Some thoughts on GIS and transportation modelling

Transportation modelling is a well established domain with dedicated experts and sophisticated software packages. Still, we thought it could be worth to take a closer look on it from an explicit spatial perspective. This is why Gudrun internet and I have organized a special session entitled “Spatial perspective on transportation modelling” at this year’s GI-Forum conference  ( internet).
We had a session with five short presentations and an extended joint discussion and a workshop session. This very brief summary simply serves as a reminder of some of the major issues that were raised.

The paper session on Wednesday was a real personal highlight. Not only the presentations were inspiring, but the audience was big and active. We had presentations from various fields, covering quite a broad range of topics (all papers are online internet as open access):

1) Gudrun provided insights into a first version of an agent-based bicycle flow model, where she demonstrated how aggregated flows emerge from the individual behaviour of numerous agents in space and time. One of the major conclusion was that while the model as such seems to generate feasable results, the validation is rather tricky since the necessary data are hardly available.

2) Christoph internet gave an excellent presentation on how to link the abstract model space with the geographical space and the model steps with a temporal continuum. Additionally he presented his approach to speed up the model performance when it contains routing functionalities. With an intelligent network simplification he was able to run the simulation 12 times faster than with the initial network graph.

3) Somehow connected to the preceding two presentations, Johannes internet gave an introduction to cognitive agents as counterparts of selfish agents, which are assumed in most routing and navigation applications. With regard to current transportation models, Johannes estimated that those models might be more accurate and thus more meaningful when “smart” agents are incorporated.

4) Leaving the field of agent-based models, Rita internet answered the question what geographers could contribute to transportation modelling in a very beautiful (literally!) way. Working on the TAPAS internet traffic model she emphasized the role of visualization for the validation and communication of the model results. Especially the spatial context of a map helps to make sense of what the model calculates and how it actually works.

5) In the last presentation of this session the award winner of the AGEO student award, Daniel Steiner internet presented parts of his master thesis where he worked with real-time data from public transit. What became very clear in this presentation was, that it is hard to find PT companies that provide real-time data and that it is even harder to use these data in models and analyses because of quality issues.

In a second session, that was organized in a workshop format, three topics that were raised in the presentations and the joint discussion were further worked on:


workshop_groupIn the very active small working groups, it quickly turned out that we as geographers do have something to contribute to the domain of transportation modelling and that there is still a lot of work to do!
In the context of data for transportation models these points were – among others – briefly discussed:

  • There are lots of static data available, mostly following an established standard. Although the number of sensors is skyrocketing they are less likely accessible; at least in many parts of the world. Additionally there are numerous standards for all kinds of sensor data, what makes it cumbersome to integrated data from different source in one and the same model. Beside measured data there are also calculated or estimated data, such as interpolations. For such data hardly any standard exists; most often these data are a kind of black box where you don’t know how they were generated.
  • The latter factor directly leads to the urgent need of sound metadata for transportation data and derived products. It is of crucial importance to know under which circumstances and for what purpose data were captured. For the interpretation of derived data (e.g. flow volumes) it is necessary to know how they were calculated etc. Without providing such information the reliability of modelling results suffers enormously.
  • An interesting observation was that whereas most often spatial data are used as inputs for transportation models, the models themselves are non-spatial, meaning that the relation between the model objects is abstract and not geographically defined.
  • Concerning the scale and aggregation level of data a rather pragmatic rule of thumb emerged: data availability, the availability of tools, processing power and the research question decide on what data are being used.

From the group working on ABM and cognitive agents a rather straight forward research agenda was drafted. The group started from three distinct characteristica of agent-based models: exploration of cause-effect relations, non-intuitive phenomena at system level, local scale. From there, the group identifed three areas of research.

  • How to shift between scales and model types (top-down vs. bottom-up)?
  • How does ‘smart’ behaviour of cognitive agents impact traffic flows on a broader scale?
  • How can the performance issue be dealt with in a reasonable way?

The third group worked on the role of geovisualization and came up with a nice paradigmatic (in the cartography community) conclusion: maps and geovisualizations are not only for communicating (one way) results but they serve as capable interface for the exploration of and interaction with the data and the model. Besides, maps and map-related visualizations put transportation models into an explicit spatial context. Thus the model and the results can be related to the environment what on the one hand can explain results and on the other hand generates new hypothesis for further investigations. At least two issues were regarded as yet unsolved:

  • How to determine the appropriate trade-off between complexity (information load) and simplicity in geovisualizations?
  • How to design visualization environments that are flexible and adaptable to facilitate real multi-perspective approaches?

Some of the aspects we were working on are documented on these flipcharts.


Of course there is lot more to work on. And that’s exactly what we are going to do now. If you want to contribute or have comments on the few points raised here, just leave me a note. I’d be more than happy to learn from you and extend the group of geographers and GIS experts that strive to contribute their spatial know how to transportation models. Such an interdisciplinary approach is, from my point of view, especially valuable were established transportation models have fallen short so far and that is in the field of active transport.



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