Extremely complex systems
Interactions between individuals are key
Aggregate models are unsuitable
But cannot capture non-linear, complex systems.
Agent-Based Modelling is better?
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
Crowd simulation with a * Kalman Filter - computationally feasible?
Bus simulation with real GPS traces - applications to real cities
Simulations of larger systems - high streets, villages, towns
Integrate disparate models (e.g. microsimulation, population projections ... )
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
Data Assimilation for Agent-Based Models (dust)
Main aim: create new methods for dynamically assimilating data into agent-based models.
Uncertainty in agent-based models for smart city forecasts
Developing methods that can be used to better understand uncertainty in individual-level models of cities
Bringing the Social City to the Smart City
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).
Post-doctoral Research Fellow (grade 7)
Full time, 3 years
Application deadline: April 2019