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Learning and Learning and Inferring Inferring
Transportation Transportation RoutinesRoutines
By: By: Lin Liao, Dieter Fox and Henry KautzLin Liao, Dieter Fox and Henry Kautz
Best Paper award AAAI’04Best Paper award AAAI’04
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AIM of the paperAIM of the paper• Describe a system that creates a
probabilistic model of a user’s daily movements through the community using unsupervised learning from raw GPS data.
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What this probabilistic What this probabilistic model can do?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.
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Describing the modelDescribing the model• Hierarchical activity 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
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xk-1
zk-1 zk
xk
mk-1 mk Transportation mode m
x=<l,v,c>Location, velocity and car
GPS reading z
tk-1tk
ftk
gk Goal g
Trip segment t
fgk
gk-1
fmk
τk-1τk
Θk-1Θk
Goal switching fg
Trip switching ft
Mode switching fm
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Location and Transportation Location and Transportation modesmodes
• 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.
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Trip segmentsTrip 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.
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GoalsGoals• 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.
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InferenceInference• 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)
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Types of InferenceTypes of Inference1. Goal and trip segment estimation2. 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
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LearningLearning• 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
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Structural learningStructural 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
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Detection of abnormal Detection of abnormal behaviorbehavior
• 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.
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Experimental resultsExperimental results• 60 days of GPS data from one person
using wearable GPS.• First 30 days for learning and the
rest for empirical comparison
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Activity model learningActivity model learning
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Infering Trip SegmentsInfering Trip Segments
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Empirical comparison to flat Empirical comparison to flat modelmodel
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Comparison to 2MM modelComparison to 2MM model
Model Start 25% 50% 75%
2MM 0.69 0.69 0.69 0.69
Hierarchical model 0.66 0.75 0.82 0.98
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Detection of user errorsDetection of user errors
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Detection of user errorsDetection of user errors
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SummarySummary• 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.