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A main challenge faced by the state-of-the-art parking sensing systems is to infer the state of the spots not covered by participants’ parking/unparking events (called background availability) when the system penetration rate is limited. In this paper, we tackle this problem by exploring an empirical phenomenon that ignoring a spot along a driver’s parking search trajectory is likely due to the unavailability. However, complications caused by drivers’ preferences, e.g. ignoring the spots too far from the driver’s destination, have to be addressed based on human parking decisions. We build a model based on a dataset of more than 55,000 real parking decisions to predict the probability that a driver would take a spot, assuming the spot is available. Then, we present a crowdsourcing system, called ParkScan, which leverages the learned parking decision model in collaboration with the hidden Markov model to estimate …