  The excessive search for parking, known as cruising, generates pollution and
congestion. Cities are looking for approaches that will reduce the negative
impact associated with searching for parking. However, adequately measuring the
number of vehicles in search of parking is difficult and requires sensing
technologies. In this paper, we develop an approach that eliminates the need
for sensing technology by using parking meter payment transactions to estimate
parking occupancy and the number of cars searching for parking. The estimation
scheme is based on Particle Markov Chain Monte Carlo. We validate the
performance of the Particle Markov Chain Monte Carlo approach using data
simulated from a GI/GI/s queue. We show that the approach generates
asymptotically unbiased Bayesian estimates of the parking occupancy and
underlying model parameters such as arrival rates, average parking time, and
the payment compliance rate. Finally, we estimate parking occupancy and
cruising using parking meter data from SFpark, a large scale parking experiment
and subsequently, compare the Particle Markov Chain Monte Carlo parking
occupancy estimates against the ground truth data from the parking sensors. Our
approach is easily replicated and scalable given that it only requires using
data that cities already possess, namely historical parking payment
transactions.
