As those data are accessible and can be used by different stakeholders, there seems not really to be an endpoint. At least, the aim is to "contain" that data at some point in different data hubs and models. As mentioned in the artifact, the traffic data hub at the municiplaity of Frankfurt, could be one such "data parking lot"; the State of Hesse plans to build up a "netzweites rechnergestütztes Radverkehrsmodell", a computational bike traffic model based on the data provided by the counters. I wonder how those models, as potential endpoints, in some way have an impact on the very counting practice. E.g., it seems the choice of site for the counters is influenced by this. (however, for the State, the aim is to select representative sites, for the municipality, sites where some sort of traffic planning is taking place, e.g. remodelling a specific cross-roads).
So far the main "fork" seems the use for either the State or the municipality. One difference I found so far is that for Frankfurt the installed counters are considered to deliver context-/place-specific numbers of bike use, hence allowing to monitor a very specific site where the bike counters are installed. At the level of the State of Hesse, the 270 counters spread over the whole state are considered to provide representative data for planning bike infrastructure in the State. I wonder how we can attend to the different pathways taken and where data are transformed in different ways to become either context-specific or representative.
This questions becomes really relevant when comparing the pace of different data as they travel along the data roads. While an data update on the dashboard once a day seems quite fast compared to bike counting processes that happen every couple years (as with the Stadtrandzählung every five years or the national mobility studies every couple years). However, compared to car data, it is rather slow as so far I assume car data is updated in real-time to the traffic dashboard and traffic monitoring. the question is for which interpretations this makes a difference. If we want to monitor the overall traffic over a longer period of time, once a day or even once a year seem suffice. But if it concerns the movement of traffic at specific times a day, it does not provide any relevant outcome.
Data mutate in the form of "reductions" but also by being complemented with more data (meta data, time stamps). The juxtaposition/ comparison/ relation to other data along the way (to determine if it is a bike or not; as one bike data nod in a map and hence adding to "bikes in Frankfurt"; as bike data in relation to other traffic data) is providing different data contexts on each site. In a sense it is an endless (?) bifurcation - created as a binary that becomes again part of a different context where it creates another bifurcation (I am thinking here of Strathern's binary license as picked up by Andrea Ballestero in Future History of Water). Any reduction becomes enmeshed in a wider context. This could be a way to focus on both the reductions and proliferating contexts as part of the journeys.
As described in the artifact, the bike counters are co-financed by the state of Hesse and the city of Frankfurt. While we know that the signals transformed into data and numbers are presented in the ecocounters dashboard, we do not know yet where those data further travel. Obviously into the data hub of the Straßenverkehrsamt. The questions remains who else is using the data (at the State of Hesse and beyond) and integrating them into other data collections, data models, etc. It would be interesting to see if those data are used for different interpretations.