Learning and Inferring Transportation Routines

1. Learning and Inferring Transportation Routines Lin Liao, Don Patterson, Dieter Fox, Henry Kautz Department of Computer Science and Engineering University of Washington

2. Outline • Motivations and Overview • Global Positioning System (GPS) • Geographic Information System (GIS) • Probabilistic Reasoning Engine • Probabilistic Reasoning Engine • Graphical model • Inference • Learning • Error detection • Conclusion

3. Global Positioning System

4. GPS Receivers GeoStats wearable GPS logger Garmin's iQue 3600 GPS-enabled PDA Samsung N300 GPS-enabled Cell Phone

5. Geographic Information System Street map Data source: Census 2000 Tiger/line data Bus routes and bus stops Data source: Metro GIS

6. Motivations High Level Activities (goals, paths, transportation modes, errors, etc) GPS, GIS

7. Architecture Learning Engine • Goals • Paths • Modes • Errors Probabilistic Model GIS Database Inference Engine

8. Just-in-time Travel Informationcustomized for each individual An ordinary real time traffic condition service Display traffic condition only on the predicted most likely trajectory

9. Activity Compass An intelligent PDA which helps guide a cognitively impaired person safely through the community. The system notes when the user departs from a familiar routine and provides proactive alerts or calls for assistance.

10. Probabilistic Reasoning • Graphical model: Dynamic Bayesian Network • Inference engine: Rao-Blackwellised particle filters • Learning engine: Expectation-Maximization (EM) algorithm

11. Transportation Routines A B Workplace • Goal: • workplace • Trip segments (Including transportation modes) • Home to Bus stop A on Foot • Bus stop A to Bus stop B on Bus • Bus stop B to workplace on Foot • Location and velocity

12. Dynamic Bayesian Network gk-1 gk Goal g tk-1 tk Trip segment t xk-1 xk x=<location, velocity> GPS reading z zk-1 zk Time k Time k-1

13. Rao-Blackwellised Particle Filtering • Inference: estimate the distribution of current state given the measurements up to now • Particle filters: • a Monte Carlo method capable of doing inference in a DBN • Approximate the distribution of the state using a number of weighted samples (particles) • Rao-Blackwellised particle filters • One variant of particle filter that combines the Monte Carlo approximation with exact inference techniques

16. Encoded in the DBN a lot of interesting information Learning the parameters of the DBN equates to learning the information about the user Usual goals Routine parking spots and bus stops Most likely trajectories getting to the goals …. Learning

17. Expectation-Maximization Algorithm • Unsupervised learning: we don’t need to label the data manually! • Capable of learning all the parameters simultaneously • A Monte Carlo version of EM is used for efficiency

18. Learning Goals

19. Learning Goals

20. Learning Trajectories Zoom into the area near the workplace Going to the workplace Going home

21. Error Detection • Track using two different trackers simultaneously • The first tracker assumes normal activity while the second tracker assumes abnormal activity • At each time, estimate the probability for each tracker

23. Thanks for your attention!