Cellular automaton model for maneuvering a vehicle in a stream

Stepan A. Bazrov, Anton S. Aleshkin


The management of urban transport networks is an important task that affects many life aspects for a modern city. To optimize the traffic flows of the city, to identify critical places and to develop recommendations for the management of the transport network, it is necessary to collect data on road traffic, and, after forming a hypothesis about a possible improvement in the situation, it is necessary to carry out traffic simulations. One of approaches to such simulations may be an approach with used the rules of a cellular automaton to simulate the motion of an individual car. This paper considers the construction of a cellular automaton from its elementary linear version (called "Rule 184", or CA-184), to an extended version that considers the maneuvering of several road users.

The article describes the procedure for forming rules for the cellular automaton of modeling the movement of the car, considering the possible maneuvering in the flow and coordination of the movement of several vehicles (due to the possibilities of cooperative line changing). As basic rules, mechanisms for controlling safe speed, forming a preferred rearrangement and considering possible states, where several participants mutually block each other's movement. The developed rules make it possible to design a cellular automaton for simulating the movement of a car with the ability to maneuver in the flow and coordinate the movement of several vehicles (due to the possibilities of cooperative line changing).

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P. A. Lopez et al., "Microscopic Traffic Simulation using SUMO," 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 2018, pp. 2575-2582, doi: 10.1109/ITSC.2018.8569938.

Lesko, Sergey & Alyoshkin, A. & Zhukov, Dmitry. (2020). Reliability Analysis of the Air Transportation Network when Blocking Nodes and/or Connections Based on the Methods of Percolation Theory. IOP Conference Series: Materials Science and Engineering. 714. 012016. 10.1088/1757-899X/714/1/012016.

Kretov А. U. Classification of cellular automata models. Main rules (In Russian) // News of TulGU. Technical sciences. 2012. Vol 2. URL: https://cyberleninka.ru/article/n/klassifikatsiya-modeley-kletochnyh-avtomatov-osnovnye-pravila (Accessed: 21.02.2023).

Maerivoet, Sven & De Moor, Bart. (2004). Non-concave fundamental diagrams and phase transitions in a stochastic traffic cellular automaton. European Physical Journal B. 42. 10.1140/epjb/e2004-00365-8.

Maerivoet, Sven & De Moor, Bart. (2005). Cellular Automata Models of Road Traffic. Physics Reports. 419. 10.1016/j.physrep.2005.08.005.

Maerivoet, Sven & De Moor, Bart. (2005). Traffic Flow Theory. Physics. 1.

Shinkarev A.A. Traffic flow model based on cellular Automation with adaptive deceleration // Bulletin of SUSU. Series: Computer technologies, control, radio electronics. 2016. Vol 1. URL: https://cyberleninka.ru/article/n/traffic-flow-model-based-on-cellular-automation-with-adaptive-deceleration (Accessed: 21.02.2023).


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