Agent-based models (ABM) 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 optimised. 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 optimised by a combination of parameter calibration and DA. The proposed model and framework can also be used in an passenger information system, or in an Intelligent Transport Systems to provide forecasts of bus locations and arrival times.
@cite_0: Abstract Agent-based simulations are useful for studying people’s movement and to help making decisions in situations like emergency evacuation in smart environments. These agent-based simulations are typically used as offline tools and do not assimilate real time data from the environment. With more and more smart buildings equipped with sensor devices, it is possible to utilize real time sensor data to dynamically inform the simulations to improve simulation results. In this paper, we propose a method to assimilate real time sensor data in agent-based simulation of smart environments based on Particle Filters (PFs). The data assimilation aims to estimate the system state, i.e., people’s location information in real time, and use the estimated states to provide initial conditions for more accurate simulation prediction of the system dynamics in the future. We develop a PF-based data assimilation framework and propose a new resampling method named as component set resampling to improve data assimilation for multiple agents. The proposed framework and method are demonstrated and evaluated through experiments using a sparsely populated smart environment.
This paper follows an identical twin' experiment framework @cite_0 , where experiment data to be used will be generated from simulation, instead of using real data. The reason is that real data often comes with noise that hides the true state of the bus route (e.g. noise from GPS data). A simulated synthetic data would enable us to control the level of noise in the data, and to evaluate the modelling results against the ground truth rather than noisy data. Figure shows the workflow of this study.