Estimating Bicycle

Accident Risk

Dynamics

Martin Loidl

Department of Geoinformatics, Z_GIS

University of Salzburg

martin.loidl@sbg.ac.at | http://gicycle.wordpress.com

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

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)

?

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)

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

Correlation

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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

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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