View
2.177
Download
0
Category
Preview:
Citation preview
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
2
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
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.
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
Dynamics & Patterns
5
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)
Dynamic & Patterns
7
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
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 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
9
Risk Estimation
10
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.
Model – Estimated Risk
12
Quality of Accessibility
13
Quality of accessibility Faculty of Natural Sciences (Salzburg)
Simulation of the effect of planned measures for safety enhancement
Simulation of Measures
14
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
Recommended