Modeling of Transport Flows in Critical Situations Using Cellular Automata in MATLAB

Modeling of Transport Flows in Critical Situations Using Cellular Automata in MATLAB

Paper in proceedings (conference paper)

Cellular automata can be used to model traffic flow by applying a simple algorithm that regulates the acceleration and deceleration of vehicles. Despite its simplicity, this model allows for the observation of large-scale phenomena, such as traffic jams that propagate backward. These backward-propagating congestion patterns emerge naturally from the interactions between vehicles without the need for centralized control. A variation of this model, implemented in MATLAB, was used to study how different parameters, such as vehicle density on the road or the number of lanes, affect traffic flow intensity. The simulation approach leverages a one-dimensional array of discrete cells, where each cell represents a possible vehicle position and its velocity. Experiments revealed that in a system with a single lane and a maximum speed of 1, a phase transition occurs at a density of 0.08 cars per cell. This phase transition marks a critical shift in traffic dynamics: below the threshold, traffic flows freely, while above it, jams persist and propagate. Moreover, this transition was observed only in cases where the maximum speed exceeded one, confirming the non-linear nature of traffic flow dynamics even under simple rule sets.

Cellular automata can be used to model traffic flow by applying a simple algorithm that regulates the acceleration and deceleration of vehicles. Despite its simplicity, this model allows for the observation of large-scale phenomena, such as traffic jams that propagate backward. These backward-propagating congestion patterns emerge naturally from the interactions between vehicles without the need for centralized control. A variation of this model, implemented in MATLAB, was used to study how different parameters, such as vehicle density on the road or the number of lanes, affect traffic flow intensity. The simulation approach leverages a one-dimensional array of discrete cells, where each cell represents a possible vehicle position and its velocity. Experiments revealed that in a system with a single lane and a maximum speed of 1, a phase transition occurs at a density of 0.08 cars per cell. This phase transition marks a critical shift in traffic dynamics: below the threshold, traffic flows freely, while above it, jams persist and propagate. Moreover, this transition was observed only in cases where the maximum speed exceeded one, confirming the non-linear nature of traffic flow dynamics even under simple rule sets.

Cellular automata, Nagel–Schreckenberg (NS) model, numerical simulation, MATLAB, animated visualization

Cellular automata, Nagel–Schreckenberg (NS) model, numerical simulation, MATLAB, animated visualization

Veronika Novotná, Bedřich Půža, Andriy Shatyrko, Volodymyr Kovtun

01.09.2025

ceur-ws

Kyiv

978-960-801-961-4

Proceedings of the International Workshop on Computational Intelligence (IWSCI 2025)

12

https://ceur-ws.org/Vol-4035/Paper3.pdf