Learning And Inferring Transportation Routines

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mstjsw

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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

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]

6. Application: Opportunity Knocks ,[object Object],[object Object]

7. Conclusions ,[object Object],[object Object]

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