Fuzzy cellular model of signal controlled traffic stream

TL;DR

This paper presents a fuzzy cellular automata-based traffic model that improves real-time traffic simulation efficiency and accuracy, addressing calibration and stochastic challenges of traditional cellular automata models.

Contribution

A novel fuzzy cellular automata traffic model that reduces computational costs and calibration issues, enabling real-time traffic control applications.

Findings

Simulation results match stochastic cellular automata behavior.

Computational cost is significantly lower with the fuzzy model.

Model is suitable for real-time traffic management.

Abstract

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.