Travel Route Planning Based On User-generated Content

Travel route planning is an important step for a tourist to prepare his/her trip. As a common scenario, a tourist usually asks the following questions when he/she is planning his/her trip in an unfamiliar place: 1) Are there any travel route suggestions for an one-day or three-day trip in Beijing? 2) What is the most popular travel path within the Forbidden City? 3) It seems that I have got lost in the Forbidden City, which direction/ path should I go along next? Currently, users have to read travelogues one by one from the Web and check the information from various forums to reach customized trip plans. To facilitate a tourist's trip planning, in this paper, we target at solving the problem of automatic travel route planning.We propose to leverage existing travel clues recovered from user-generated content, i.e., geo-tagged photos and world travelogues to suggest customized travel route plans according to users'preferences. As the footprints of tourists at memorable destinations, the geo-tagged photos could be naturally used to discover the travel paths within a destination (attractions/landmarks) and travel routes between destinations. Travelogues are employed to map users'preference with destinations. Based on that information, we can provide a customized trip plan for a tourist, i.e., the popular destinations to visit, the visiting order of destinations, the time arrangement in each destination, the typical travel paths within each destination, and instant trip guider within large destinations. Users are also enabled to specify personal preferences such as visiting location, visiting time/season, travel duration, and destination style in an interactive manner to guide the system.Owning to 20 million geo-tagged photos and 200,000 travelogues, an online system has been developed to help users plan travel routes for over 30,000 attractions/ landmarks in more than 100 countries and territories. Instant guidance within large destinations is also developed based on the travel paths mined within destinations. Experimental results show the intelligence and effectiveness of the proposed framework.