  With the increasing availability of transaction data collected by digital
parking meters, paid curbside parking can be advantageously modeled as a
network of interdependent queues. In this article we introduce methods for
analyzing a special class of networks of finite capacity queues, where tasks
arrive from an exogenous source, join the queue if there is an available server
or are rejected and move to another queue in search of service according to the
network topology. Such networks can be useful for modeling curbside parking
since queues in the network perform the same function and drivers searching for
an available server are under combinatorial constraints and jockeying is not
instantaneous. Further, we provide a motivating example for such networks of
finite capacity queues in the context of drivers searching for parking in the
neighborhood of Belltown in Seattle, Washington, USA. Lastly, since the
stationary distribution of such networks used to model parking are difficult to
satisfactorily characterize, we also introduce a simulation tool for the
purpose of testing the assumptions made to estimate interesting performance
metrics. Our results suggest that a Markovian relaxation of the problem when
solving for the mean rate metrics is comparable to deterministic service times
reflective of a driver's tendency to park for the maximum allowable time.
