We have just published a new open access paper that is about how data assimilation (using a particle filter) can reduce the uncertainty in a simulation of a bus route. Ultimately we hope that the work will allow us to improve bus servies for passengers by, for example, creating better short-term predictions bus arrival times. You can see the slides for the accompanying presentation on the right.

The citation is:

  • Kieu, Le-Minh, N. Malleson, and A. Heppenstall (2019). Dealing with Uncertainty in Agent-Based Models for Short-Term Predictions’. Royal Society Open Science 7(1): 191074. DOI: 10.1098/rsos.191074 (open access)

The abstract is:

  • Agent-based models (ABMs) are gaining traction as one of the most powerful modelling tools within the social sciences. They are particularly suited to simulating complex systems. Despite many methodological advances within ABM, one of the major drawbacks is their inability to incorporate real-time data to make accurate short-term predictions. This paper presents an approach that allows ABMs to be dynamically optimized. Through a combination of parameter calibration and data assimilation (DA), the accuracy of model-based predictions using ABM in real time is increased. We use the exemplar of a bus route system to explore these methods. The bus route ABMs developed in this research are examples of ABMs that can be dynamically optimized by a combination of parameter calibration and DA. The proposed model and framework is a novel and transferable approach that can be used in any passenger information system, or in an intelligent transport systems to provide forecasts of bus locations and arrival times.