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Targeted sensing technology for bicycle research - early results from a longitudinal study in Oslo

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Abstract and Figures

This study makes use of a Global Positioning System (GPS) based mobile application called GoEco! Tracker to identify route choice and transport mode in order to observe the before and after effects of a bicycle infrastructure project in Oslo, Norway. Approximately 200 participants were recruited to the study who live or travel in close proximity to Oslo Municipality’s City Route 1 bicycle lane project (Byrute 1). Their travel behaviour was recorded over a period of 1-3 weeks in May and June 2017 and repeated in September 2017. Preliminary results suggest a significant route substitution effect of the existing cyclists, moving from nearby parallel alternatives onto the upgraded infrastructure segment.
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Targeted sensing technology for
bicycle research
early results from a longitudinal
study in Oslo.
Ray Pritchard
PhD Candidate
Department of Architecture & Planning, NTNU
Dominik Bucher
PhD Candidate
Institute of Cartography and Geoinformation, ETH Zürich
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NTNU Sustainability Science 2017 2
Overview
1. Research aim
2. Why sensing technology
3. The available options
4. Chosen approach
5. Case: Oslo
6. Early results
7. Discussion
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1. Research aim
Does new bicycle infrastructure result in new cyclists?
Route shift Mode shift Induced
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2. Why sensing technology?
Bicycle users are human hard to predict
Prioritisation of bicycle infrastructure
Role of new links in network
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3. The available options
GPS units
Smartphone apps active and passive
registration (battery consideration)
Crowdsourced data
Intercept survey
Travel diary
Reidentification from camera
Francke & Lissner 2017
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4. Chosen approach GPS automatic registration
(and classification)
Moves (ProtoGeo)
Google Fit
Commercial TRavelVU, Sense.dat
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4. GPS automatic registration
Moves is not designed for research
Access token required
Connected app to backend GoEco! Tracker
aggregates data
Post-processing for additional mode ID
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4. Recruitment to study
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5. Early results
May and September data collection
Participant retention – OS updates, forgotten username…
1/3 noticed changes in bicycle infrastructure
1/5 self-reported changes in their travel behaviour
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5. Early results Markveien before and after
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Markveien (intervention street) Thorvald Meyers gt (potential source street)
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5. Early results. After: fewer users but mostly
migrated to intervention street
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6. Discussion
Low point resolution filtering needed
More detailed analysis
Direction, different modes, other bicycle
infrastructure projects
Cross reference with traffic counts and
video footage to determine increase in
volumes
Alternative study designs
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Summary
Passive GPS registration through apps opens up for many natural
experiments
Crowdsourced data is not always representative
Open (research) data?
Recruitment remains time-consuming for such natural
experiments
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NTNU Sustainability Science 2017 17
Thanks!
Questions or feedback?
Ray Pritchard
Department of Architecture and Planning, NTNU
Raymond.Pritchard@ntnu.no
... If we consider that many people need to be actively convinced to use e-bikes before the above scenarios become reality, it is also interesting to look at where to put marketing and persuasive efforts. Several projects analyze the use of soft incentives to persuade people to change their behavior [43,10], while other approaches focus on economic or infrastructural changes [62,63]. Knowing where to deploy such incentives for maximal effect is particularly important for companies and organizations operating in the environmental space, but will also concern policy makers and last but not least electric bicycle sellers. ...
Article
Full-text available
To ensure long-term availability of mobility and to keep the effects of humanity on global climate limited, politics, research, and industry currently search for ways to reduce greenhouse gas (GHG) emissions caused by it. A potential reduction could be achieved by transitioning towards more efficient transport modes, optimally powered by renewable energy. Based on mobility (commutes), population, and weather data from Switzerland, we present a model to compute energy savings due to a (hypothetical) widespread deployment of electric bicycles. In different scenarios, we analyze the dependence of the commuting energy demand on users’ preferences about when to take the bike, such as an aversion to biking on cold and rainy days. Our study shows that GHG emission reductions of up to approx. 10% of the total emissions from diesel and gasoline are possible. In combination with a widespread deployment of electric vehicles, further savings of up to 17.5% could be achieved. In particular, the willingness to drive longer commutes by e-bike influences the potential GHG emission reductions, followed by the willingness to use the bicycle at temperatures below 10°C. Using these results, we identify the spatial energy and greenhouse gas savings potential and thus regions that are particularly suited for e-bike use. The identification of regions with high saving potentials allows for targeted marketing, transition-supporting incentives, or infrastructural changes to maximize the reduction of emissions.
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