Tagged: FCD

Floating Bicycle Data

Our colleagues from Salzburg Research (SR) internet are very active in the field of floating car data generation, management and analysis. Among others, this real-time traffic status service internet is fed by their data.
In order to establish a community of researchers, authorities and companies around the topic of floating car data, SR hosts the annual “FCD Forum” in Salzburg. This year, I had the honor to contribute to the program internet. Since we have been working a lot with bicycling data over the last years, I was asked to evaluate the potentials of a conceptual transfer from FCD to “Floating Bicycle Data”. Well, a very fundamental finding in my research is that the term “Floating Bicycle Data” is not established yet in the scientific literature. Thus, the term is to be regarded as a word game derived from the forum’s agenda. However, I think it makes perfectly sense to invest some efforts in this context.

In my presentation internet, I started my argumentation from the fact that a) bicycle traffic is a relevant element of urban mobility, b) the modal share is likely to increase in the next years and c) a sound evidence base is required for future investments in bicycling infrastructure.
Currently, very little is known about the spatial and temporal distribution of bicycle traffic within cities. Comparably few permanent counting stations, sporadic, punctual counting campaigns and irregular mobility surveys do not provide sufficient and reliable data to support evidence-based policies on the local scale level. On the other hand, the popularization of the “humans as sensors” concept (Goodchild 2007 internet) has opened new possibilities to acquire data on bicyclists’ movements in urban networks. When talking about floating bicycle data, I used it as a catchy term, which summarizes all kind of geo-located movement data from bicyclists; they don’t need to be necessarily in real-time.

As I’ve shown in my presentation, there a numerous application examples where floating bicycle data would make perfectly sense. However, there are several conceptual challenges, which need to be considered (most of them are also relevant for floating car data):

  1. When floating bicycle data are harvested through crowd-sourcing applications the data are not necessarily representative for the entire population. I referred to participation inequality or the 90-9-1 rule (see Nielsen 2006 internet) in this context. Additionally, different apps are used for different purposes. Thus, the data might be biased for example towards leisure trips (as it is the case with Strava internet data in Salzburg).
  2. Currently, there is no common data standard and the heterogeneity of bicycle mobility data is huge. Good news in this context were published earlier in this year by the European Commission (see this report internet from the COWI project).
  3. Since there is no obligation to register bicycles, the (spatial distribution of the) total population is unknown. Consequently, it is hard to estimate the total bicycle traffic volume from samples. In contrast to that, cars are registered and at least the┬ácar holders’ address is known.
  4. In order to further process movement data (GPS trajectories), a sound and very detailed reference graph is required for map matching. In most cases network graphs are not available at this level of detail (this holds true for authoritative data as well as for OSM). Consequently, GPS trajectories can only be matched to center lines at the moment.

Although this selection of challenges might be regarded as obstacle for a broader engagement (I prefer to interpret them as research opportunities), I expect the topic of floating bicycle data to emerge in the coming years for a simple reason: the market for floating bicycle data is definitely smaller than for floating car data. But, bicycle traffic is already a major element in urban traffic and its share will become even more substantial in the next years. As a consequence, cities need to invest in adequate infrastructure and these investments will hardly be made without a sound evidence base. Floating bicycle data could close a significant gap in this regard.

 

If you are already working with floating bicycle data (but haven’t used the term yet), have ideas on how to further push the topic or simply want to comment on the concept, please do not hesitate to contact me! I’m happy to learn from your expertise.
For those who are about to write a thesis in this or a related context, have a look at this proposal internet.

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