Modeling traffic flow mixed with automated vehicles considering drivers ’ character difference

Abstract

Modeling impact of drivers’ character difference on the traffic flow mixed with automated vehicles would be a challenge. It needs to understand the character property of drivers who steer ACC or CACC vehicles, and figure out the different behaviors due to character difference during the formation and disengagement of CACC strings when the manually driven vehicles are mixed. Also it requires depicting the different behaviors of manually driven vehicles due to character difference under the influence of CACC cooperative strategy. To deal with these problems, we propose a four-lane cellular automaton traffic model to simulate the interaction between automated vehicles and manually driven vehicles, in which the drivers who control automated vehicles are classified into adaptive and inadaptive drivers, and the drivers of manual vehicles are classified into aggressive and cautious drivers. Numerical results show that increasing of CACC penetration could effectively alleviate the traffic congestion and improve the traffic capacity. And the cooperative driving strategy of CACC would work better and the traffic capacity enhances with the increasing of the adaptive drivers. On the other hand, with the increasing of the aggressive drivers, the vehicle clusters and stop-and-go waves occurred more frequently, thus the traffic capacity decreases. It indicates that the mixed traffic flow has different properties in terms of capacity and traffic congestion when different components of drivers’ character are involved.