Traffic Flow Cellular Automaton Model

This is a cellular automaton model for traffic flow simulation, capable of replicating real-world traffic flow conditions with high accuracy. The model implements fundamental CA principles through discrete grid-based cells where each cell represents a road segment with predefined states (empty or occupied by vehicles). Key algorithmic components include: - State transition rules governing vehicle acceleration/deceleration based on gap distances - Stochastic randomization parameters to simulate realistic driver behaviors - Periodic boundary conditions for continuous traffic flow simulation - Density-flow relationship calculations for fundamental diagram generation By studying this type of traffic flow model, researchers can gain deeper insights into traffic flow mechanisms and patterns. The model serves as a valuable tool for transportation planning decisions and evaluations, enabling the study of congestion phenomena, analysis of various traffic management strategies, and identification of methods to enhance road capacity. Furthermore, it provides a computational framework for developing intelligent transportation systems to optimize urban traffic management and improve commuter experiences through scenarios like: - Variable speed limit implementations - Ramp metering simulations - Traffic light timing optimization algorithms - Emergency vehicle priority routing simulations