bicycle risk estimation - short report
TRANSCRIPT
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Estimating Bicycle
Accident Risk
Dynamics
Martin Loidl
Department of Geoinformatics, Z_GIS
University of Salzburg
[email protected] | http://gicycle.wordpress.com
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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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Risk Estimation
Reason for bicycle risk estimation
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Risk Estimation
Reason for bicycle risk estimation
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Random
Correlated
Dangerous (high risk)
?
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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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Counting Station
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Data from Tracking App
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Aggregated Statistics
Vandenbulcke et al. (2009), Yiannakoulias et al. (2012), …
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Municipalities, Belgium
Census districts, Hamilton (Can)
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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 stationNumber of accidents: 10 year aggregate per day
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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 stationNumber of accidents: 10 year aggregate per day
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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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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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Risk Estimation
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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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Model – Risk Estimation
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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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