The whole thing feels a bit over-engineered to me at first glance.

More money is being spent on science, but is more money actually making it through the administrative bloat encumbering most institutions to the actual performance of research?

Anecdotally, I have a colleague who has received funding from the NSF and the amount of regulations and paperwork and various travel and meeting-related obligations related to the funding soak up so much of the actual dollar amount supplied. (You have to use your funding dollars to satisfy the various required meetings, travel, and paperwork-filling.) The constraints are so ridiculous that satisfying them consumes nearly all the resources the NSF provided, and the little that remains is actually not sufficient to perform the research with. Worse, he has now wasted months of his time satisfying various oversight requirements administrated by both the NSF and the research institution he works in, leaving him an unreasonably small amount of time to actually achieve any significant progress on his work. Once this round of funding dries up, he will be left with no choice but to repeat the process in order to secure some more funding to continue to barely make progress on his stated research goal.

If I had to make up a number to describe the dollar efficiency of research funding, in some cases I might assert it is negative: Not only is it just being soaked up by self-serving, efficiency-draining administrative requirements, it literally destroys the most valuable resource (time!), leaving the researcher with none to actually engage in their subject matter of expertise.

There are far too many large, bloated consultancies that specialize not in delivering quality products and services, but rather in "surviving" the government procurement process.

Props as well to the Swiftly team - I had a change to take a peak at some of the APIs they expose to their customers and it's quite valuable. In particular, they roll up stop-pair segment performance on routes by time of day, which allows someone to query for bus performance by discrete route-schedule-segments.

Gathering this type of data is quite labor intensive and a significant technical lift (I was once part of a project doing this with GTFS-RT data from the NYC MTA). This type of information, and the broader ecosystem of performance related API services they provide to their users (based off the limited amount I have seen), can enable operators to extract highly articulated performance statistics about their fleet, on their own.

And wrt anon data versus specific trip data - that is anonymized when the trips are uploaded to the system. There will be no way to identify specific trips with the tool.