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.
Disentangling the origin of chemical differences using GHOST
Spatial analysis and modelling of bicycle accidents and safety threats
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
3
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.
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
9
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
11
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.
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.wordpress.com