  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.
