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Learning & Inferring Transportation Routines: A Hierarchical Activity Model

This paper presents a system that creates a probabilistic model of a user's daily movements through the community using unsupervised learning from raw GPS data. The model can infer locations of usual goals like home or work, modes of transportation, predict future movements, infer flawed behavior or broken routines, and robustly track behavior without GPS signal. It utilizes a Dynamic Bayesian network with Rao-Blackwellized particle filtering for inference, handles trip segments and goal switching, and estimates decision-making at different graph vertices. The paper demonstrates the efficacy of this model through experimental results using 60 days of GPS data from one user.

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Learning & Inferring Transportation Routines: A Hierarchical Activity Model

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  1. Learning and Inferring Transportation Routines By: Lin Liao, Dieter Fox and Henry Kautz Best Paper award AAAI’04

  2. AIM of the paper • Describe a system that creates a probabilistic model of a user’s daily movements through the community using unsupervised learningfrom raw GPS data.

  3. What this probabilistic model can do? • Infer locations of usual goal like home or work place. • Infer mode of transportation • Predict future movements (short and long-term) • Infer flawed behavior or broken routine • Robustly track and predict behavior even in the presence of total loss of GPS signal.

  4. Describing the model • Hierarchicalactivity model of a user from a data collected from a wearable GPS. • Represented by a Dynamic Bayesian network • Inference performed by Rao-Blackwellised particle filtering

  5. gk Goal g gk-1 fgk Goal switching fg tk tk-1 Trip segment t ftk Trip switching ft mk-1 mk Transportation mode m Mode switching fm τk τk-1 fmk xk xk-1 x=<l,v,c> Location, velocity and car Θk-1 Θk zk-1 zk GPS reading z

  6. Location and Transportation modes • Xk = <lk,vk,ck> gives location, velocity of the person and location of person’s car • Location lk is estimated on a graph structure representing a street map using the parameter θk. • zk is generated by person carrying GPS data. • mk can be {Bus,Foot,Car,Building} • τ models the decision a person makes when moving over a vertex in the graph, for example, to turn right on a signal.

  7. Trip segments • tk is defined by: • Start location tsk • End location tek and • Mode of transportation tmk • Switching nodes • Handle transfer between modes and trip segments.

  8. Goals • A goal represents the current target location of the person. • E.g. Home, grocery store, locations of friends • Assumption: Goal of a person can only change when the person reaches the end of a trip segment level.

  9. Inference • Inference: estimate current state distribution given all past readings • Particle filtering • Evolve approximation to state distribution using samples (particles) • Supports multi-modal distributions • Supports discrete variables (e.g.: mode) • Rao-Blackwellisation • Particles include distributions over variables, not just single samples • Improved accuracy with fewer particles (hopefully)

  10. Types of Inference • Goal and trip segment estimation • GPS based tracking on street maps • Estimate a person’s location by a graph-structure S = (V,E) • Aim: Find the posterior probability by Rao-Blackwellised particle filtering. Prior by Kalman-filtering

  11. Learning • Structural learning • Searches for significant locations, e.g. user goals and mode transfer locations • Parameter learning • Estimate transition probabilities • Transitions between blocks • Transitions between modes

  12. Structural learning • Finding goals • Locations where a person spends extended period of time • Finding mode transfer locations • Estimate mode transition probabilities for each street • E.g. bus stops and parking lots are those locations where the mode transition probabilities exceed a certain threshold

  13. Detection of abnormal behavior • If person always repeats usual activities, activity tracking can be done with a small number of particles. • In reality, people often do novel activities or commit some errors • Solution: Use two trackers simultaneously and compute Bayes factors between the two models.

  14. Experimental results • 60 days of GPS data from one person using wearable GPS. • First 30 days for learning and the rest for empirical comparison

  15. Activity model learning

  16. Infering Trip Segments

  17. Empirical comparison to flat model

  18. Comparison to 2MM model

  19. Detection of user errors

  20. Detection of user errors

  21. Summary • Paper introduces Hierarchical markov model that can learn and infer user’s daily movements. • Model uses multiple levels of abstractions: lowest level GPS, highest level transportation modes and goals. • Rao-Blackwellised particle filtering used for inference • Learning significant locations was done in an unsupervised manner using the EM algorithm. • Novelty detection or abnormal behavior by model detection.

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