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.

2. The Problem
Multi-Day and Multi-Stay Travel Planning
using Geo- Tagged Photos
Nov 5, 2013
GEOCROWD 2013
Xun Li
2

3. 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

4. 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

5. 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

6. Framework
6

7. 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} ]

8. 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

9. Multi-day and Multi-stay Travel Planning using Geo-tagged Photos
9

10. 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

11. 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

12. 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

13. 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

14. Results
• 2,135 POIs in Australia
14
OPTICS Result POI and Travel Patterns

15. Results
A 2-day tourist trip itinerary, which starts and ends at
Sydney International Airport
15

16. 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

17. Results
A 4-day tourist trip itinerary, which starts and ends at
Sydney International Airport
17

18. 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

19. Validation
19

20. Validation
20

21. 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

22. Future Work
Validation
Survey
22

23. Thanks!
The Problem
Nov 5, 2013
GEOCROWD 2013
Xun Li
23