• Onno Zoeter , Chris Dance , Mihajlo Grbovic , Shengbo Guo , Guillaume Bouchard
19th ITS World Congress, Vienna, Austria, 22-26 October 2012.
In this paper we address a challenging problem of predicting on-street parking
occupancy based on sensors with occasionally missing data. These missing values are most
likely not missing at random. However, the exact process behind it is unknown at the outset.
For example, an entry in our data stream might read: “at time t, blockface b, which has
capacity 20, has 18 working sensors, and of those working sensors 10 indicate that a parking
space is occupied.” The challenge is to infer as accurately as possible the actual occupancy of
blockface b, i.e. the occupancy of all parking spaces, including the ones with inoperative
sensors. The fact that sensors are inoperative only occasionally allows us to learn a demand
model and a sensor noise model jointly, and use this to “fill in the blanks” in the best possible
way. We introduce a series of sensor noise models of increasing complexity that are suited for
several sensor characteristics. Cross-validation allows the selection of the suitable model for a
particular application. The sensor noise models we introduce are flexible and can be combined
with any probabilistic model for demand and hence have a very wide applicability. In our
demonstration application we find that sensors are more likely to be inoperative when
parking spaces are empty, in particular during busy traffic periods. This supports the
hypothesis that drive-by-traffic is an important source of noise for the sensor. Compared to
baseline methods based on a missing-at-random assumption our method gives better
predictive performance and avoids a systematic bias in the inferred occupancies.
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