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Swiss Competence Center for Energy Research
Efficient Technologies and Systems for Mobility
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Introduction
Technological advances, infrastructural limits and the demands for
more ecologically sustainable transport lead to a rapid change in the
ways we perceive and use mobility. A potential pillar for future mobility
is Mobility as a Service (MaaS) [1], where multiple modes of transport are
integrated and made available as an easily accessible, packaged offer.
With Green Class, the Swiss Federal Railways (SBB CFF FFS) offered
such a service as part of a pilot study in 2017 to a limited number of
people. We here present the geographic information system (GIS)-
based framework used to analyze the effects of this offer on the
mobility behavior and greenhouse gas (GHG) emissions of the involved
users. The results show that people change their mobility behavior
depending on the transport modes available to them before their use of
MaaS. Additionally, the electric car (part of the MaaS offer) led to a
noticeable reduction in GHG emissions.
Using a GIS-based Framework to Analyze the Impact of a Mobility as a Service Offer
Mobility as a Service recently saw an increased interest
due to new technological possibilities (e.g., given by
smartphones of people or sensors that track vehicles).
Preliminary studies such as UbiGo [2] analyzed the
challenges and potentials of MaaS in an urban context.
In a collaboration with the Swiss Federal Railways (SBB
CFF FFS), we studied the effects of a MaaS offer with
respect to the way that people change their mobility
behavior and how this influences the greenhouse gas
emissions of the involved persons. To analyze the large
amounts of data we developed a GIS-based framework,
which is presented here alongside some of the key
insights gained by applying the framework to data from
around 140 users. One can see that people’s adaption
of their mobility behavior depends on the available
transport modes and their behavior before their use of
MaaS.
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, jonietzd@ethz.ch, mraubal@ethz.ch
Analysis Framework
The employed analysis framework is centered around a
PostGIS database, which is used to store the
preprocessed tracking and car data, but also the
intermediate datasets (such as map matched routes and
quality indicators) and the final mobility metrics and
user profiles. The visualization module extracts specific
data points from the database and uses them for a
variety of plots.
Modal Split
CO2Emissions User Groups Expected Impact
Depending on the personal context, a MaaS offer can
lead to substantially lower GHG emissions. The
majority of participants in the Green Class pilot study
could reduced their GHG emissions. The participants
that increased their GHG emissions after using the MaaS
offer, were intensive users of public transportation
before the project and partially replaced the train by the
electric car.For the large majority, lower GHG
emissions were possible by replacing journeys with
the internal combustion engine car with the electric
car. People who did not frequently use public transport
before, showed an increase in its use, as the included
general travelcard enabled them to use all public
transport within Switzerland without marginal costs. In
terms of the analysis framework, future work includes a
statistics module that complements the visual analytics
and facilitates statistical analyses, as well as the
integration of (individual) spatio-temporal context (e.g.
weather conditions or air quality) more thoroughly [4].
[1] Boulouchos, K., F. Cellina, F. Ciari, B. Cox, G. Georges, M.
Hirschberg, D. Jonietz, R. Kannan, N. Kovacs, L. Küng, T. Michl,
M. Raubal, R. Rudel & Schenler, W. (2017). Towards an Energy
Efficient and Climate Compatible Future Swiss Transportation
System. SCCER Mobility: ETHZ, PSI, SUPSI, ZHAW.
[2] Sochor, J., Strömberg, H., & Karlsson, I. M. (2015). Implementing
mobility as a service: challenges in integrating user, commercial,
and societal perspectives. Transportation Research Record:
Journal of the Transportation Research Board, (2536), 1-9.
[3] Jonietz, D. & Bucher, D. (2018). Continuous trajectory pattern mining
for mobility behaviour change detection. In Proceedings of the
14th International Conference on Location Based Services (LBS
2018), Zurich, January 2018.
[4] Jonietz, D. & Bucher, D. (2017). Towards an Analytical Framework
for Enriching Movement Trajectories with Spatio-Temporal Context
Data. In Proceedings of the 20th Conference on Geo-information
Science (AGILE 2017), Wageningen, May 2017.
After the pilot study started in week 3 of 2017, the
electric car quickly gains a large share of the modal split.
The importance of the electric car slowly fades over the
course of the project until it stabilizes at around 15 %.
Furthermore, the graph shows weeks with abnormal
mobility behavior such as the summer holidays (weeks
29 – 31) or Christmas (weeks 51 + 52)
