Heterogeneous traffic flow model under connected vehicles environment considering cyberattacks

Under the Vehicle-to-Vehicle (V2V) environment, connected vehicles (CVs) can share the traveling information with each other to keep the traffic flow stable. However, the open network cooperation environment makes CVs vulnerable to cyberattacks, which leads to changes in driving behavior. The existing theories divide cyberattacks into three types: bogus information, replay/delay and collusion cyberattacks. In addition, the mixed flow consisting of truck and car is a common form of road traffic. In order to clarify the potential impact of cyberattacks on mixed traffic flow, this paper proposes an extended car-following model considering cyberattacks under CVs environment. Subsequently, the stability of the model is analyzed theoretically, and the stability condition of the model is obtained. The numerical simulation is carried out and the result shows that the cyberattacks lead to different degrees of traffic behavior hazards such as queue time extension, congestion and even rear end collision. Among them, cooperative attack is the most serious.

Automated summary

This paper proposes an extended car-following model considering cyberattacks under CVs environment and analyzes the potential impact of cyberattacks on mixed traffic flow. The results show that cyberattacks can lead to increased traffic behavior risks such as queue time extension, congestion, and rear end collision, with cooperative attacks being the most serious.