1. Urban Computing with Taxicabs
TMSG
Cappuccino
Oct. 12th, 2011

2. About the Authors..
Yu Zheng, Yanchi Liu, Jing Yuan, Xing Xie from M$RA
Yu Zheng
Ubicomp - Ubiquitious computing group
WSM - Web Search & Mining Group
Yu Zheng’s weibo :

3. About the Authors..
Yu Zheng, Yanchi Liu, Jing Yuan, Xing Xie from M$RA
Yanchi Liu, also from University of Science and Technology Beijing
Paper published by ACM on Sept ( ? )

4. Agenda About the authors(finished) Introduction Overview
Evaluation Settings
Evaluation Results
Related Works
Conclusion
Comments

5. Introduction GPS equips are generally used nowadays.
GPS-equipped taxicabs can be viewed as ubiquitious mobile sensiors, gathering datas of traffic flows.

6. Introduction
This paper’s goal is to detect the flawed and less effective urban planning in a city according to the GPS trojectories of taxicabs.
Saying Beijing has 35 million personal trips per day created by various kinds of vehicles and that 1.44 million personal trips are generated by taxi. Thus, the percentage of taxi trips from total trips are 4.2%. The author believed 4.2% is a significant sample reflecting to urban traffic flow.

7. Overview
Let’s introduce the variables one at a time..

8. Overview

9. Overview

10. Overview Consisted of two major components: Modeling citywide traffic
Detecting flawed planning

11. Overview
Disjoint regions using major roads.

12. Overview
Building region matrix: This process can be break down into 3 steps...
1. Temporal partition
Weekday/weekend
Rush hours/un-rush hours

13. Overview
1. Temporal partition

14. Overview 2. Transition construction
Pick out effective(occupied) taxi trajectories
Construct transitions between two regions according to Def. 3
Transitions Def. 3

15. Overview 2. Transition construction
Note that a trajectory discontinuously traversing two regions still formulate a transition between two regions.

16. Overview
2. Transition construction

17. Overview 2. Transition construction
Black point represents a region pair, while blue and red points are the projections of these re- gion pairs on XZ and YZ spaces.

18. Overview 2. Transition construction
Most taxis intend to travel through a shortcut instead of the roundabout route if the shortcut is effective. On the contrary, if most taxis pass additional regions, that means the route directly connecting two places is not very effective.

19. Overview
3. Build region matrix

20. Overview
Detecting flawed planning
Skyline detection

21. Overview
In this paper, which means there’s no region pair a(p, q) having a lower speed and bigger theta than those belongs to skyline.

22. Overview The detected skyline is comprised of 3 kinds of region pairs.
1. Very small E(V) and theta, which means the two regions are connected with some direct routes while the capacity of these routes are not sufficient.

23. Overview
2. A region pair with a small E(V) and big theta, which means people have to take detours and also suffer from a very slow speed. Worse case. 3. A region pair with a big E(V) and big theta. Meaning that the travel speed is fast but far, still has flaws.

24. Overview
Examples of 1, 2, and 3.

25. Overview Pattern mining from skylines(2 steps)
Formulating skyline graphs
2. Mining frequent sub-graph patterns
先點一下！

26. Overview Formulating skyline graphs
connect two consecutive slots if they are spatially close to each other.
There could be multiple
isolated graphs pertaining
to a day.

27. Overview
Formulating skyline graphs

28. Overview 2. Mining frequent sub-graph patterns
Mining the association rules.
The mined association rules can consist of over 2 patterns. E.g. g1, g2 => g3. Also, these association rules may NOT be geospatially close to each other.

29. Evaluation Settings
Taxi trajectories

30. Evaluation Settings Map data
Road network of Beijing, consist of 106,579 road vertices and 141,380 road segments.
Picked out 25,262 road segments w/ leveling from 0 to 6. Use only the 0 to 2 level. (0 is highest representing highways)
Create 444 regions in result.
106,579 路口 ; 141,380路段

31. Evaluation Settings Verify the detected flaws in the following 2 ways:
Verify the urban plannings that had been implemented between the times of the two datasets.
Check if some flaws that have been detected in both datasets by our methods embodied in the future urban planning of Beijing.

32. Evaluation Results

33. Evaluation Results
Sum up that the traffic conditions in Beijing become worse in 2010 than 2009.

34. Evaluation Results Taxi drivers took fewer passengers than b4.
The average speed dropped between the two years.
In short, the traffic condition became worse.

35. Evaluation Results
Most regions becomes shallower in 2010 than 2009, especially in hot areas.
The travel speed of taxis in these regions decreased.

36. Evaluation Results
By looking at the results, we observe two aspects: 1. Some flawed planning occurring in 2009 disappeared in The number of regions having defects increased in 2010 beyond 2009 and some flaws occuring in 2009 still exist.

37. Evaluation Results
Some flawed planning occurring in 2009 disappeared in 2010.

38. Evaluation Results Some flawed planning that still exists in 2010.
The planning of these two subway lines denotes that the urban planner has recognized the problem existing in the regions, justifying the validity of the results generated using our method.

39. Evaluation Results
Can also found association rules between the detected patterns.
Support = 0.05
Confidence = 0.7

40. Evaluation Results
We will show more interesting results and a live demo during the presentation at the conference.

41. Related works Mining taxi trajectories Urban computing
Effectiveness, like what I’ve presented last time.
Destination prediction, like what Danny presented last time.
Urban computing
Most papers do their researchs on social computing and some user interaction stuff while this paper explore the urban computing from the perspective of urban planning.

42. Conclusion
This paper detect the flaws in the existing urban planning of a city using the GPS trajactories of taxis traveling in the urban areas.

43. Conclusion Future plans:
1.Studying the geographic features of a region, such as the road segments and POI.
2. The purpose of people’s travel.
Point of interest

44. Comments Easy reading Interesting and well-applied on a useful case
Chart arrangement thing again.
Like reading a novel or comic, no highlights.

45. Urban Computing with Taxicabs
The End 