Alan Turing Institute: AI UK Smart Cities

Simulating the City with AI
Agent-Based Modelling


Alison Heppenstall and Nick Malleson

University of Leeds and The Alan Turing Institute, UK

n.s.malleson@leeds.ac.uk


Slides available at:
https://urban-analytics.github.io/dust/presentations.html

Quantifying Real-Time Urban Dynamics

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Traditional data may quickly become out of date ...

Google Mobility Data: people spending much more time at home
Diagram of the sims

Agent-Based Modelling

Systems are driven by individuals

(cars, people, ants, trees, whatever)

Individual-level modelling

Rather than controlling from the top, try to represent the individuals

Autonomous, interacting agents

Represent individuals or groups

Situated in a virtual environment

Problem: Models will Diverge

Lots of uncertainties

Inputs (measurement noise)

Parameter values

Model structure

... and that's assuming that the system hasn't changed fundamentally

Possible Solution: Data Assimilation

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

Other advantages

Move information from data-rich to data-poor areas

Quantify the uncertainties in the model

I.e. model structure v.s. parameter / variable values

Diagram of data assimilation and an ABM

Real Time City Crowd Modelling

Image of escalators in a train station

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)

Grand Central Terminal (New York)

Pedestrian traces

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

http://www.ee.cuhk.edu.hk/~xgwang/grandcentral.html

Grand Central Terminal floor plan with entrances around the sides and an obstruction in the centre. Pedestrian trajectories estimated from the video

Data assimilation with a Particle Filter

Flowchart of the experimental design. Lots of models ('particles') are run simultaneously.

Crowd Simulation with a Particle Filter

Animation of a crowding model with data assimilation

Crowd Simulation with a Particle Filter

Animation of a crowding model with data assimilation

Preliminary Particle Filter Results

Box Environment: More particles = lower error

median absolute error change with number of agents and particles: greater
                              complexity caused by larger numbers of agents can be mitigated by increasing
                              the numbers of particles.
Malleson, Nick, Kevin Minors, Le-Minh Kieu, Jonathan A. Ward, Andrew West, and Alison Heppenstall. (2020) Simulating Crowds in Real Time with Agent-Based Modelling and a Particle Filter. Journal of Artificial Societies and Social Simulation 23(3) DOI: 10.18564/jasss.4266.

Difficulties (I)

Exponential increase in complexity

Number of collisions increases exponentially with number of agents
N. Malleson, K. Minors, Le-Minh Kieu, J. A. Ward, A. West and Alison Heppenstall (2020). Simulating Crowds in Real Time with Agent-Based Modelling and a Particle Filter. Journal of Artificial Societies and Social Simulation (JASSS) 23(3), 3.

Preliminary Particle Filter Results

Grand Central Station: Filtering makes it worse!

No data assimilation. Entrance gate is known; speed and exit gate are unknown

Distance between agent and real trajectory. (a) Model time; (b) Pedestrian time.

Ternes, P., J. Ward, A. Heppenstall, V. Kumar, Le-Minh Kieu, N. Malleson (2020) Using data assimilation to reduce uncertainty in an agent-based pedestrian simulations in real time. Under review.

Preliminary Particle Filter Results

Grand Central Station: Filtering makes it worse!

With data assimilation - error increases!

Distance between agent and real trajectory. (a) Model time; (b) Pedestrian time.

Ternes, P., J. Ward, A. Heppenstall, V. Kumar, Le-Minh Kieu, N. Malleson (2020) Using data assimilation to reduce uncertainty in an agent-based pedestrian simulations in real time. Under review.

Difficulties (II)

Nonlinear trajectories

Shows that some pedestrian trajectories are linear, others very curved
Ternes, P., J. Ward, A. Heppenstall, V. Kumar, Le-Minh Kieu, N. Malleson (2020) Using data assimilation to reduce uncertainty in an agent-based pedestrian simulations in real time. Under review.

Other Methods (ongoing)

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.

Ethical Implications

Data Bias

Need to be very careful: biased data -> biased models

The digital divide

Tracking People

Advantage with these methods is we don't need to track people

Models work with counts of flows

Nature article: Make more digital twins

Conclusions

Feeding data into agent-based models is hard!

Computational and methodological challenges

Particle filters work on simple models, but naive filter breaks down

Future Work

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

Alan Turing Institute: AI UK Smart Cities

Simulating the City with AI
Agent-Based Modelling


Alison Heppenstall and Nick Malleson

University of Leeds and The Alan Turing Institute, UK

n.s.malleson@leeds.ac.uk


Slides available at:
https://urban-analytics.github.io/dust/presentations.html