of users. In addition, implementation issues that need to be considered when calculating the shortest path were not clearly pointed out. Regarding this, this paper presents a quantified method to find out popular tourist destinations using social big data, and discusses problems that may occur when applying the shortest path algorithm and a heuristic algorithm to solve it. To verify the proposed method, 63,000 places information was collected from the Gyeongnam province and big data analysis was performed for the places, and it was confirmed through experiments that the proposed heuristic scheduling algorithm can provide a timely response over the real data.