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Travel Plan using Geo-tagged Photos in Geocrowd2013

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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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Travel Plan using Geo-tagged Photos in Geocrowd2013

  1. 1. The Problem Multi-Day and Multi-Stay Travel Planning using Geo- Tagged Photos Nov 5, 2013 GEOCROWD 2013 Xun Li 2
  2. 2. Related Work in Geo-tagged Photos Research Exploring landmarks or attraction places “k-means” mean shift density based clustering hierarchical clustering 3 Mining travel patterns from geo-tagged photos weighted plotting travel routes lines flow maps traffic flows Making travel recommendations/plans based on geo-tagged photos • One-day travel planning • Multi-day travel planning
  3. 3. Related Work in Operational Research Orienteering Problem • Given a set of attractions and a time budget, find a tour to maximize the collected scores from selected attractions Team Orienteering Problem (TOP) with Time Window(TOPTW) • Given a set of attractions and a time budget, send out several teams to find a set of tours to maximize the collected scores from selected attractions • TOP problem plus setting up attractions with different time windows Algorithms: • Heuristic solutions (e.g. dynamic programming, greedy algorithm, iterated local optimum search ) Limitations: • Travel attractions are from existed resources (e.g. tourist offices) • Attractiveness scores of POIs are predefined with categorical scores according to the types of attractions (e.g. museum, archaeology, nature etc.) • Correlations between POIs are not considered. 4
  4. 4. Research Problem Automatically recommend multi-day and multi-stay travel plan to travellers based on travel knowledge that mined from Geo-tagged Photos • Integrates Geo-tagged Photos research with latest techniques 5 in operational research • Driven by rich travel information mined from geo-tagged photos: • Points of interest, attractive score, suggested visiting time, opening and closing time, travel recurrence weights • Relevance • Tourism research and practice • Personal guide services
  5. 5. Framework 6
  6. 6. Finding POIs from Geo-Tagged Photos 7 Algorithm: Ordering Points To Identify the Clustering Structure • Density-based clustering • Hierarchical clustering structure POI Properties: • Name: the peak (Mean Shift) is mapped to and labeled using the nearest feature from a preloaded OSM data • Attractive Score Si : # of troutists • Suggested Visiting Time Ti : avg( tlast_photo-tfirst_photo) • Time Window [Otime>8am , Ctime<6pm]i : [min{tfirst_photo}, max{ tlast_photo} ]
  7. 7. Travel Information and Travel Graph Model 8 Travel Information: • Traveling time between POIs: using OSM data and Open Source Routing Machine System (OSRMS, Luxen et. al. 2011) • Traveling weights and Travel Graph Model: Reconstruct individual travel route Travel graph model Recurrence weight of sub-trip
  8. 8. Multi-day and Multi-stay Travel Planning using Geo-tagged Photos 9
  9. 9. A Heuristic Solution 10 A modified Iterative Local Search based heuristic algorithm to solve the multi-day and multi-stay travel plan problem • A variant of TSP problem (NP) • Approximate optimal solution as fast as possible Algorithm • A variant of TSP problem (NP) • Approximate optimal solution as fast as possible
  10. 10. Heuristic Solution—Construct Step Initialize tour with virtual POIs • Virtual POI: no location information Inserting POIi between POIi and POIk • Find best candidate • : reoccurring travel weights • • Filtering: 11
  11. 11. Heuristic Solution—Shake Step Shake to remove a set of selected POIs from each sub-tour • Proved in [] as a good technique to explore the entire solution space and correct earlier mistake solution 12
  12. 12. Experiments Application area • Australia (Sydney) • Tourism industry contributes 3% GDP (2011) • 5.9 million international tourists (2011) Data: • 118,736 geo-tagged photos contributed by 4,920 registered Internet users in Panoramio.com • Average 24 geo-tagged photos per user 13
  13. 13. Results • 2,135 POIs in Australia 14 OPTICS Result POI and Travel Patterns
  14. 14. Results A 2-day tourist trip itinerary, which starts and ends at Sydney International Airport 15
  15. 15. Results The detail of the 2-day tourist trip itinerary is shown below: Day 1 (pink route): start from Sydney International Airport at 8am; drive about 0.12 hours to Chinese Garden of Friendship at 8:20, spend 1 hour there; drive 0.01 hours to Sydney Town Hall at 9:30, spend about 2.4 hours there; drive 0.01 hours to Sydney Aquarium at 12:00, spend about 1.5 hours there; drive 0.03 hours to the Mercantile at 13:40, spend 3.2 hours there; drive 0.15 hours to the Gap Park at 16:50, spend 1 hour there; drive 0.14 hours to Sydney Harbor Bridge at 18:00, find a hotel nearby to stay. Day 2 (green route): start from near Sydney Harbor Bridge at 8am, spend about 3.9 hours there; drive 0.01 hours to Sydney Opera House at 11:50, spend about 3.9 hours there; drive 0.01 hours to Museum of Contemporary Art at 15:30 and spend about 1.9 hours there; drive 0.15 hours to Sydney International Airport at 18:00. 16
  16. 16. Results A 4-day tourist trip itinerary, which starts and ends at Sydney International Airport 17
  17. 17. Results The detail of the 4-day tourist trip itinerary is shown below: Day 1 (pink route): start from Sydney International Airport at 8am; drive 0.15 hours to Customs House at 8:15, spend about 4.5 hours there; drive 1.8 hours to The Giant Stairway at 14:45, spend about 1hour there; drive 0.01 hours to The Three Sisters at 15:40, spend about 1.5 hours there; drive 0.02 hours to Scenic World Blue Mountains at 16:50, spend about 1 hour there; drive 1.8 hours to Sydney Aquarium, and find a hotel nearby to stay Day 2 (green route): start from Sydney Aquarium at 8am, spend about 1.7 hours there; drive0.03 hours to Royal Botanic Gardens at 9:50, spend about 1.6 hours there; drive 0.03 hours to Milsons Point at 11:20, spend about 2.5 hours there; drive 0.01 hours to Olympic Pool North Sydney at 13:50, spend about 2.5 hours there; drive 0.15 hours to The Gap Park at 16:35, spend about 1 hour there; drive 0.14 hours to Sydney Opera House, and find a hotel nearby to stay Day 3 (blue route): start from Sydney Opera House at 8am, spend 4 hours there; drive 0.01 hours to Sydney Visitors Information Centre at 12:00, spend about 4 hours there; drive 0.01 hours to Museum of Contemporary Art at 16:20, spend about 1 hours there; drive 0.03 hours to Chinese Garden of Friendship at 17:20, spend about 0.5 hour there; drive 0.05 hours to Sydney Harbour Bridge, and find a hotel nearby to stay Day 4 (light yellow route): start from Sydney Harbour Bridge at 8am, spend about 3.5 hours there; drive 0.01 hours to the Mercantile at 11:30, spend about 3.2 hours there; drive 0.02 hours to the Cenotaph at 14:50, spend about 0.8 hours there; drive 0.01 hours to the Sydney Town Hall at 15:30, spend about 2.3 hours there; drive 0.13 hours to Sydney International Airport at 18:00. 18
  18. 18. Validation 19
  19. 19. Validation 20
  20. 20. Conclusion Main contribution • An Intelligent Tourist Trip Plan System • Solve multi-day and multi-stay travel plan problem using modified ILS based heuristic algorithm • More applicable to realistic problems than existing solutions • large Internet social media data • results visualization (travel patterns, travel plans) 21
  21. 21. Future Work Validation Survey 22
  22. 22. Thanks! The Problem Nov 5, 2013 GEOCROWD 2013 Xun Li 23

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