The Influence of Different Driving Parameters on Traffic with a Cellular Automata Model

Reliable traffic simulations with high-quality results contribute to the understanding of the traffic system and effective planning. In this article, firstly, an existing cellular automata (CA) model is modified to perform a comprehensive analysis of the influence of key parameters on traffic flow and average velocity. The simulations are conducted with realistic parameter values under heterogeneous conditions. Based on a safety analysis, a novel adaptive acceleration capability is developed, allowing vehicles to accelerate or decelerate at the desired acceleration/deceleration rate depending on the current road conditions. Secondly, the reaction time parameter is introduced into the model to study its impact on traffic under homogeneous and heterogeneous conditions. Thirdly, the model can simulate traffic flow according to different maximum velocities. Additionally, the influence of the maximum acceleration rate on traffic is studied. The results show that the maximum traffic flow at a maximum velocity of 60 km/h and a reaction time of 1 s is 2198 vehicles/h at a density of 0.29 vehicle/cell in adaptive acceleration mode. Generally, the adaptive acceleration capability increases traffic flow by up to 30% at densities of less than 0.7 vehicle/cell and even more at higher densities. Similarly, a 0.2 s lower reaction time increases traffic flow by up to 40%. At a maximum velocity of 110 km/h, the maximum traffic flow is 23% higher than at 50 km/h. However, the maximum velocity has little effect on traffic flow at higher densities. The maximum acceleration rate has a limited impact on traffic flow.

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Publication Details

Journal
Applied Sciences
Published
2025-11-22
DOI
https://doi.org/10.3390/app152312404
Primary Topic
Traffic control and management
Type
article
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article

The Influence of Different Driving Parameters on Traffic with a Cellular Automata Model

Goran Turk, Andrej Rigler
Applied Sciences
Traffic control and management
article

The Influence of Different Driving Parameters on Traffic with a Cellular Automata Model

Goran Turk, Andrej Rigler
article en

Abstract

Reliable traffic simulations with high-quality results contribute to the understanding of the traffic system and effective planning. In this article, firstly, an existing cellular automata (CA) model is modified to perform a comprehensive analysis of the influence of key parameters on traffic flow and average velocity. The simulations are conducted with realistic parameter values under heterogeneous conditions. Based on a safety analysis, a novel adaptive acceleration capability is developed, allowing vehicles to accelerate or decelerate at the desired acceleration/deceleration rate depending on the current road conditions. Secondly, the reaction time parameter is introduced into the model to study its impact on traffic under homogeneous and heterogeneous conditions. Thirdly, the model can simulate traffic flow according to different maximum velocities. Additionally, the influence of the maximum acceleration rate on traffic is studied. The results show that the maximum traffic flow at a maximum velocity of 60 km/h and a reaction time of 1 s is 2198 vehicles/h at a density of 0.29 vehicle/cell in adaptive acceleration mode. Generally, the adaptive acceleration capability increases traffic flow by up to 30% at densities of less than 0.7 vehicle/cell and even more at higher densities. Similarly, a 0.2 s lower reaction time increases traffic flow by up to 40%. At a maximum velocity of 110 km/h, the maximum traffic flow is 23% higher than at 50 km/h. However, the maximum velocity has little effect on traffic flow at higher densities. The maximum acceleration rate has a limited impact on traffic flow.

Applied SciencesVol. 15(23)
University of Ljubljana (SI), Geodetic Institute of Slovenia (SI), Slovenian Centre of Excellence for Space Sciences and Technologies (SI)
Openalex Percentile: Top 55%
Traffic control and management
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The Influence of Different Driving Parameters on Traffic with a Cellular Automata Model — Goran Turk, Andrej Rigler · Applied Sciences (2025) | TGRS Research Map | TGRS