Researchers at Italy’s University of Trento have studied ant behavior to understand how ants move efficiently while preventing stop-and-go disruptions, even in large groups. The research, published in Transportation Research Interdisciplinary Perspectives, suggests that autonomous vehicles could use similar strategies.
Professors from the University of Trento have studied ant behavior to understand how these insects navigate efficiently without stop-and-go disruptions, even in large groups, with findings recently published in Transportation Research Interdisciplinary Perspectives.
Marco Guerrieri, a professor specializing in road and railway infrastructure, and Nicola Pugno, a professor of solids and structural mechanics, co-authored the study.
Ant behavior tracked with deep learning
Ant behavior has inspired various scientific fields because of their ability to solve complex problems. As they move, ants create trail networks that share similarities with vehicular traffic on highways.
By analyzing a 30-centimeter ant trail—100 times the body length of each ant—and using deep learning algorithms to track movements in video footage, the researchers mapped the ants’ trajectories, speeds, flows, and densities. The results show that ants use strategies like platoon formation, steady speed, and no overtaking to avoid jams, even at high densities.
According to Guerrieri, ants are one of the few species capable of managing bidirectional traffic flows, similar to our roads, yet they navigate seamlessly without congestion. He further notes that ants follow pheromone trails marked by a leader ant, moving in platoons with small gaps and without overtaking.
“From ants walking on a pheromone trail to vehicles driving in a highway lane, the main challenge for all collective systems is to avoid congestion at high densities in crowded environments,” the researchers noted in the study.
The case study further shows that ants solve complex traffic problems using simple, self-organized rules that are not imposed externally, like in traditional traffic. These rules emerge from direct contact or chemical signals between ants, making their behavior more cooperative than that of vehicles on conventional roads, the study says.
Traffic strategies based on ant behavior for autonomous vehicles
The research makes several key contributions. First, it deduces the microscopic traffic variables found in ant streams while it also analyzes the collective strategies ants use to prevent congestion. Based on these findings, the study proposes traffic regulation strategies inspired by ants’ behavior.
Thus, Guerrieri suggests that future traffic systems for autonomous vehicles (CAVs) could be inspired by ant behavior. Just as ants communicate through pheromones, CAVs could use advanced communication technologies to interact with one another and road infrastructure, forming coordinated platoons.
This would allow them to move at high speeds with close spacing across parallel lanes, improving traffic efficiency, service levels, and reducing gas emissions.
However, the study also points out several limitations. One major limitation is that traffic data collection focused on only one ant species, making it impossible to generalize the results to all ant species.
Additionally, the analysis was based on a single trail section without curves, intersections, or conflict areas between ant streams. Lastly, since CAVs are emerging technologies, there is no empirical data from real-world traffic and highway applications to support the findings.
By mazzuki1|2025-01-24T12:00:52-05:00January 24th, 2025|Kool-Sci DAILY|Comments Off on Transportation researchers look to ants for inspiration