Abstract
For the accurate prediction of a complex system, determining how to model well it is essential. A classical simulation modeling method that abstracts causality between inputs and outputs utilizing knowledge such as physical laws or operating rules is widely used. However, it may cause a problem in reliability of the model’s validity if data acquisition of the actual system is difficult. Machine learning, on the other hand, is a method to represent a correlation between one set of data and another. The model can be built using the big data of the target system. It has a limitation in that it is impossible to predict accurately using the learned model if the parameters or the operating rules are changed after the model is learned. In this paper, we propose a collaborative modeling method using big data-based machine learning and simulation modeling. Specifically, a hypothetical model can be constructed through a cellular automata model (simulation modeling), and parameters and functions necessary for a hypothetical model can be simulated by learning and applying an artificial neural network model (machine learning). This paper shows that the proposed method can be applied to the traffic model to predict traffic congestion in an unsteady state.
Highlights
There are various methods to model a system for analysis and prediction
For accurate modeling of complex systems, this paper identifies the limitations of simulation modeling and machine learning and proposes a simulation modeling method in which machine learning is embedded by combining two methods
The hypothetical model of the system is constructed through the cellular automata, and the transition function required for the cellular automata is learned through the artificial neural network (ANN) model, which is a method of machine learning with big data obtained from the observation/operation of the actual system
Summary
There are various methods to model a system for analysis and prediction. As the system becomes more complex, it is very important to determine how to abstract and model it well. We can expect more accurate prediction of the state of the cell when the characteristics of the terrain and real-time weather information are reflected in the ideal model stage by stage (Figure 6) At this time, the transition function does not have the same rules for each cell or does not change over time but depends on the location/time change of the cell. When it is learned through big data, the model reliability can be ensured based on the actual data It is possible to apply and reflect much additional information through big data obtained from the actual system
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