Vehicle Tracking with Fine-Grained GPS and Implications for Safety

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Center for Transportation Research - UT Austin

2015 D-STOP Symposium session by D-STOP Researcher Todd Humphreys. Watch the presentation at http://youtu.be/sSotvhKg3Wc?list=PLWQCGQLl10mwkino_uNmTO4JXOg5oCWtU Get symposium details: http://ctr.utexas.edu/research/d-stop/education/annual-symposium/

Technologie

Vehicle Tracking with Fine-Grained GPS
and Implications for Safety
Todd Humphreys, UT Austin
D-STOP Symposium | March 2, 2015

Cell-Resolution Mobility Traces
Montjoye et al., “Unique in the Crowd: Privacy Bounds of Human Mobility” Nature, 2013
• 93% predictable (theoretical limit) (Song et al., 2010)
• highly unique: with only 4 random spatio-
temporal points (1-hour time res.), 95% of traces
can be identified (Montjoye et al., 2013)
At cell-level granularity, mobility traces are ...

Eagle and Pentland, “Eigenbehaviors: identifying structure in routine,” Behav Ecol Sociobiol, 2009
Behavior from Cell-Resolution Mobility Traces
Eigenbehavior analysis allows discovery of behaviors that are correlated
across large gaps in time (e.g., I came in early so I can leave early and go to a
show tonight).

Google Now-Resolution Mobility Traces
What is the street value of this trace?

Zang and Bolot, “Anonymization of location data does not work” MobiCom, 2011
The value of mobility traces goes as
their information content
From 25 million cell users across U.S. Sprint network ...

Cell-level: What are your
daily patterns?
City-level: Where do you live?
Coarse GPS/WiFi-level:
What is your approximate context?
Decimeter-level:
What is your immediate present reality?

Why we may want our vehicles to know
their position at sub-decimeter accuracy:
• safety and security for connected veh.
• more efficient traffic flow
• personal assistant; forecasting
• proof of location

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Vehicle Tracking with Fine-Grained GPS and Implications for Safety

1. Vehicle Tracking with Fine-Grained GPS and Implications for Safety Todd Humphreys, UT Austin D-STOP Symposium | March 2, 2015

2. Cell-Resolution Mobility Traces Montjoye et al., “Unique in the Crowd: Privacy Bounds of Human Mobility” Nature, 2013 • 93% predictable (theoretical limit) (Song et al., 2010) • highly unique: with only 4 random spatio- temporal points (1-hour time res.), 95% of traces can be identified (Montjoye et al., 2013) At cell-level granularity, mobility traces are ...

3. Eagle and Pentland, “Eigenbehaviors: identifying structure in routine,” Behav Ecol Sociobiol, 2009 Behavior from Cell-Resolution Mobility Traces Eigenbehavior analysis allows discovery of behaviors that are correlated across large gaps in time (e.g., I came in early so I can leave early and go to a show tonight).

4. Google Now-Resolution Mobility Traces What is the street value of this trace?

5. Google Now-Resolution Mobility Traces What is the street value of this trace?

9. What is the street value of this trace?

10. Zang and Bolot, “Anonymization of location data does not work” MobiCom, 2011 The value of mobility traces goes as their information content From 25 million cell users across U.S. Sprint network ...

11. Cell-level: What are your daily patterns? City-level: Where do you live? Coarse GPS/WiFi-level: What is your approximate context? Decimeter-level: What is your immediate present reality?

12. Why we may want our vehicles to know their position at sub-decimeter accuracy: • safety and security for connected veh. • more efficient traffic flow • personal assistant; forecasting • proof of location

13. A few cm-precise mobility traces ...

14.

15.

16.

17.

18. Cell-level: What are your daily patterns? City-level: Where do you live? Coarse GPS/WiFi-level: What is your approximate context? Decimeter-level: What is your immediate present reality?

Vehicle Tracking with Fine-Grained GPS and Implications for Safety

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