The data is unfortunately not granular enough. It combines "reserved spots" and garages. A reserved spot could be an open parking lot with an assigned number. It doesn't help with the charging at all.
I'd guesstimate that the cars parked "american style" (in a private garage or on the driveway of the house) in Europe is < 10%. You really need to live in a small town or a village to find these setups.
> PyTorch offers a way to export to ONNX but you will encounter various errors. [1]
I mean sure, there are limitations, but this is greatly exaggerating their impact in my experience. I'd be curious to hear from anyone where these have been serious blockers, I've been exporting PyTorch models to ONNX (for CV applications) for the last couple of years without any major issues (and any issues that did pop up were resolved in a matter of hours).
This doesn't exactly answer your question, but what I settled on during my dissertation (granted, that was a few years ago now) was SUMO: https://www.eclipse.org/sumo/
IIRC you could import maps from from open street map, but I'm not sure if it has a "headless" mode, without all the visualisation.
On the fence about this. On the one hand I agree that sensitive data should stay local, but on the other hand this would make their very valuable asset (the model) public.
