  A comprehensive vehicular network analysis requires modeling the street
system and vehicle locations. Even when Poisson point processes (PPPs) are used
to model the vehicle locations on each street, the analysis is barely
tractable. That holds for even a simple average-based performance metric -- the
success probability, which is a special case of the fine-grained metric, the
meta distribution (MD) of the signal-to-interference ratio (SIR). To address
this issue, we propose the transdimensional approach as an alternative. Here,
the union of 1D PPPs on the streets is simplified to the transdimensional PPP
(TPPP), a superposition of 1D and 2D PPPs. The TPPP includes the 1D PPPs on the
streets passing through the receiving vehicle and models the remaining vehicles
as a 2D PPP ignoring their street geometry. Through the SIR MD analysis, we
show that the TPPP provides good approximations to the more cumbrous models
with streets characterized by Poisson line/stick processes; and we prove that
the accuracy of the TPPP further improves under shadowing. Lastly, we use the
MD results to control network congestion by adjusting the transmit rate while
maintaining a target fraction of reliable links. A key insight is that the
success probability is an inadequate measure of congestion as it does not
capture the reliabilities of the individual links.
