1. Mobility Trajectory Mining Human Mobility Modeling at Metropolitan Scales
Sibren Isaacman
2012 Mobisys
Jie Feng
2016 THU FIBLab

2. Syllabus Motivation Challenge and Contribution Data Description Model
Spatial and temporal parameters
Evaluation
Application
Discussion

3. Motivation CORE Previous work: WHERE: Goals:
ad hoc, university campus, universal model
Still need a realistic model: matches empirical observations for large and distinct geographic areas.
WHERE:
how large populations move within different metropolitan areas
Goals:
Individual: how they move between important places in their lives
Aggregate: reproduce human densities over time at metropolitan areas
Application: evaluate what-if scenarios
Look at, not realistic, limited, not fit to specific area
New Model
How the addition of a new residential or employment area might change traffic patterns.
CORE

4. Challenge and Contribution
No semantic info: important place
Coarse granularity: cell tower, active
Contribution:
synthetic CDRs: Less storage, open data, what-if
Model: generate synthetic CDRs
Validation and Application
Large-scale validation: NY and LA
Daily ranges for travel
Overcoming challenge

5. Data Description CDRs US Census Data(detailed info)
Home/work locations and commute distances
Previously Published data
Calling Patterns
Enough
No such data
Performance loss
[1] S. Almeida, J. Queijo, and L. Correia. Spatial and temporal traffic distribution models for gsm. In Vehicular Technology Conference, Sept
[5] J. Candia, M. C. González, P. Wang, T. Schoenharl, G.Madey, and A.-L. Barabási. Uncovering individual and collective human dynamics from mobile phone records. MATH.THEOR., 41:224015, 2008.

6. Model: Individual Detail Input: real CDRs/Census
60%
Input: real CDRs/Census
Step1:Home distribution
Step2:Commute Distance
Step3:Work ……
Step1: Identify the key properties of human movement
Step2: Use PDs to generate synthetic CDRS

7. Spatial and Temporal Parameters
Spatial Information: Important Locations
Previous work+: statistics and regression
Spatiotemporal Information: Hourly Population Densities
CDRs: call records = population
Census: 7pm-7am(home),7am-7pm(work)
Temporal Information: Calling Patterns
PerUserCallsPerDay: average and std
When
Standard deviation of calls
+ S. Isaacman, R. Becker, R. Cáceres, S. Kobourov,M. Martonosi, J. Rowland, and A.Varshavsky. Identifying important places in people’s lives from cellular network data. In 9th International Conf. on Pervasive Computing, 2011.

8. Evaluation Earth Mover’s Distance (EMD) Comparison Models
Synthetic pop density VS real pop density(real CDRs)
Image test*
Human readable
Comparison Models
Random Waypoint: RWP
r ori->r des-> r vel->r wait -> ……
r:random
Weighted Random Waypoint: WRWP
Selected home/work
r velocity and r wait
location shift transform
EMD attempts to find the minimum amount of energy required to transform one probability distribution into another
* Y. Rubner, C. Tomasi, and L. J. Guibas. A metric for distributions with applications to image databases. In Proc.IEEE International Conference on Computer Vision, 1998.

9. Application(boxplot)
Daily ranges of travel
Message Propagation
Hypothetical Cities
If 10% work at home

10. Discussion Group mobility Related work Future work?
Model group migration between street blocks during the day
Data: Mobile phone data
Every group size is the point
Related work
Gravity model between cities
Origin-Destination Matrix
SIR model (susceptible, infected, removed)
Individual model
Ad-hoc network
Future work?
Community detection + OD matrix + something……