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Efficient on-street parking sensor placement

When placing sensors in an environment, it may not be possible to directly cover all entities of interest with sensors due to cost or other restraints. This leads to a sensor placement problem in which only a subset of all sensible sensor locations is equipped with sensors. If data concerning the system to be measured is already available or easily procured, sensor locations can be selected in a data-driven approach. Without data, alternative methods have to be applied. In this paper, we present and compare various data-driven and data-agnostic methods for selecting parking sensor locations in a city environment. Experiments using real-world data show that methods only requiring parking bays' locations compare reasonable well to data-driven approaches requiring environment data which may be expensive to acquire.