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Learning And Inferring Transportation Routines

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Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
1. Introduction ,[object Object],[object Object],[object Object],[object Object]
2. Hierarchical activity model ,[object Object],[object Object],[object Object],[object Object]
two time slices k − 1 and k novelty mode next goal and current trip segment GPS sensor measurement
Locations and transportation modes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Trip segments ,[object Object],[object Object],[object Object]
Goals & Novelty ,[object Object],[object Object],[object Object],[object Object]
3. Inference ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Rao–Blackwellized particle filter for estimation in the flat model ,[object Object],[object Object],[object Object],histories location of the person car location
RBPF algorithm for the flat model Sampling step Kalman filter step Importance weights
RBPF algorithm for the hierarchical model
Detecting novel behavior
4. Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
5. Experimental results ,[object Object],[object Object],[object Object]
Activity model learning ,[object Object],[object Object],[object Object]
Empirical comparison to other models
Error detection
6. Application: Opportunity Knocks ,[object Object],[object Object]
Opportunity Knocks(1)
Opportunity Knocks(2)
Opportunity Knocks(3)
Opportunity Knocks(4)
Opportunity Knocks(5)
Opportunity Knocks(6)
7. Conclusions ,[object Object],[object Object]

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Learning And Inferring Transportation Routines