An agent-based simulation model for estimating bicycle flows at the local scale level. Presentation slides from International Cycling Safety Congress (ICSC) 2018 in Barcelona.

1. A very high resolution
bicycle flow model
Dr. Martin Loidl | martin.loidl@sbg.ac.at
Barcelona, October 10 2018

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LOIDL, M., TRAUN, C. & WALLENTIN, G. 2016. Spatial patterns and temporal dynamics of urban bicycle crashes—A case study from Salzburg (Austria). Journal of Transport
Geography, 52, 38-50.

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is cycling dangerous?

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missing data [at local level]

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exposure data
demand and capacity
effect of interventions

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Foto: ML
Foto: Ewald Judt,
Austria-Forum

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an agent-based approach
funded by

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individual mobility behavior »
emergent patterns

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cyclist ≠ cyclist

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Fotos: https://pixabay.com

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socio-demographic
statistics
facilities road graph
mobility
survey
time use
statistics

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Travel time, s
min max avg std
simulated 1 186 16.61 16.09
bc 1 8450 766.55 883.83
Distance, m
min max avg std
simulated 67.86 19938.01 3743.62 2708.73
bc 2.7 52583.85 4060.07 5912.52
Speed, m/s (including stops)
min max avg std
simulated 0.58 34.6 4.69 1.9
bc 0.05 35.65 4.76 1.64

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LOIDL, M., WALLENTIN, G., WENDEL, R. & ZAGEL, B. 2016. Mapping Bicycle Crash Risk Patterns on the Local Scale. Safety, 2, 17.

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integration of existing data
into simulation model
 temporal resolution: second
 spatial resolution: meter
 application use cases:
a. simulating infrastructural measures
b. simulation of interventions and
cycling promotion
c. spatially and temporally explicit
exposure data
 it‘s still a model
 data demand and dependency
 model complexity and required
computing power
ongoing research
bicycle-observatory.zgis.at
@gicycle_
gicycle.wordpress.com

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funded by
acknowledgments
dana kaziyeva & gudrun wallentin (co-authors)