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London Underground
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Bayesian Modeling
100%
Modeling Framework
100%
Route Choice
100%
Journey Time
75%
Smart Card
50%
Transit Route
50%
Observational Data
25%
Time-varying Data
25%
Mixture Distribution
25%
Railway Network
25%
Bayesian Inference
25%
Individual Travel
25%
Corroborative Evidence
25%
Posterior Probability
25%
Passenger Traffic
25%
Mixture Model
25%
Gaussian Mixture Model
25%
Oyster
25%
Public Transport Drivers
25%
Travel Data
25%
User Journey
25%
Travel Demand
25%
Underground Network
25%
Lognormal Mixture Model
25%
Central Zone
25%
Smart Card Data
25%
Potential Practical Application
25%
Automatic Fare Collection System
25%
Engineering
Railway
100%
Good Performance
100%
Limitations
100%
Supporting Evidence
100%
Gaussians
100%
Lognormal
100%
Basestation
100%
Posterior Probability
100%
Mixture Distribution
100%
Computer Science
Case Study
100%
Modeling Framework
100%
Bayesian Modeling
100%
Smartcards
100%
Good Performance
50%
Posterior Probability
50%
Observational Data
50%
Smart Card
50%
Supporting Evidence
50%
Collection System
50%
Mathematics
Bayesian
100%
Route Choice
100%
Mixture Model
50%
Probability Theory
25%
Gaussian Distribution
25%
Mixture Distribution
25%
Bayesian Inference
25%
Observational Data
25%
Base Station
25%
Psychology
Case Study
100%
Mixture Model
100%
Gaussian Distribution
50%
Economics, Econometrics and Finance
Bayesian
100%
Wealth
50%
Transport Demand
50%