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People are the drivers of processes in cities
We need to understand mobility patterns:
Crime – how many possible victims?
Pollution – who is being exposed? Where are the hotspots?
Economy – can we attract more people to our city centres?
Disease - which times / places have large numbers of interactions
Traditional data may quickly become out of date ...
Systems are driven by individuals
(cars, people, ants, trees, whatever)
Rather than controlling from the top, try to represent the individuals
Autonomous, interacting agents
Represent individuals or groups
Situated in a virtual environment
Lots of uncertainties
Inputs (measurement noise)
... and that's assuming that the system hasn't changed fundamentally
Used in meteorology and hydrology to bring models closer to reality.
Try to improve estimates of the true system state by combining:
Noisy, real-world observations
Model estimates of the system state
Move information from data-rich to data-poor areas
Quantify the uncertainties in the model
I.e. model structure v.s. parameter / variable values
Simulating a city in real-time is too hard!! (for now)
For now lets start a crowd
What methods can we use to incorporate data?
How much data do we need?
Track every individual?
Track some individuals?
Just aggregate counts (e.g. number of people passing a footfall camera)
B. Zhou, X. Wang and X. Tang. (2012) Understanding Collective Crowd Behaviors: Learning a Mixture Model of Dynamic Pedestrian-Agents. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012
No data assimilation. Entrance gate is known; speed and exit gate are unknown
With data assimilation - error increases!
Ensemble Kalman Filter
Ward, Jonathan A., Andrew J. Evans, and Nicolas S. Malleson. (2016) Dynamic Calibration of Agent-Based Models Using Data Assimilation. Royal Society Open Science 3(4). DOI: 10.1098/rsos.150703.
Unscented Kalman Filter
Clay, Robert, Le-Minh Kieu, Jonathan A. Ward, Alison Heppenstall, and Nick Malleson (2020) Towards Real-Time Crowd Simulation Under Uncertainty Using an Agent-Based Model and an Unscented Kalman Filter’. In Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection 12092:68–79. Lecture Notes in Computer Science. DOI:10.1007/978-3-030-49778-1_6.
Quantum Field Theory - Creation and Annihilation Operators
Tang, Daniel. (2019) Data Assimilation in Agent-Based Models Using Creation and Annihilation Operators. ArXiv:1910.09442 [Cs]. arxiv.org/abs/1910.09442.
Need to be very careful: biased data -> biased models
The digital divide
Advantage with these methods is we don't need to track people
Models work with counts of flows
Feeding data into agent-based models is hard!
Computational and methodological challenges
Particle filters work on simple models, but naive filter breaks down
Towards Digital Twins of Human Systems
Join up simulations at multiple spatial and temporal resolutions
Real-time analysis tools and virtual labs for policy development
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