PROJECT TITLE :

Dynamic Prediction of Vehicle Cluster Distribution in Mixed Traffic: A Statistical Mechanics-Inspired Method

ABSTRACT:

The advent of intelligent vehicle technologies holds vital potential to change the dynamics of traffic flow. Previous work on the consequences of such technologies on the formation of self-organized traffic jams has led to analytical solutions and numerical simulations at the mesoscopic scale, which may not yield significant info regarding the distribution of car cluster size. Since the absence of large clusters may be offset by the presence of several smaller clusters, the distribution of cluster sizes will be as vital because the presence or absence of clusters. To obtain a prediction of auto cluster distribution, the included work presents a statistical mechanics-galvanized technique of simulating traffic flow at a microscopic scale via the generalized Ising model. The results of the microscopic simulations indicate that traffic systems dominated by adaptive cruise control ( acc)-enabled vehicles exhibit a better probability of formation of moderately sized clusters, as compared with the traffic systems dominated by human-driven vehicles; however, the trend is reversed for the formation of huge-sized clusters. These qualitative results hold significance for algorithm design and traffic control because it is easier to predict and take countermeasures for fewer massive localized clusters as opposed to several smaller clusters unfold across totally different locations on a highway.