  To gain theoretical insight into the relationship between parking scarcity
and congestion, we describe block-faces of curbside parking as a network of
queues. Due to the nature of this network, canonical queueing network results
are not available to us. We present a new kind of queueing network subject to
customer rejection due to the lack of available servers. We provide conditions
for such networks to be stable, a computationally tractable "single node" view
of such a network, and show that maximizing the occupancy through price control
of such queues, and subject to constraints on the allowable congestion between
queues searching for an available server, is a convex optimization problem. We
demonstrate an application of this method in the Mission District of San
Francisco; our results suggest congestion due to drivers searching for parking
stems from an inefficient spatial utilization of parking resources.
