Developing traffic models is crucial for accurately depicting observed and realistic traffic behavior and phenomena, which can be used for traffic system simulation and management. One advanced approach in traffic system modeling is the use of cellular automaton (CA), a dynamic system where discrete spatial cells take on discrete values and evolve according to a discrete-time update rule. The synchronized computing process makes it suitable for parallel computation. This study aims to demonstrate the utilization of CA models in various traffic flow scenarios, such as single-lane, multi-lane, network, and pedestrian dynamics. Based on simulation results, we summarize the diverse evolution properties of these CA models.