Traffic management in congested networks requires a clear understanding of traffic flow operations and its various phenomena. To this end, cellular automata have become a well-established tool for modeling and analysis. This chapter extends a recently introduced reliable cellular automata model to simulate asymmetric two-lane traffic flow. The aim is to faithfully reproduce the lane change process when vehicles with different deceleration capacities are considered, as the model incorporates drivers’ individual characteristics and acceleration constraints on vehicles in the definition of the lane change decision process. Thus, the model reconstructs the behavior of drivers on a microscopic level, in a similar fashion to human responses, and also recovers the observed traffic phenomena at a macroscopic level. Simulation results on a two-lane system with periodic conditions show that the model reproduces most empirical findings of multilane traffic flow. The model preserves CA models’ computational simplicity.