ANTi-JAM solutions for smart roads: Ant-inspired traffic flow rules under CAVs environment

Abstract

The behaviour of ants has inspired various scientific disciplines due to their ability to solve even complex problems. During their movement, ants generate trail networks that share many characteristics with vehicular traffic on highways. This research aims to estimate the values of traffic flow variables (mean speed, density, and flow) in ant trails without intersections or branches that could alter the dynamics of each ant. A case study in an outdoor environment was analyzed. The macroscopic traffic flow variables of interest were estimated using the deep learning method and the YOLO detection algorithm. The results show that ants adopt specific traffic strategies (platoon formation, quasi-constant speed and no overtaking maneuvers) that help avoid jam phenomena, even at high density. Emerging technologies, including smart roads, communication systems, and Cooperative and Automated Vehicles (CAVs), allow us to speculate on the use of traffic control systems inspired by ant behaviour to avoid the risk of congestion even at high traffic volumes, as demonstrated by the preliminary results of this research.