Last week I gave another PhD progress report at ZGIS' PhD seminar. I showed a couple of preliminary results of a study I'm planning to publish in spring 2016. Suggestions, comments and questions are highly welcome at this stage.

1. Estimating Bicycle
Accident Risk
Dynamics
Martin Loidl
Department of Geoinformatics, Z_GIS
University of Salzburg
martin.loidl@sbg.ac.at | http://gicycle.wordpress.com

2. Overview
 Risk estimation
 Aggregated statistics
 Risk estimation on a local scale
 Exposure variable
 Accident data
 Risk estimation
 Risk estimation – risk model
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http://www.roydwyer.com/wp-content/uploads/2013/04/Bicycle-Car-Accident-
Attorney-Portland-Oregon.jpg

3. Risk Estimation
 Reason for bicycle risk estimation
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4. Risk Estimation
 Reason for bicycle risk estimation
4
Random
Correlated
Dangerous (high risk)
?

5. Risk Estimation
 Need for adequate exposure variable
 Distance travelled
 Total travel time
 Number of trips
 [Inhabitants]
 Availability, quality of exposure variable
 Traffic models
 Primarily for MIT and PT
 Counting stations
 Representative spatial distribution
 Tracking apps
 Biased sample
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6. Counting Station
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7. Data from Tracking App
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8. Aggregated Statistics
 Vandenbulcke et al. (2009), Yiannakoulias et al. (2012), …
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Municipalities, Belgium
Census districts, Hamilton (Can)

9. Correlation
 High correlation bicycle volume – crash occurrences on
city level
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1
10
100
1,000
10,000
100,000
1,000,000
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

10. Correlation
10
1
10
100
1,000
10,000
100,000
1,000,000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Bicycle Traffic
Number of Accidents
r = 0,97
Bicycle traffic: annual counts
at one central station
Number of accidents: 10 year
aggregate per day

11. Exposure Variable
 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.
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12. Accident Data
 Geo-located bicycle accidents Salzburg
 2002-01 – 2011-12
 3,048 valid records
 Source: police reports
 Underreporting
 Biased sample: overrepresentation of bicycle – motorized
vehicle collisions
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13. Risk Estimation
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14.  Modelling potential safety threats
 Example: indicator-based assessment tool (Loidl & Zagel
2014)
Modelling Safety Threats
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.
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15. Model – Risk Estimation
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16. Conclusion & Outlook
 Risk estimation inevitable for targeted interventions
 Exposure variable hardly ever available on local scale
 Results from ABM as „good guess“
 Spatial dynamics and variabilities become obvious on
local scale
 Risk estimation for calibration/validation of model
 Transferability, scalability
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@gicycle_
gicycle.wordpress.com
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