Cellular Automata for Traffic Modeling

Cellular automata represent a computational mathematical model composed of numerous simple cells, where each cell can exist in different states. These cells interact with neighbors and evolve according to predefined rules. This framework finds applications across diverse domains including biology, physics, and computer science. In biological studies, cellular automata can model cell development and differentiation processes through state transition rules. In physics, they simulate complex phenomena like fluid dynamics by implementing neighborhood interaction algorithms. For computer science applications, cellular automata provide foundations for artificial intelligence and machine learning algorithms, where cell states can represent data points and evolution rules mimic learning processes. Key implementation aspects involve defining state transition functions, neighborhood configurations (e.g., Moore or von Neumann neighborhoods), and boundary handling methods for finite grids.