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Mobility Trajectory Mining
Human Mobility Modeling
at Metropolitan Scales
Sibren Isaacman
2012 Mobisys
Jie Feng
2016 THU FIBLab
Syllabus
• Motivation
• Challenge and Contribution
• Data Description
• Model
• Spatial and temporal parameters
• Evaluation
• Application
• Discussion
Motivation
• Previous work:
• 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
CORE
Challenge and Contribution
• Challenge:
• 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
Data Description
• CDRs
• US Census Data(detailed info)
• Home/work locations and commute distances
• Previously Published data
• Calling Patterns
[1] S. Almeida, J. Queijo, and L. Correia. Spatial and temporal traffic distribution models for gsm. In Vehicular Technology Conference, Sept. 1999.
[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.
Model: Individual
Detail
Step1: Identify the
key properties of
human movement
Step2: Use PDs to
generate synthetic CDRS
Input: real CDRs/Census
Step1:Home distribution
Step2:Commute Distance
Step3:Work ……
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
+ 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.
Evaluation
• Earth Mover’s Distance (EMD)
• Synthetic pop density VS real pop density(real CDRs)
• Image test*
• Human readable
• Comparison Models
location shift transform
• 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
* 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.
Application(boxplot)
• Daily ranges of travel
• Message Propagation
• Hypothetical Cities
• If 10% work at home
Discussion
• Group mobility
• Model group migration between street blocks during the day
• Data: Mobile phone data
• Every group size is the point
• Related work
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•
•
•
•
Gravity model between cities
Origin-Destination Matrix
SIR model (susceptible, infected, removed)
Individual model
Ad-hoc network
• Future work?
• Community detection + OD matrix + something……