Congestion-Aware Cooperative Adaptive Cruise Control for Mitigation of Self-Organized Traffic Jams
Taehooie Kim, Kshitij Jerath
Abstract
Previous work has shown that Adaptive Cruise Control (ACC) can improve traffic flow by raising the critical vehicular density at which congestion first appears. However, these works also indicate that traffic with medium-to-high penetration of ACC-equipped vehicles is more susceptible to the formation of self-organized <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">phantom</i> traffic jams induced by perturbations in vehicular demographics. In this work, we propose a congestion-aware Cooperative Adaptive Cruise Control (CACC) algorithm as an alternative to address the trade-off between competing goals of raising the critical density, and reducing susceptibility to congestion observed at higher penetration rates of ACC-equipped vehicles. The congestion-aware CACC algorithm is modeled after the General Motor’s car-following models, wherein the driver sensitivity is altered based on the prevailing congestion state (or traffic jam size) downstream of the connected vehicle. The dynamics of the self-organized traffic jam are modeled using a master equation. Results indicate that the congestion-aware CACC algorithm can increase the effective critical density leading to higher traffic flows, while also reducing the susceptibility to perturbations in vehicle demographics in the density range that adversely affects ACC-equipped vehicles.