Fuzzy cellular model of signal controlled traffic stream
For traffic control engineers, the model enables real-time simulation by reducing computational overhead, though it is an incremental improvement over existing cellular automata approaches.
The paper introduces a fuzzy cellular traffic model that combines cellular automata and fuzzy calculus to eliminate the need for multiple Monte Carlo simulations and calibration issues. The model achieves computational cost reduction while maintaining consistency with stochastic cellular automata, making it suitable for real-time traffic control applications.
Microscopic traffic models have recently gained considerable importance as a mean of optimising traffic control strategies. Computationally efficient and sufficiently accurate microscopic traffic models have been developed based on the cellular automata theory. However, the real-time application of the available cellular automata models in traffic control systems is a difficult task due to their discrete and stochastic nature. This paper introduces a novel method of traffic streams modelling, which combines cellular automata and fuzzy calculus. The introduced fuzzy cellular traffic model eliminates main drawbacks of the cellular automata approach i.e. necessity of multiple Monte Carlo simulations and calibration issues. Experimental results show that the evolution of a simulated traffic stream in the proposed fuzzy cellular model is consistent with that observed for stochastic cellular automata. The comparison of both methods confirms that the computational cost of traffic simulation is considerably lower for the proposed model. The model is suitable for real-time applications in traffic control systems.