A multi-lane traffic simulation model via continuous cellular automata

Traffic models based on cellular automata have high computational efficiency because of their simplicity in describing unrealistic vehicular behavior and the versatility of cellular automata to be implemented on parallel processing. On the other hand, the other microscopic traffic models such as car-following models are computationally more expensive, but they have more realistic driver behaviors and detailed vehicle characteristics. We propose a new class between these two categories, defining a traffic model based on continuous cellular automata where we combine the efficiency of cellular automata models with the accuracy of the other microscopic models. More precisely, we introduce a stochastic cellular automata traffic model in which the space is not coarse-grain but continuous. The continuity also allows us to embed a multi-agent fuzzy system proposed to handle uncertainties in decision making on road traffic. Therefore, we simulate different driver behaviors and study the effect of various compositions of vehicles within the traffic stream from the macroscopic point of view. The experimental results show that our model is able to reproduce the typical traffic flow phenomena showing a variety of effects due to the heterogeneity of traffic