  With the emergence of autonomous vehicles, it is important to understand
their impact on the transportation system. However, conventional traffic
simulations are time-consuming. In this paper, we introduce an analytical
traffic model for unmanaged intersections accounting for microscopic vehicle
interactions. The macroscopic property, i.e., delay at the intersection, is
modeled as an event-driven stochastic dynamic process, whose dynamics encode
the microscopic vehicle behaviors. The distribution of macroscopic properties
can be obtained through either direct analysis or event-driven simulation. They
are more efficient than conventional (time-driven) traffic simulation, and
capture more microscopic details compared to conventional macroscopic flow
models. We illustrate the efficiency of this method by delay analyses under two
different policies at a two-lane intersection. The proposed model allows for 1)
efficient and effective comparison among different policies, 2) policy
optimization, 3) traffic prediction, and 4) system optimization (e.g.,
infrastructure and protocol).
