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“From Location Tracking To Personalized Eco-Feedback: Results From The GoEco! Study” Published by Spotlight on Energy Research

Category : Uncategorized

The ETH Zürich Energy Science Center featured an article about “From Location Tracking To Personalized Eco-Feedback: Results From The GoEco! Study” on their Spotlight on Energy Research blog. You can read the full article here. At this point we would like to thank the Energy Science Center for giving us the opportunity to present our research to their audience!


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Journal paper at ISPRS IJGI accepted!

Category : Uncategorized

Our paper “Assessing the Influence of Spatio-Temporal Context for Next Place Prediction using Different Machine Learning Approaches” has been accepted at the ISPRS International Journal of Geo-Information. We are very happy to announce that it is also featured as cover story of the respective special issue!
For more information, visit our publications site.


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SBB Launches New SBB Green Class Mobility Package

Category : Uncategorized

The SBB (Swiss Railway) have officially launched the new SBB Green Class Mobility Package which combines various sustainable means of transportation and services for road and railway.
Researchers from the MIE Lab will further on provide scientific support for this exciting project.

For more information visit SBB Green Class


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Call for Papers: Spatial Big Data and Machine Learning in GIScience

Category : Uncategorized

Together with colleagues from the University of Illinois at Urbana-Champaign, we are organising a workshop on “Spatial Big Data and Machine Learning in GIScience” at this year’s GIScience conference in Melbourne, Australia.
If you are interested in the process of generating knowledge from large spatial datasets, please note the 1st Call for Papers on the workshop website.


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New project on transportation and logistics for small-scale food producers

Category : General , news

Researchers of the MIE Lab are collaborating with alpinavera to survey the transportation behaviour of small-scale food producers in Switzerland and develop alternative concepts!

For more information, please visit our projects site.


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Full paper accepted at AGILE 2018!

Category : Uncategorized

Our full paper “Identifying and Interpreting Clusters of Persons with Similar Mobility Behaviour Change Processes” has been accepted at AGILE 2018 in Lund, Sweden!
For more information, visit our publications site.


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Extended Abstract accepted at IATBR 2018

Category : Uncategorized

Collaborating with our colleagues from IVT – Institute for Transport Planning and Systems at ETH Zurich, our extended abstract titled “Usage patterns and impacts of a mobility flat rate traced with a Smartphone App” was accepted at IATBR 2018 in Santa Barbara, California.


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OMLETH at Innovedum Event 2017

Category : Uncategorized

The annual Innovedum event on November 23 at the Semper Aula was an opportunity for informal discussion among the Innovedum project leaders, departmental Educational Developers, and students. Around 150 participants attended the event. The Rector gave the welcome speech and presented certificates to the students for their efforts on behalf of Student Innovedum. The event’s highlight was the short presentation of three Innovedum projects under which OMLETH was shown. Martin Raubal and Christian Sailer presented the project and showed the brand new OMLETH explainer video (only in German), which received much positive feedback and interesting discussions at the aperitif.

Photos from the evening by Heidi Hostettler

http://www.heidi-hostettler.ch/Innovedum_2017

The new video OMLETH explainer video


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Continuous Trajectory Pattern Mining for Mobility Behaviour Change Detection

Category : research

Due to recent technological developments, it is now possible to track our movements at a high level of detail and with relatively low effort and cost, e.g., by using built-in GPS-receivers of our smartphones. This novel data source provides exciting new possibilities for increasing the sustainability of our mobility behaviour through monitoring as well as real-time regulation and management of our transport systems. For instance, there are now innovative systems which aim to monitor and directly influence our mobility decisions by providing eco-feedback (e.g., GoEco!). Such systems rely on identifying when and how their users change their behaviour, and e.g., form more sustainable travel habits. This time-consuming and cost-intensive task is currently mostly done manually. In general, however, methods to automatically detect and evaluate behaviour change are needed for understanding how people will react to new mobility options such as electric vehicles, the shared economy or mobility as a service (MAAS).

In this study, we propose a concept for a fully automated system which continuously monitors movement behaviour based on a stream of movement data, and uses data mining techniques to detect behavioural anomalies.

Proposed System

A particular problem here is data incompleteness caused by people not using the tracking application continuously. As these may trigger false alarms, we have to identify and filter these incomplete records in a separate step (Completeness Assessment). Another problem are behavioural changes which are not caused by changing travel habits, but a different contextual situation (e.g., a holiday trip). By additionally comparing the places which are visited by a person, our system is able to filter those temporary anomalies.

Detected Anomalies for One User

The figure shows the mobility behaviour anomalies (blue) and the place-related (yellow) anomalies for a user of our test sample. Behavioural anomalies are detected from calender week 2017-06 onwards. Since the visited places remain the same, we can conclude that this user indeed changed her mobility behaviour habits. In the future, a fully automated system could interpret and automatically react to this behaviour change, e.g., by sending out notifications to the users or analysts, triggering a response (e.g. encouraging or discouraging the observed behaviour change), logging the occurrence of the anomaly in a database, or providing information to an expert for decision support.

For more information, see

Jonietz, D., Bucher, D. (accepted): Continuous Trajectory Pattern Mining for Mobility Behaviour Change Detection. Accepted at: LBS 2018, Zurich, Switzerland.


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The MIE Lab is growing!

Category : news

We welcome Henry Martin as our new team member! With his focus on mobility data analysis, Henry will fit perfectly into the MIE Lab.