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.
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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9. Correlation
High correlation bicycle volume – crash occurrences on
city level
9
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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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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