Brief introduction for a student project which deals with the possibilities of agent-based simulation approaches for estimating bicycle traffic in an urban road network.
Vision and reflection on Mining Software Repositories research in 2024
Agent-based simulation of bicycle traffic - Background information
1. Simulating Bicycle
Traffic in Salzburg
- Background Information
Martin Loidl
Department of Geoinformatics, Z_GIS
University of Salzburg
martin.loidl@sbg.ac.at | http://gicycle.wordpress.com
2. Bicycle Promotion
Urban agglomerations suffer from negative effects of -
still growing! - car traffic
Environmental impact
Financial/economic impact (externalities!)
Social impact
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reuters.com salzburg24.at salzburg.orf.at
3. Bicycle Promotion
Bicycle as efficient, urban mode of transport
Cheap
Accessible
Environmentally friendly
Healthy
Flexible
Fast within 5-10km
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6. Lack of Data
“Such a lack of reliable and high-resolution data
about bicycle-specific factors is hence one of the most
central issues in research into cycling, as it often
hampers to get in-depth knowledge on the factors that
significantly influence both bicycle use and cycling
accidents.” (Vandenbulcke-Plasschaert 2011: 20)
“Without information about how much cycling is being
done, statements about how many cycle crashes occur
are of limited use.” (OECD 2013: 63)
VANDENBULCKE-PLASSCHAERT, G. 2011. Spatial analysis of bicycle use and accident risks for cyclists. PhD, Université catholique de Louvain.
OECD 2013. Cycling, Health and Safety. In: TØRSLØV, N. (ed.). Paris: ITF-OECD Working Group on Cycling Safety.
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7. Data needed
To know [estimate] when + where + how many
Benchmarking („What effect do my initiatives have?“)
Demand-based planning („Where is infrastructure most
needed?“)
Traffic management („How can I guide bicyclists to
efficiently distribute all traffic participants in the network?“)
Routing information („At which point of time are certain
connections suitable/to be avoided?“)
Accident analysis („What is the risk to be involved in an
accident?“)
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8. What we have so far I
Epidemiological analysis (prevalence, risk) based on
mostly weak, highly aggregated data
Study from 2012 (ISI-indexed) uses data from study from
2008
Study from 2008 refers to data from the European
Comission, published in 2002
This publication is an annual report for 2000
The annual report uses data which where nationally
collected between 1970 and 1997
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https://gicycle.wordpress.com/2014/02/11/where-do-the-data-come-from
9. What we have so far II
Traffic models for motorized individual
traffic and partly for public transport
Obligation to register cars (know exact
number of vehicles)
Large intrest in data telematic systems
No traffic models for bicyclists and
pedestrians
No registration
Lower demand (better: direct economic
pressure)
Many influental variables
Passenger car
Public transport
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mvv-muenchen.de
10. What we have so far III
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Traffic flow simulation
http://www.mtreiber.de/MicroApplet_html5
11. What we have so far IV
Single counting systems along major routes
Don‘t necessarily allow for global information
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Stadt Salzburg
12. What we want to have
Estimation of flows per segment per time interval
Different scenarios for environment and agents
We provide the following data:
Road network as routeable graph with all available
attributes
Socio-demographic data in 1.000/250m grid
LULC data (digital cadastral map)
POIs
Data from 6 counters
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