1. Siyuan Liu *#, Yunhuai Liu *, Lionel M. Ni *# +, Jianping Fan #, Minglu Li + * Hong Kong University of Science and Technology # Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences + Shanghai Jiao Tong University July 27 th, 2010@SIGKDD 2010 Towards Mobility-based Clustering

2. Outline Introduction Related work Mobility based clustering Field study evaluation Conclusion

3. Outline Introduction Related work Mobility based clustering Field study evaluation Conclusion

4. Smart City [1] China's urbanization Massive issues and problems City monitoring and management Pervasive information and knowledge Digital technology Data collection, storage and mining Real life data sets Vehicle GPS data sets (one year, two cities) Mobile phone networks data sets (one year, two cities) Hot spot detection in the city [1] Smart City Research Group. http://www.cse.ust.hk/scrg

5. Motivation Vehicle instant locations of sample taxis at 13:00PM on 12 th Dec, 2006 Traffic congestion Event detection Commercial promotion Crowded spots and areas in the city

6. Data set Ideal case: we should have all information of all vehicles in the city Reality: only a sample set of all vehicles Taxi GPS data (ID, location, speed, time, direction, status) 0.3% of the two million vehicles in Shanghai Could we utilize such a very limited sample set to detect hot spot in the city?

7. Challenges Extremely limited sample set Dense? Sparse? Sparse! Dense! Notable location error Could density based clustering handle it?

8. Methodology Observation The low speed may indicate that the area is crowded Method Mobility-based clustering Study the speed (mobility) instead of the density Moving objects as sensors

9. Outline Introduction Related work Mobility based clustering Field study evaluation Conclusion

10. Related Work Static objectsMoving objectsProblems Partitioning methods Hierarchical methods Density based methods Grid based methods Model based methods etc. Raw data based methods Feature based methods Model based methods etc.  What if the mobility is high?  What if the density is poor?  What if the location is lossy? 10 Density based clustering

11. Outline Introduction Related work Mobility based clustering Field study evaluation Conclusion

12. Mobility-based Clustering Field study evaluation Learning in practice Crowded- ness model validation Spot crowded- ness model Object mobility model 12 Roadmap

13. Object Mobility Model Speed estimation Road network grid Interpolation Direction distinguishing Speed spectrum of road directionSpeed spectrum of reverse direction NanPu Bridge 13

14. Spot Crowdedness Model Linear crowdedness function Statistical crowdedness function 14

15. Crowdedness Model Validation Validation 1. Taxis traces 2. Buses traces 15

16. Learning in Practice Characterizing spots α, г Sensor object profiling Hot spots and hot regions Temporal hot spots Evolutionary hot regions Spot crowdedness 16 Learning in Mobility Based Clustering

17. Learning in Practice Characterizing spots 17

18. Learning in Practice Sensor object profiling 18

19. Learning in Practice Sensor object profiling 19

20. Learning in Practice Hot spots and hot regions 20 Hot spot, even sparse sample points NOT hot spot, even dense sample points

21. Learning in Practice Temporal hot spots Event detectionTemporal consistence

22. Learning in Practice Evolutionary hot regions Area difference ratio Crowdedness difference ratio 22

23. Outline Introduction Related work Mobility based clustering Field study evaluation Conclusion

24. Field Study Evaluation 24

25. Field Study Evaluation 25

26. Outline Introduction Related work Mobility based clustering Field study evaluation Conclusion

27. Conclusion and Future Work Contributions Mobility-based clustering model Key factors on spot crowdedness Hot spots and hot regions Future work More accurate speed information More accurate location information

28. Thanks for your attention!