This presentation was given at the International Cycling Safety Congress 2015 in Hannover/Germany. I have argued, that bicycle accidents are spatial by their very nature. Thus GIS analysis and geospatial models can help to gain a better understanding of bicycle accidents and to develop evidence-based safety strategies.

1. Spatial analysis and modelling of bicycle
accidents and safety threats
Martin Loidl | martin.loidl@sbg.ac.at
Robin Wendel | robin.wendel@sbg.ac.at
Bernhard Zagel | bernhard.zagel@sbg.ac.at
International Cycling Safety Congress
Hannover, Sept. 15th- 16th 2015

2. 2
Bicycle crashes are spatial (and temporal) by their very nature.
GIS
Spatial analysis of
bicycle crashes
Modelling safety
threats
 Dynamics & Patterns
 Risk estimation
 Status-quo analysis
 Simulation
 Routing information

3.  Geographical coordinate as common denominator for
multiple layers
 Digital, abstract representation of geospace
Geographical Information Systems
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LOIDL, M. 2016. Spatial information for safer bicycling. In: GÓMEZ, J. M., SONNENSCHEIN, M., VOGEL, U.,
WINTER, A., RAPP, B. & GIESEN, N. (eds.) Advances and new Trends in Environmental Informatics: Selected
and Extended Contributions from the 28th International Conference on Informatics for Environmental
Protection. Berlin, Heidelberg: Springer.

4.  Crashes are not evenly distributed over the network  spatial
and temporal variations
 Know where and when crashes occur  patterns  evidence-based,
targeted safety strategies
 Case Study Salzburg (Austria)
 > 3,000 geolocated crash reports 2002-2011
 Modal split 20% bicycle
Spatial Analysis of Bicycle Crashes
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Pictures © Stadtgemeinde Salzburg

5. Dynamics & Patterns
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6. Dynamics & Patterns
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3,048 crashes at 1,865 locations (1,379 single crash locations)
16 locations with > 10 crashes (6.5% of all crashes)

7. Dynamic & Patterns
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 Intersections at radial connector roads
 Temporally homogeneous
 „Structural deficit“  poor infrastructure design

8.  Globally high correlation bicycle volume – crash occurrences
 Spatial distribution and variation beyond scale level of whole
city?
Risk Estimation
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1
10
100
1000
10000
100000
1000000
Su Mo Tu We Th Fr Sa
Bicycle Traffic
Number of Accidents
r = 0,98
Bicycle traffic: annual counts
at one central station
Number of accidents: 10 year
aggregate per day

9.  Problem of exposure variable  flow model for bicycles
 Agent-based model for simulation of bicycle flows:
 WALLENTIN, G. & LOIDL, M. 2015. Agent-based bicycle traffic model
for Salzburg City. GI_Forum ‒ Journal for Geographic Information
Science, 2015, 558-566.
Risk Estimation
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10. Risk Estimation
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11.  Analysis of historical data  modelling (potential) safety
threats
 Findings become scalable and transferable
 Models as backbones of planning and communication tools
 Example: indicator-based assessment tool (Loidl & Zagel 2014)
Modelling Safety Threats
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LOIDL, M. & ZAGEL, B. Assessing bicycle safety in multiple networks with different data models. In:
VOGLER, R., CAR, A., STROBL, J. & GRIESEBNER, G., eds. GI-Forum, 2014 Salzburg. Wichmann, 144-154.

12. Model – Estimated Risk
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13. Quality of Accessibility
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 Quality of accessibility Faculty of
Natural Sciences (Salzburg)

14.  Simulation of the effect of planned measures for safety
enhancement
Simulation of Measures
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15. Routing Information
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 www.radlkarte.info: safest routes for Salzburg

16.  Mobility ( bicycle safety) is a spatial phenomenon
 GIS helps to gain spatially informed insights and to extract useful
information
 GIS analysis of crash occurrences reveals spatial and temporal
dynamics + allows for risk estimation
 Geospatial models can be implemented in various tools
 Quality assessment in terms of safety
 Simulation
 Information
 GIS can contribute to evidence-based, integrated strategies
for bicycle safety improvement
Conclusion
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@gicycle_
gicycle.wordpress.com