A Two Lanes Cellular Automata Model for Traffic Flow Considering Realistic Driving Decisions

This paper extends a recently proposed single-lane cellular automata (CA) model to simulate asymmetric two-lane traffic flow. The model incorporates drivers' individual characteristics and acceleration constraints of vehicles in the definition of lane changing decision process. The aim is to make the lane change process more in line with real features. Thus, the drivers' representation in the model is more like human response by reconstructing the local behavior on a microscopic level. Simulation results on a system with periodic conditions show that lane usage inversion, lane change rate versus density and different traffic states are reproduced by the model. Besides, in this model the flow is not dominated by the introduction of larger vehicles with smaller maximum velocity, even if their lane change is not prohibited. Moreover, ping-pong lane changes are reduced. The model preserves the computational simplicity of CA models.