Jane Hillston - Moment Analysis, Model Reduction and London Bike Sharing

Explore moment analysis, model reduction techniques, and their application to London's bike-sharing system in this insightful conference talk. Delve into the challenges of reducing complex models across various scientific disciplines to a manageable number of variables for practical computation and accurate prediction. Discover how powerful statistical approaches based on large-scale data analysis are revolutionizing the modeling paradigm. Learn about the emerging combination of statistical inference, high-throughput computation, and physical laws in model development. Examine the mathematical foundations for integrating these methods, with a focus on collective dynamics, molecular modeling, cell biology, and fluid dynamics. Gain valuable insights into bike-sharing systems, user journey planning, Markov queueing models, time-inhomogeneous PCTMCs, moment approximation techniques, and the application of these concepts to London's bike-sharing network. Understand the process of deriving significant station sets, specifying initial states for reduced PCTMCs, and evaluating experimental results.

Intro
Bike-sharing Systems (BSS)
User Journey Planning
Markov Queueing Model cont.
Time-inhomogeneous PCTMC
Moment approximation for PCTMCS
Moment Equations for PCTMCS
Model BSS as PCTMC
The Naive PCTMC model for BSS
Directed Contribution Graph
Indirect contribution coefficient
Derive significant stations set
Specify the initial state of the reduced PCTMC
Experiments
Evaluation
Conclusion and future work