What are the potentialities of crowdsourcing for dynamic maps of on-street parking spaces?

Finding a parking space is a crucial mobility problem, which could be mitigated by dynamic maps of parking availability. The creation of these maps requires current information on the state of the parking stalls, which could be obtained by (I) instrumenting the road infrastructure with sensors, (II) using probe vehicles, or (III) using mobile apps. In this paper, we investigate the potential predictive performances of a random forest binary classifier, comparing these three data collection strategies. As for the dataset, we used real infrastructure measurements in San Francisco for solution I. We simulated the crowdsourcing solutions II and III by downsampling that dataset, based on different assumptions. Evaluations show that the instrumented solution is clearly superior over the two crowdsourcing strategies, but with remarkably small differences to the probe vehicle scenario. On the other hand, a mobile app would require a very high penetration rate in order to be used for meaningful predictions.