Spatial analysis and modelling of bicycle accidents and safety threats

Martin Loidl | martin.loidl@sbg.ac.atRobin Wendel | robin.wendel@sbg.ac.at

Bernhard Zagel | bernhard.zagel@sbg.ac.at

International Cycling Safety CongressHannover, Sept. 15th- 16th 2015

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Bicycle crashes are spatial (and temporal) by their very nature.

GISSpatial analysis of bicycle crashes

Modelling safety threats

Dynamics & Patterns Risk estimation

Status-quo analysis Simulation Routing information

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.

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

Dynamics & Patterns

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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)

Dynamic & Patterns

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Intersections at radial connector roads

Temporally homogeneous

„Structural deficit“ poor infrastructure design

Globally high correlation bicycle volume – crash occurrences

Spatial distribution and variation beyond scale level of whole city?

Risk Estimation

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1

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1000000

Su Mo Tu We Th Fr Sa

Bicycle Traffic

Number of Accidents

r = 0,98

Bicycle traffic: annual counts at one central stationNumber of accidents: 10 year aggregate per day

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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Risk Estimation

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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.

Model – Estimated Risk

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Quality of Accessibility

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Quality of accessibility Faculty of Natural Sciences (Salzburg)

Simulation of the effect of planned measures for safety enhancement

Simulation of Measures

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Routing Information

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www.radlkarte.info: safest routes for Salzburg

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