TBH, this is already quite a step. There's much more data available. I worked on systems that also had LPRs (license plate readers), and they generate more detailed information: you'd get 1M vehicle passings per day in a medium-sized city. But they're privacy sensitive. Same goes for bluetooth detection or face recognition. Data owners aren't going to expose that kind of fine-grained data to the general public easily.

Do you know if there are datasets with pseudonymized LPR data available? I'm not interested in the actual license plate obviously, but being able to so say that two data points are the same car would be extremely interesting data especially for these larger and larger datasets spanning longer periods of time.

I'm also aware there is still a bit of a privacy concern with that type of data but honestly don't really have the math background to know exactly how that occurs or the extent to which you can minimize it.

Riding a bike I'm still interested in the volume of cars on the road, and probably more interested in the volume of cars than the volume of bikes to be honest.

It depends on the needs. In your case (it seems) you are looking for the safest (on shared infrastructure) and / or cleanest routes for biking. And the same value can be extracted for vehicle users (more efficient routes). In my case, it is mobility planning. I wasn't denying the value of the dataset, I was just asking for more :)