Knowing when and where how many people cycle would be valuable for many questions associated with bicycle research, planning and promotion. As I’ve already noted on several occasions, the data availability and quality is generally very poor in this context.
For motorized traffic and public transportation networks, a whole lot of different traffic models and extensive data capture systems (floating car data, sensor networks etc.) exist. However, the demand for high-quality data is enormous as many involved parties have interest in efficient traffic management systems.
There might be several reasons for why there are – in contrast to MIT and PT – only very few cases of sound bicycle traffic models (to be exact, I don’t know of any real macroscopic bicycle traffic model for a whole urban network). On the other hand, lots of practical questions as well as research approaches depend on sound data.
Take for example the investments in bicycle infrastructure. The sums which are spent in the construction and maintaining of the “hardware” are not too small (the bicycle advocate of Salzburg with its 150,000 inhabitants has an annual budget of at least € 1 mio.). But without exact knowledge of how many cyclists actually use this infrastructure or could have been attracted, it’s impossible to judge whether the return of investment is positive or not.
Another example would be the analysis of bicycle accidents and the calculation of risk factors. Again, highly aggregated or roughly estimated data – as they are used quite often – don’t really help.
Slides of introductory lesson for Master’s students
I’ve confronted a colleague at our department, who is an expert on spatial simulation, with this kind of “problem statement”. As she was looking for a nice topic for her course in the Master’s programme of applied geoinformatics anyway, we decided to give the idea of modelling bicycle traffic a try.
Within the next months students will work on several research questions, ranging from model optimization and calibration to different scenarios. In the end we expect a first estimation of bicycle flows for every segment in the road network for different time intervals, environments and scenarios. The bicycle traffic model will be based on agent-based simulation, implemented in a NetLogo environment.
While I was preparing some basic literature (Bezzan & Klügl (2014) for example is worth a read, PDF here), I realized that it might be a long road to meaningful and useable results. There are still major white spots on the map when it comes to using agent-based simulation for modelling bicycle traffic. Actually, little methodological work has been done so far and use cases are rare.
If you have any hints, know relevant papers or have applied agent-based simulation in this context, please let me know!