Abstract
By utilizing large amount of crowd volunteered geo-tagged photos, existing research can successfully discover landmarks or attractive areas, mine travel patterns, find classical travel routes and recommend travel destinations or routes for inexperienced tourists. However, few of them focuses on a complicated real-life travel planning problem--planning multi-day and multi-stay (different places of accommodation) travel for tourist. By integrating new techniques in data mining and operational research, we develop a novel travel planning system to design multi-day and multi-stay travel plans based on geo-tagged photos. Specifically, a modified Iterated Local Search heuristic algorithm is developed to find an approximate optimal solution for the multi-day and multi-stay travel planning problem using points of interests (POIs) and recurrence weights between POIs in a travel graph model, which are discovered from photos. To demonstrate the feasibility of this approach, we retrieved geo-tagged photos in Australia from the photo sharing website Panoromia.com to design experimental multi-day and multi-stay travel plans for tourists. The travel patterns that are mined using flow-mapping technique at different geographical scales are used to evaluate the experimental results.
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