1. Project Assignment 1 - Research Paper Presentation
Understanding Transportation Modes Based on GPS Data for Web Applications
Project Assignment 1 - Research Paper Presentation
Instructor: Dr. Wei Ding
Team 1: Priyanka Das, Max Ward, and Jacky Yu
Feb. 24, 2011

2. Understanding Transportation Modes Based on GPS Data for Web Applications
Authors Yu Zheng, Microsoft Research Asia Yukun Chen, Tsinghua University Quannan Li, Huazhong University of Science and Technology Xing Xie, Microsoft Research Asia Wie-Ying Ma, Microsoft Research Asia Publication ACM Transactions on The Web, Vol. 4, No. 1, Article 1, January 2010
Authors - in this instance these authors are connected through their work on GeoLife, a concept being developed by Microsoft.
Publication - ACM is Association for Computing Machinery which has as its mission to Advance Computing as a Science and Profession.
Transactions on the Web (TWEB) is a journal publishing refereed articles reporting the results of research on Web content, applications, use, and related enabling techologies.
Important motivation is a shared interest in what they call prolific or ubiquitous computing, or computing that is everywhere...
----- Meeting Notes (2/20/11 14:16) -----
Feb. 24, 2011

3. Ubiquitous Computing Main Motivation
Social networking - “friends” vs. “people you encounter everyday”
Travel Blogging - manual input is onerous
Route planning - not mode based
Practical application examples - drug store to do list....
Feb. 24, 2011

4. Presentation Outline Introduction and Terminology- Max
Approach Architecture - Priyanka
Experiments - Jacky
Conclusion - All
Introduce the team and our roles
Names and topics
about 15 minutes each
finish with our own thoughts about the research and its application
Feb. 24, 2011

5. Key Considerations Understanding Transportation Modes
Mode Detection and Change Detection
GPS Data
Introduce the topic - what we are talking about...
High level discussion of Mode detection and what that means
High level discussion of change detection and what that means
Detection based on GPS data alone
Introduction
Feb. 24, 2011

6. Bus Drive Bike Walk Illustration of the issue
Phase in dots then key then map
Talk about different transportation modes and how the addition of a mapping layer makes it easier to understand
Introduction
Feb. 24, 2011

7. Understanding Transportation Modes
Speed
Distance
Direction
Location
Time
Walk Drive Bus Bike
What are the main features differentiate these transportation modes?
What differentiates modes?
How are modes similar?
Are there points when modes might be indistinguishable from one another?
How do you tell the difference?
Introduction
Feb. 24, 2011

8. Accurate Prediction Introduction Feb. 24, 2011
Need Images to illustrate the points about the difficulty of mode detection based on these issues Make three slides with three different images...
Previous studies problems at end of paper
Problems with Velocity
Problems with Additional Data Reliance
Introduction
Feb. 24, 2011

9. Bus Drive Bike Walk Transportation really happens like this:
First you walk then you may change to some other mode, and then you may change back to walking.....
Example of google map directions from one Boston landmark to another.
Introduction
Feb. 24, 2011

10. Key Finding
Walking is highly involved with all investigated transportation modes.
Introduction
Feb. 24, 2011

11. Preliminary Concepts GPS Log comprised of GPS Points GPS Trajectory
Concrete examples.
GPS Log Longitude latitude and time stamp
GPS Trajectory
Walk Segment and Non-walk Segment
Change Points
Walk Points and Non-walk Points
Bike, Bus, Driving (or riding or taking taxi), Walk
Change Point
Walk Segment
Non-Walk Segment
Terminology
Feb. 24, 2011

12. Heading Direction = p1.head
Calculated Features p1 p3 p2
GPS Points = p1, p2, p3
p1.head
p2.head
Heading Direction = p1.head L1
Spatial Distance = L1 T1
Temporal Interval = T1
p1.V1
Velocity = p1.V1 (L1/T1)
Concrete examples
Given GPS points p1 and p2
Spatial Distance L1
Temporal Interval T1
Heading Direction p1.head
Velocity p1.V1 = L1/T1
Heading Change H1 = |p1.head-p2.head| H1
Heading Change = H1
Basic Features
Distance of a segment
Max. Velocity
Max. Acceleration
Avg. Velocity
Expected Velocity
Variance of Velocity
Advanced Features
Heading Change Rate
Stop Rate
Velocity Change Rate
Terminology
Feb. 24, 2011

13. Infer Transportation Modes
Architecture of Approach
Spatial Knowledge Extraction
Change Point-Based Segmentation
Graph Based Post-processing
Framework of Post-processing
Feature Extraction
Normal Post-processing
Prior Probability-based Enhancement on Graph
HCR { Heading Change Rate }
SR { Stop Rate }
Transition Probability-based Enhancement on Graph
VCR {Velocity Change Rate }
Inference Model
Approach Architecture
Feb. 24, 2011

14. Architecture of Approach
Approach Architecture
Feb. 24, 2011

15. Change Point-based Segmentation
How change points can be detected automatically from a given GPS log?
The detecting approach is derived from the commonsense knowledge of the real world.
Typically, people need to walk before transferring transportation modes
Typically, people need to stop and then go when transferring modes
Approach Architecture
Feb. 24, 2011

16. Fig.1. The distribution of the maximum velocity of a segment
Fig.2. The distribution of the average speed of a segment
Fig.3. The distribution of the maximum
acceleration of a segment.
Approach Architecture
Feb. 24, 2011

17. Change Point-based Segmentation
Fig.4. An example of detecting change points
An individual transfers from bus to driving using walk as a transition
Step 1: Distinguish all possible Walk Points, non-Walk Points.
Step 2: Merge short segment composed by consecutive Walk Points or non-Walk points
Step 3: Merge consecutive Uncertain Segment to non-Walk Segment.
Step 4: End point of each Walk Segment are potential change points
Approach Architecture
Feb. 24, 2011

18. Feature Extraction
Fig.5(a). Heading change rate
Fig.5(b). Velocity change rate and Stop rate
Heading Change Rate (HCR): The frequency that people change their heading direction to some extent within a unit distance.
Stop Rate (SR): The stop frequency of a moving object within a unit distance.
Velocity Change Rate (VCR: It is the number of GPS points, whose velocity change % over its prior point exceeds a certain threshold, within a unit distance.
Approach Architecture
Feb. 24, 2011

19. Inference Model
Given the features X of each segment , we can interpret its transportation
Mode based on supervised learning method.
Using a training dataset, which contains a set of features-class pairs
We can train an inference model to classify the coming segments in the future.
The model built is universal to understand the GPS trajectories for a variety of people.
Approach Architecture
Feb. 24, 2011

20. Fig.6. Mining spatial knowledge from GPS logs.
Spatial Knowledge Extraction
Fig.6. Mining spatial knowledge from GPS logs.
Approach Architecture
Feb. 24, 2011

21. Graph-Based Post Processing
Fig.7. Flowchart of the graph-based post processing
Approach Architecture
Feb. 24, 2011

22. Normal Post Processing
Segment[i].P(Bike) = Segment[i].P(Bike) * P(Bike | Driving)
Segment[i].P(Walk) = Segment[i].P(Walk) * P(Walk | Driving)
Approach Architecture
Feb. 24, 2011

23. Prior Probability-based Enhancement
Transition Probability-based Enhancement
Probability is location constrained and contains more commonsense of real world.
Approach Architecture
Feb. 24, 2011

24. Data Gathering Experiments Feb. 24, 2011
Limitation - Outdoor movements only.
Record data every 2 seconds.
65 users over 10 months.
Collect data and update mode by users everyday.
Total distance more than 30,000 km. (19,000 Miles)
Total duration more than 2,000 hrs.
Covers most of big cities in China, some cities in other countries.
Experiments
Feb. 24, 2011

25. Data Processing 70% used for training set, the rest for test set.
Balanced on # of mode for both datasets.
Divided into segments by Change Point-Based method.
Compare with other segmentation methods later.
So, it is a supervised learning.
Why later? Before we can evaluate the methods, need to do feature selection. Therefore, every method will use same features.
Also, how to evaluate?
Experiments
Feb. 24, 2011

26. Evaluation Approach Inference Accuracy N : # of the segments
m: # of segments correctly predicted
Problem with As: 99 very short (walk) segments and 1 very long (bus) segments = 99%
AD is more objective to measure the inference accuracy
Recall is more important than Precision at this stage because do not want to miss change point
Recall and Precision on Change Points
walk
walk
Wrong prediction (fp)
walk
Actual change point (tp)
Missed change point (fn)
walk
bike
walk
Experiments
Feb. 24, 2011

27. Chosen: V <= 2.5 (m/s) and a <= 1.5
Segmentation Process
Why high Recall first? Still have chances to improve precision.
Find all the possible change points even with wrong production, can be fixed at next step by merging segments as well as post-process.
Chosen: V <= 2.5 (m/s) and a <= 1.5
Step 1: Walk-Segment
Test the thresholds of V (Velocity) and a (acceleration).
High recall with an acceptable precision.
Experiments
Feb. 24, 2011

28. Segmentation Process Chosen: MDB = 20m, TS = 10s
Why do merge? Maybe a short stop on traffic light, or making turns.
Chosen: MDB = 20m, TS = 10s
Step 2: Merging Segments
Minimal Distance Bound (MDB), Minimal Time Span (TS).
Segment’s distance < MDB OR its time span < TS, than merged into its backward segments.
Experiments
Feb. 24, 2011

29. Segmentation Process Chosen: DT = 200m, SN = 2
Determent what is an uncertain segment
Chosen: DT = 200m, SN = 2
Step 3: Uncertain Segments
SN = # of consecutive Uncertain Segments.
DT = Distance of a Certain Segment
Segment’s distance < DT, it is an Uncertain Segment.
If SN > n, these segments will be merged into one segment.
Experiments
Feb. 24, 2011

30. Advanced Features HCR: 15 degree SR: 2.5 m/s VCR: 0.7 |V2-V1| / V1
Strong features to identify transportation mode.
Less direction change while driving or bus.
What indicates a head change? (turning degree)
If the H > Hc, the corresponding GPS point will be indicated as a Head-Change point.
Why SR Useful? To distinct taking bus with driver.
VCR becomes the most powerful beyond other candidates.
A sudden stop or speed up, can not be done while walking. Break or gear up.
VCR: 0.7
|V2-V1| / V1
Experiments
Feb. 24, 2011

31. Post-Processing Introduce T1 and T2 Thresholds T1 = 0.6 and T2 = 0.36
Transition Probability-Based
Normal Post-processing
Prior Probability-Based
P(mi |X) stands for the posterior
probability of a segment being a kind of transportation mode given feature X.
i.e. P(Walk | SR)
The segment falls on the right side is regarded as correctly identified.
Introduce T1 and T2 Thresholds
T1 = 0.6 and T2 = 0.36
max (P(mi |X)) > T1 : more than 90% correct rate to reduce the risk of modifying a correct prediction
max (P(mi |X)) < T2 : more than 60% false rate ensuring a false inference would be revised.
Experiments
Feb. 24, 2011

32. Post-Processing OPTICS algorithm
CorDist : core-distance
minPts: minimal points within the core-distance
"Ordering Points To Identify the Clustering Structure"
Instead of individual GPS coordination, form clustered change-points.
i.e. an intersection.
OPTICS algorithm
# of change point are constrained by the real world.
CorDist = 25 and minPts = 5 are adopted in this research.
Experiments
Feb. 24, 2011

33. Feature Selection Experiments Feb. 24, 2011
Advanced features standout.
Using the inference accuracy without postprocessing.
Using a subset feature selection method
Combination = SR+HCR+VCR with the Basic Features attain the highest accuracy.
Experiments
Feb. 24, 2011

34. Evaluate Segmentation Methods
Uniform Duration = divide trajectory into same duration segments (120s).
Uniform Distance = divide trajectory into same distance segments (200m).
Using obtained Enhanced Features.
Compare with Uniform Duration-Based and Distance-Based.
Set 120s on Duration-Based and 200m on Distance-Based achieved their best performance.
Experiments
Feb. 24, 2011

35. Final Results Experiments Feb. 24, 2011
The improvement can be achieved by collecting more data.
Experiments
Feb. 24, 2011

36. Key Points Change Point-Based segmentation
Maintaining a segment of a single mode as long as possible.
By merging, eliminate the traffic conditions.
Introducing Advanced Features
The characteristics of transportation mode;
Cars can not change direction as flexible as walking.
These characteristic shows regardless of traffic conditions.
Graph-Based Postprocessing
Learning from the given data, not using the real world feeds.
Not a “route”, but a network of connected change points.
Give you a wrap up.
Conclusion
Feb. 24, 2011

37. Our Thoughts Change Point-Based segmentation
Finding Driving/Bus segments first, instead of walk segments, to indentify the change points maybe more accurate since walking speed is limited.
AS seems more important than AD in the real world.
Maybe there are more effective and reasonable ways of setting thresholds.
The mode changing statistic have to be rebuilt while in different country.
Practical application may be better suited to route planning than social networking or travel advice.
Many thresholds are chosen based on common sense in this study.
Conclusion
Feb. 24, 2011

38. Questions?
Conclusion
Feb. 24, 2011