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Spatial analysis and modelling of bicycle accidents and safety threats

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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.

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Spatial analysis and modelling of bicycle accidents and safety threats

  1. 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. 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. 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.
  4. 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 4 Pictures © Stadtgemeinde Salzburg
  5. 5. Dynamics & Patterns 5
  6. 6. Dynamics & Patterns 6 3,048 crashes at 1,865 locations (1,379 single crash locations) 16 locations with > 10 crashes (6.5% of all crashes)
  7. 7. Dynamic & Patterns 7  Intersections at radial connector roads  Temporally homogeneous  „Structural deficit“  poor infrastructure design
  8. 8.  Globally high correlation bicycle volume – crash occurrences  Spatial distribution and variation beyond scale level of whole city? Risk Estimation 8 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. 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
  10. 10. Risk Estimation 10
  11. 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.
  12. 12. Model – Estimated Risk 12
  13. 13. Quality of Accessibility 13  Quality of accessibility Faculty of Natural Sciences (Salzburg)
  14. 14.  Simulation of the effect of planned measures for safety enhancement Simulation of Measures 14
  15. 15. Routing Information 15  www.radlkarte.info: safest routes for Salzburg
  16. 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 16 @gicycle_ gicycle.wordpress.com

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