  The integration of automotive technology with Internet-connectivity promises
to both dramatically improve transportation, while simultaneously introducing
the potential for new unknown risks. Internet-connected vehicles are like
digital data because they can be targeted for malicious hacking. Unlike digital
data, however, Internet-connected vehicles are cyber-physical systems that
physically interact with each other and their environment. As such, the
extension of cybersecurity concerns into the cyber-physical domain introduces
new possibilities for self-organized phenomena in traffic flow. Here, we study
a scenario envisioned by cybersecurity experts leading to a large number of
Internet-connected vehicles being suddenly and simultaneously disabled. We
investigate post-hack traffic using agent-based simulations, and discover the
critical relevance of percolation for probabilistically predicting the outcomes
on a multi-lane road in the immediate aftermath of a vehicle-targeted cyber
attack. We develop an analytic percolation-based model to rapidly assess road
conditions given the density of disabled vehicles and apply it to study the
street network of Manhattan (NY, USA) revealing the city's vulnerability to
this particular cyber-physical attack.
