Adaptive Cruise Control: Towards higher traffic flows, at the cost of increased susceptibility to congestion

Self-organizing traffic jams are known to occur in medium-to-high density traffic flows and it is suspected that ACC algorithms may affect their onset in mixed human-ACC traffic flows. Unfortunately, closed-form solutions that predict the statistical occurrence of these jams in mixed traffic do not exist. In this paper, a closed form solution that explains the impact of adaptive cruise control (ACC) on congestion due to the formation of self-organizing traffic jams (or “phantom” jams) is obtained. The master equation approach is selected for developing a model that describes the self-organizing behavior of traffic flow at a mesoscopic scale. The master equation approach is further developed to incorporate driver (or agent) behavior using ACC or car-following algorithms. The behavior for both human-driven and ACC vehicles is modeled using the General Motors‟ fourth model. It is found that while introduction of ACC vehicles into traffic may enable higher traffic flows, it also results in disproportionately higher susceptibility of the traffic flow to congestion.