Impact of intelligent agents on the avoidance of spontaneous traffic jams on two-lane motorways

Jessica, Malerczyk; Sabine, Lerch; Bernd, Tibken; Anton, Kummert · 2020 · DOAJ

DOI: 10.1051/matecconf/202030805003

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Summary

This paper investigates the potential of intelligent agents, specifically autonomous vehicles, to mitigate spontaneous traffic jams, often referred to as phantom jams, on two-lane motorways. Motivated by the need to reduce carbon dioxide emissions associated with traffic congestion, the study addresses how human driving behaviors, such as overreaction and sudden overtaking, contribute to these jams. The authors aim to determine the threshold of autonomous vehicle penetration required to significantly improve traffic flow and avoid congestion. The research utilizes an extended Nagel-Schreckenberg (NaSch) cellular automaton model, incorporating the velocity-dependent randomization (VDR) model to simulate realistic traffic phenomena like hysteresis. The simulation environment consists of a two-lane motorway with asymmetric lane-changing rules reflecting the obligation to drive on the right. Autonomous vehicles are modeled as intelligent agents equipped with sensors to detect surrounding vehicles' positions and velocities. These agents operate by avoiding lane changes that would force following vehicles to decelerate and by adapting their speed to the vehicle ahead. The simulations were conducted in MATLAB using a closed system with a global density of 12%, varying the proportion of intelligent vehicles from 0% to 100%. The results demonstrate that when 100% of vehicles are autonomous, spontaneous traffic jams are completely eliminated. The fundamental diagrams for this scenario show only free traffic phases, whereas the reference simulation with human drivers exhibits synchronized traffic and wide moving jams. Average velocities increase significantly in the fully autonomous scenario. However, introducing only 5% of intelligent vehicles yields no positive impact; in fact, the number of congestion events increases by 19.8% compared to the reference, as human behavior predominates. As the proportion of intelligent vehicles increases, traffic conditions improve gradually. A significant positive impact is observed at a 30% penetration rate, where the number of congestion situations decreases substantially, the length of traffic jams is reduced by more than half, and average speeds increase. While the maximum width of remaining jams increases slightly due to convoys formed by human drivers, these jams dissolve more quickly because autonomous vehicles can accelerate immediately. The study concludes that while full autonomy eliminates phantom jams entirely, a penetration rate of approximately 30% is sufficient to significantly harmonize traffic flow and reduce congestion. This finding suggests that partial adoption of intelligent vehicles can yield substantial benefits for traffic efficiency and emission reduction, even before complete automation is legally or technically feasible. The work highlights the importance of swarm intelligence in stabilizing traffic flow and provides evidence-based insights for future traffic management and autonomous vehicle deployment strategies.

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• traffic density