The official name of our department at the University of Salzburg reads a bit cumbersome: Interfaculty Department of Geoinformatics. Now, there is an administrative reason for this (for details have look at our website ). But by far more important is the philosophy behind the prefix interfaculty. It means that GIS is regarded as cross-sectional tool- and mindset.
If you’re interested in one of the many outcomes of such inter-disciplinary work, you might join one of our workshop sessions (“Spatial perspective on transportation modelling”) at the GI-Forum conference in Salzburg next week. It’s organized by my colleague Gudrun Wallentin and myself. Gudrun is an ecologist by training and an expert in spatial simulation. An ecologist working together with a geographer on transportation issues – can there be any relevant outcome? Well, I’d like to give you an example from my current PhD research …
On several occasions I’ve already pointed to the benefits of a geospatial analysis of bicycle accidents. Knowing where (and of course when) accidents happen is a crucial information for targeted counter measures. As long as the analysis exclusively focus on accident frequency, you are fine with geocoded accident reports (apart from data quality issues and severe underreporting). But when it comes to risk calculation it becomes tricky. Here are two examples how risk calculations are commonly done:
1) Accidents per inhabitants per census district.
This migth be a valid approach if large areas were compared with each other, but on the city level useless results are produced. Have a look at the map of Salzburg below. On the left side the number of accidents per inhabitants is calculated for each census district. High risks are indicated at the periphery although the absolute number of accidents is comparably low. This if of course due to the fact that relatively few people live in this areas (the aerial image of the city gives you a perfect overview) while they are frequently traversed by commuters and leisure bicyclists.
2) Accidents per distance travelled per census district.
Yiannakoulias et al. (2012) , for example, use this approach. They estimate the total distance travelled from commuting data extracted from the Canadian census. While the presented results look reasonable, they don’t allow for a downscaling to the street level. Apart from this, the data availability is not always that good. Consequently the total distance travelled – independently from the scale of the reference units – is subject to numerous assumptions and estimations.
There are some more approaches which pop up from time to time (recently a reviewer suggested to me to relate bicycle accidents to LULC data …), but in the end we always face the problem that we don’t have a glue how many cyclists are actually on the road. For motorized traffic sophisticated traffic flow models exist. They are based on huge amounts of data from an extensive network of counting stations and on board sensors.
With an equivalent for bicycle traffic sound risk calculations for each road segment and different points of time would be possible. Currently two major drawbacks (at least in most cities) make it impossible to simply transfer MIT models to bicycle traffic: there is no obligation to register bicycles (thus we don’t know the statistical population) and very few counting stations. The latter issue is partly met by VGI data, such as data from the fitness app Strava (see Griffin & Jiao (2015) ). But these data are neither representative for the whole traffic (the focus lies on leisure trips) nor for the whole population (the app is used by a non-representative fraction of the bicyclists).
Discussing these issues with Gudrun (over several interfaculty cup of coffee) brought us to the idea to test the applicability of agent-based (ABM) models for simulating bicycle traffic flows in an urban network. Using ABM in the transportation modelling domain is a real “minority program”. There is a very inspiring overview paper by Bazzan & Klügl (2014) , but apart from this very few literature actually does exist. To my current knowledge ABM has never been used for the simulation of bicycle traffic flows.
At the above mentioned workshop session at the GI-Forum conference Gudrun and I are going to present the results of our first try (pre-print of the paper ). To be honest: I didn’t expect such nice results. While there are several issues which need to be improved, the results definitely push us to further work on this topic. And of course, to use the simulated bicycle traffic flows for risk calculations.
As a result from the ABM we have simulated bicycle flows for every road segment and for every point of time. This allows for a risk calculation (or to be more precise: risk estimation) on the most detailed scale level. For the risk estimation the reported bicycle accidents for the years 2002-2011 from the city of Salzburg (Austria) are used. The bicycle traffic flow (number of trips per segment) is the averaged sum for one year. The following analyses use a regular hexgrid as reference unit. Alternatively the single road segments could have been used.
In the left map the total number of accidents within the 10 years of observation are related to the reference units. Accident hot-spots along the main bicycle corridor along the Salzach river become obvious. Relating the accident occurrences to the total network length (center) offers little additional explanation. Spatial clusters of bicycle accident occurrences emerge along the most frequented roads. Both maps indicate hot-spots in the city center, what could lead to the misinterpretation, that these are dangerous places for bicyclists.
Only from the right map information about dangerous places, that are segments with a high risk, can be deduced. Compared to the other two maps the image flips: the risk along the Salzach river is much lower than in the periphery. Risk hot-spots emerge where the quality of the bicycle infrastructure is comparably low and the MIT volume is high.
From this simple example several conclusions about accident risk for bicyclists can be drawn. But the point I want to make here is to demonstrate how useful ABM is in this context. It helps to gain a rough idea of the spatial and temporal distribution of bicycle flows and it tackles the constant problem of data (and information) shortage. Compared to aggregated statistics ABM allows for analysis on a much more detailed level. Once having risk estimates further analyses and reasoning are possible. For example the correlation between infrastructure and risk can be investigated. Or the question to which degree the number of bicyclists on the road increases (or decreases?) the overall risk can be answered. You see, we see lots of work ahead!
If you have any ideas how the merge of ABM and GIS can be further used in the transportation domain, if you have suggestions for improvements or if you are an expert in any related domain that wants to discuss over some more interfaculty cups of coffee – please feel free to use the comment or contact function! And if you are the GI-Forum conference anyway, join us on Wednesday, 8th July, at 1pm in room 413 (first floor). I’m looking forward to inspiring conversations!
First, an interfaculty department is great – it brings together an ecologist and a geographer to work on transportation modelling.
Second, ABM helps to simulate bicycle traffic flows, which can serve as input for risk estimations.