Autonomous vehicles (AVs) have the potential to provide new paradigms to enhance the safety, mobility, and environmental sustainability of surface transportation. However, as vehicles become more computerized and internally interconnected by electronic control systems, their vulnerability to cyber-attacks is a fast-growing concern and a national priority. Evidence from the Internet virus suggests that AVs will have critical challenges posed by epidemic-style malware like Stuxnet. This self-propagating malware is a fast and powerful way of disrupting the AV system and transportation infrastructure. This study presents a mathematical model for Stuxnet-style malware’s temporal and spatial spread. Taking cues from the field of epidemiology and ecology, the malware will be described as an infectious epidemic to capture the dynamics of temporal and spatial propagation behavior. This study is the first attempt to analyze the spread of Stuxnet-style malware on AVs. The future uses of such a model for the temporal-geographic spread of AVs-based infectious malware are discussed.

Heavy trucks, with their slow acceleration and reduced speed, can cause traffic delays at intersections, thereby affecting the efficiency of traffic flow. To address this issue, this paper introduces a virtual traffic control system called advanced stop point and prior start time (ASP-PST). The ASP-PST leverages vehicle-to-infrastructure (V2I) communications between traffic signals and connected and automated heavy trucks. The system guides autonomous heavy trucks on where to stop and when to initiate their movement ahead of the green light. This strategy reduces start-up delays by managing vehicle movement control, enabling trucks to reach sufficient speed before approaching the intersection, thereby ensuring smooth passing and harmonized traffic flow. An analytical solution for the ASP-PST has been developed and validated through microscopic simulation tests at signalized intersections. The results show reductions in travel time of up to 50% in mixed traffic conditions, consisting of both heavy trucks and general cars, and illustrate the formation of well-coordinated platoons with uniform spacing. Demonstrating efficacy across various network environments, the ASP-PST offers a potential solution to enhance traffic flow and reduce congestion caused by heavy trucks at intersections.