A Blueprint for Urban Analytics
12-13 April, Newcastle University

Dynamic Simulation Models, Data Assimilation & Uncertainty


Nick Malleson*, Wouter Steenbeek and Martin Andresen

*School of Geography, University of Leeds, UK


nickmalleson.co.uk

These slides:

Acknowledgements

Luke Archer & Kevin Minors, Data Science Interns, Leeds Institute for Data Analytics

Minh Kieu, Leeds Institute for Data Analytics

Jon Ward, School of Mathematics

Alison Heppenstall, School of Geography

Christoforos Anagnostopoulos, Jonathan Coello, and others at Improbable.

picture of people in time square
Photo by Meriç Dağlı] on Unsplash

Difficult to model cities

Extremely complex systems

Interactions between individuals are key

Aggregate models are unsuitable

"Computationally convenient".

But cannot capture non-linear, complex systems.

Agent-Based Modelling is better?

Divergence

Nonlinear models predict near future well, but diverge over time.

Solution: Data Assimilation (?)

Try to improve estimates of the true system state by combining:

Noisy, real-world observations

Model estimates of the system state

Ensemble Kalman Filter (EnKF)

Ward, J., A. Evans, N. Malleson (2016) Dynamic calibration of agent-based models using data assimilation. Royal Society Open Science. 3:150703. (open access). [DOI: 10.1098/rsos.150703]

Bus Simulation with a Particle Filter

Crowd Simulation with a Particle Filter

Animation of bus simulation with data assimilation

Crowd Simulation with a Particle Filter

Animation of bus simulation with data assimilation

Dynamic City Simulation: What Next?

Short-term

Crowd simulation with a * Kalman Filter - computationally feasible?

Bus simulation with real GPS traces - applications to real cities

Medium-term

Simulations of larger systems - high streets, villages, towns

Integrate disparate models (e.g. microsimulation, population projections ... )

Long-term

Real-time, dynamic simulation of an urban area - part of a digital twin

Provide the best representation of now

Could transform the way that cities are managed

For more information about what we're doing

ESRC Logo

Data Assimilation for Agent-Based Models (dust)

http://dust.leeds.ac.uk/

Main aim: create new methods for dynamically assimilating data into agent-based models.

Uncertainty in agent-based models for smart city forecasts

Turing Logo

turing.ac.uk

Developing methods that can be used to better understand uncertainty in individual-level models of cities

Bringing the Social City to the Smart City

https://alisonheppenstall.co.uk/research/bringing-the-social-city-to-the-smart-city/

For more information about what we're doing

AAMAS presentation:

Understanding Input Data Requirements and Quantifying Uncertainty for Successfully Modelling ‘Smart’ Cities. Presentation to the 3rd International Workshop on Agent-Based Modelling of Urban Systems (ABMUS), part of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018). 10-15 July, Stockholm. Full abstract (pdf).

Now Recruiting:
Simulating Urban Systems

Post-doctoral Research Fellow (grade 7)

Full time, 3 years

Application deadline: April 2019

https://jobs.leeds.ac.uk/Vacancy.aspx?ref=ENVGE1089

A Blueprint for Urban Analytics
12-13 April, Newcastle University

Dynamic Simulation Models, Data Assimilation & Uncertainty


Nick Malleson

Professor of Spatial Science

School of Geography, University of Leeds, UK


nickmalleson.co.uk

These slides:
https://urban-analytics.github.io/dust/presentations.html