Abstract
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.
Highlights
Agent-based modelling (ABM) [1] is a field that excels in its ability to simulate complex systems
This paper proposes parameter calibration and data assimilation (DA) frameworks to enhance the prediction accuracy in ABMs when the system under study has a stochastic and dynamic nature
The methods will be applied to real data from real systems, but currently hypothetical ‘pseudo-truth’ data are generated to test the algorithms in an experimental environment as per the ‘identical twin’ approach
Summary
Agent-based modelling (ABM) [1] is a field that excels in its ability to simulate complex systems. ABM has emerged as an important tool for many applications ranging from urban traffic simulation [2], humanitarian assistance [3] to emergency evacuations [4]. Despite the many advances and applications of ABM, the field suffers from a serious drawback: models are currently unable to incorporate up-to-date data to make accurate real-time predictions [5,6,7]. Models are typically calibrated once, using historical data, projected forward in time to make a prediction. Calibration is ideal for one point in time, but as the simulation royalsocietypublishing.org/journal/rsos R.
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