A very high resolution bicycle flow model
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The document discusses a high-resolution bicycle flow model developed by Dr. Martin Loidl, analyzing urban bicycle crashes and mobility through agent-based approaches. It emphasizes the integration of various data types for simulating infrastructural measures and interventions to promote cycling safety. Ongoing research aims to enhance model complexity while addressing data demand and computing power needs.
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missing data [at local level]](https://image.slidesharecdn.com/2018-10-10-icsc-flow-model-181009142907/85/A-very-high-resolution-bicycle-flow-model-5-320.jpg)
A very high resolution bicycle flow model
- 1. A very high resolution bicycle flow model Dr. Martin Loidl | martin.loidl@sbg.ac.at Barcelona, October 10 2018
- 2. 2 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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- 5. 5 missing data [at local level]
- 6. 6 exposure data demand and capacity effect of interventions
- 7. 7 Foto: ML Foto: Ewald Judt, Austria-Forum
- 9. 9 individual mobility behavior » emergent patterns
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- 15. 15 socio-demographic statistics facilities road graph mobility survey time use statistics
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- 19. 19 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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- 21. 21 LOIDL, M., WALLENTIN, G., WENDEL, R. & ZAGEL, B. 2016. Mapping Bicycle Crash Risk Patterns on the Local Scale. Safety, 2, 17.
- 22. 22 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
- 23. 23 funded by acknowledgments dana kaziyeva & gudrun wallentin (co-authors)