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Analysis framework and results of the SBB Green Class pilot studies

Authors:

Abstract

Many of today's urgent challenges, especially climate change and green house gas emissions are closely linked to the movement of people and goods. A possible path towards a more sustainable transport sector is given by the concept of Mobility as a Service (MaaS). SBB Green Class is one of the first large pilot projects for a multi-modal mobility flat rate. Despite the nonrepresentative sample, the results are a detailed observation of MaaS first movers that have access to a comprehensive mobility package. The results show that users integrate the new mobility options into their mobility mix in the long term and use them in combination with public transport. In particular, replacing the conventional car with an electric car leads to significantly lower CO2-emissions on average. In the future, we expect that MaaS offers will become more common and more affordable and therefore will be adopted by a substantial portion of the population.
Swiss Competence Center for Energy Research
Efficient Technologies and Systems for Mobility
Partners
References
Other
Airplane
Car
E-Car
Train
Bus / Tram
Bike / E-Bike
Walk
Modes of transport
The figure shows all triplegs binned by
length. It compares the participants’
mobility behaviour to a pseudo-control
group (see Data & data preparation). This
comparison shows that the EV is mostly
replacing the conventional car and not train and public
transport. Furthermore, there is a preference for the EV
for shorter distances.
Result 1: Modal split
Setting: Two large-scale (189 participants), long-term (1-year)
and high-resolution (24/7 user-labelled GPS tracking) pilot
studies [1] to test multi-modal mobility flat rates. Participants are
equipped with a GA 1st or 2nd class, bike- and car-sharing credit
and either an Electric Vehicle (EV) with P+Rail or an Electric Bike.
Analysis: Survey and GPS travel diary data was analysed over
time and compared to Swiss mobility microcensus data.
Results:
Users integrate new mobility options into their mobility mix.
New mobility options are used in combination with trains and
local public transport.
Replacing the conventional car with an electric car led to
significantly lower average CO2 emissions.
Analysis framework and results of the SBB Green Class pilot studies
The modal split of the participants of the Green Class
(GC) E-Car pilot study shows that the electric vehicle
(EV) becomes a permanent part of the participants’
mobility mix. Furthermore, the EV is used in
combination with public transport and mostly replaces
trips made with the conventional (fossil-fuel based) car.
Henry Martin, Dominik Bucher, David Jonietz, Martin Raubal
Institute of Cartography and Geoinformation, ETH Zurich
Stefano-Franscini-Platz 5, 8093 Zurich, Switzerland
martinhe@ethz.ch, dobucher@ethz.ch, mraubal@ethz.ch
Henrik Becker, Kay W. Axhausen
Institute for Transport Planning and Systems, ETHZ Zurich
Stefano-Franscini-Platz 5, 8093 Zurich, Switzerland
axhausen@ivt.baug.ethz.ch
Result 2: Mode choice
Data & data preparation
Preparation of travel diary data: Participants are
tracked using an app based on tracking technology from
MotionTag [3]. We perform several preprocessing steps
shown in the workflow above.
Comparison to mobility microcensus data: We
generate a pseudo-control group by filtering and re-
weighting the Swiss Mobility and Transport Microcensus
according to the demographics of the GC participants
using an iterative proportional fitting approach (IPF) [4].
Expected impact
Many of today’s urgent challenges, especially climate
change and green house gas emissions are closely linked
to the movement of people and goods. A possible path
towards a more sustainable transport sector is given by
the concept of Mobility as a Service (MaaS).
SBB Green Class is one of the first large pilot projects for
amulti-modal mobility flat rate. Despite the non-
representative sample, the results are a detailed
observation of MaaS first movers that have access to a
comprehensive mobility package.
The results show that users integrate the new mobility
options into their mobility mix in the long term and use
them in combination with public transport. In
particular, replacing the conventional car with an
electric car leads to significantly lower CO2-emissions
on average.
In the future, we expect that MaaS offers will become
more common and more affordable and therefore will
be adopted by a substantial portion of the population.
[1] https://www.sbb.ch/en/travelcards-and-tickets/railpasses/greenclass/about-
sbb-green-class/pilot-projects.html
[2] Bundesamt für Statistik (BFS) und Bundesamt für Raumentwicklung (ARE)
(2017) Verkehrsverhalten der Bevölkerung - Ergebnisse des Mikrozensus
Mobilität und Verkehr 2015
[3] https://motion-tag.com/en/
[4] Stephan, F. F. (1942) Iterative method of adjusting frequency tables
when expected margins are known, Annals of Mathematical
Statistics, 13 (2) 166–178
Result 3: CO2 reduction
The moving average of the weekly CO2-emissions of
Green Class E-Car participants shows a strong reduction
at the beginning of the project (~week 7). CO2-
emissions stay on a low level except for peaks during
holidays. The reduction can be explained by the
replacement of the fossil-fuel based car with the EV
while conserving the share of public transport.
Mobility package and participants
Gender
Male
Female
Age
E-Car pilot study: 139 participants with train pass (GA
1st Class), BMW i3 EV, P+Rail parking space, mobility
car-sharing and PubliBike bike-sharing.
E-Bike pilot study: 50 participants with train pass (GA
1st or 2nd Class), Stromer E-Bike and mobility car sharing.
Both samples are not representative for the Swiss
population in terms of gender, age, average household
income and mobility. All participants were tracked ~6
weeks before the project started.
8. Detect short walks 9. Map Matching
13. Detect customer movements 14. Flag first and last miles 15. Extract within Switzerland data
2. Parsing and import of storyline and
waypoint files in various formats
3. Extract staypoints and triplegs from
storylines
4. Parse and import BMW data
5. Consolidate mode of transport
labels with BMW data
6. Impute missing purpose of
staypoints
7. Detect public transport transfers
10. Detect and flag anomalies11. Detect trips12. Detect tours
16. Aggregation and export of results
1. Database creation
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  • Statistik Bundesamt Für
Bundesamt für Statistik (BFS) und Bundesamt für Raumentwicklung (ARE) (2017) Verkehrsverhalten der Bevölkerung -Ergebnisse des Mikrozensus Mobilität und Verkehr 2015