NaSch Cellular Automata Implementation in MATLAB

This article explores a simple MATLAB implementation method for the NaSch cellular automata model, designed for beginners entering the field of traffic simulation through cellular automata learning. Using cellular automata algorithms, we can effectively model and analyze traffic flow patterns, which holds significant importance for urban planning and traffic management applications. The MATLAB implementation involves key functions for setting up the cellular grid, defining vehicle movement rules (acceleration, deceleration, randomization), and updating cell states iteratively. The algorithm core implements the four-step NaSch process: acceleration, deceleration based on distance to preceding vehicles, randomization to simulate driver behavior, and position updating. Through MATLAB, we can efficiently code this algorithm and perform visual analysis using plotting functions to display traffic density evolution. This article will demonstrate how to configure and execute the code, including parameter settings for vehicle density and maximum velocity, along with interpreting simulation results through traffic flow diagrams and fundamental diagrams. The implementation utilizes MATLAB's matrix operations for efficient state updates and includes visualization commands like imagesc() for real-time traffic pattern display. This guide will help readers better understand cellular automata principles and their practical application in traffic flow modeling, providing valuable guidance for learning and research in related fields.