Hard to Park? Estimating Parking Difficulty at Scale
Andrew Tomkins, Ravi Kumar, Neha Arora, James Cook, Ivan Kuznetsov, Yechen Li, Huai-Jen Liang, Andrew Miller, Iveel Tsogsuren and Yi Wang
In this paper we consider the problem of estimating the difficulty of parking at a particular time and place; this problem is a critical sub-component for any system providing parking assistance to users. We describe an approach to this problem that is currently in production in Google Maps, providing inferences in cities across the world. We present a wide range of features intended to capture different aspects of parking difficulty and study their effectiveness both alone and in combination. We also evaluate various model architectures for the prediction problem. Finally, we present challenges faced in estimating parking difficulty in different regions of the world, and the approaches we have taken to address them.
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