Category Archives: Uncategorized

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New article in Renewable & Sustainable Energy Reviews published

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How well can you charge your battery electric vehicle with solar power generated from your own roof?


Our latest journal article “Using rooftop photovoltaic generation to cover individual electric vehicle demand—A detailed case study” shows that this works surprisingly well even without a dedicated battery home storage.
The article was also featured in the ETH News and other media outlets. Check out the press release or the journal article published in Renewable & Sustainable Energy Reviews (open access).


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3rd place in the Traffic4cast 2021 extended challenge

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Nina Wiedemann of MIE Lab achieved 3rd place in the NeurIPS 2021 Traffic4cast competition!
In this year’s extended challenge, the task was to predict traffic volume and speed in unknown cities, based on movie-format spatio-temporal data. Our team could improve the generalisation ability of the convolutional neural network with a patch-based approach. For more details on the method, check out our preprint on arXiv.


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New Survey Paper Is Published – “Applications of Deep Learning in Congestion Detection, Prediction and Alleviation: A Survey”

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How can deep learning be used to make road travel safer and faster at the same time? Can we achieve these two objectives while ensuring fairness across individuals or user groups? How important is the percentage of data available for better model performance? Are there potential conflicts of interest if a small number of private players cater to the route suggestion requirements for the majority of the population? Check out the latest publication from MIE-lab- “Applications of deep learning in congestion detection, prediction and alleviation: A survey”, authored by Nishant Kumar and Prof. Dr. Martin Raubal. The paper attempts to answer these questions based on the current state of research. The paper is available open access at TR:C.

As the title suggests, the paper covers three specific tasks. In the first part (congestion detection), we summarize how the deep learning models that were initially developed for computer vision tasks are being applied in detecting traffic congestion. A clear distinction is seen between the models and data sources used in different geographical locations. In the second part (congestion prediction), we summarize why congestion prediction is a more difficult task than traffic prediction. We discuss specific examples of how researchers have leveraged the domain knowledge from transportation (such as the heterogeneity of road networks) to improve the overall prediction accuracy of deep learning models. In the third part (congestion alleviation), we summarize how deep learning is being used for alleviating congestion. We review the challenges in implementing demand-side solutions argue in favour of system-level optimizations with the policymaker in the loop.

Finally, we draw attention to the bigger picture and the potential clash of interests between our efforts to alleviate recurrent and non-recurrent congestion. Presented below is a hypothetical U-curve showing the current understanding of how traffic congestion and road safety are related.


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A Clustering-Based Framework for Individual Travel Behaviour Change Detection

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In our recently published 2021 GIScience paper “A Clustering-Based Framework for Individual Travel Behaviour Change Detection“, we propose a clustering-based pipeline to delineate travel behaviours and detect possible change periods/points from raw GPS recordings. In particular, considering trip mode, trip distance, and trip duration as travel behaviour dimensions, we measure the similarities of trips and group them into clusters using hierarchical clustering. Two different methods are then proposed to detect changes in an individual’s observed usage proportion of trip clusters. Through testing the framework on a large-scale longitudinal GPS tracking dataset, we demonstrate its effectiveness in detecting change periods/points by jointly considering multiple travel behaviour dimensions.

The code is openly available on GitHub, with the possibility to reproduce the framework on the Geolife dataset. The study is to be presented at GIScience 2021 and the pdf version is available here.


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Keynote and paper presentation at the 12th International Symposium on Digital Earth (ISDE12)

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The MIE-Lab will be present at ISDE12 in Salzburg this year. Professor Martin Raubal will give a keynote with the title: Spatial decision-making for sustainability on Tuesday July 6th. Furthermore, Henry Martin will present the paper Using Information and Communication Technologies to facilitate mobility behavior change and enable Mobility as a Service in the solution for society & citizens track on July 7th. Find out more about the program here . The journal issue can be found here and the paper is available here.


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Doctoral Examination of Dominik Bucher

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Dominik Bucher has successfully defended his doctoral thesis titled “Spatio-Temporal Information and Communication Technologies Supporting Sustainable Personal Mobility” on 21 September 2020.

His research revolved around the question of how smartphone-tracked individual mobility data can be analyzed and utilized to support people in transitioning towards sustainable mobility usage. To this purpose, analysis methods (that identify transport modes, extract preferences and context, and detect changes in behavior), routing algorithms (that put an emphasis on personalized inter-modal transport involving a large number of transport options) and communication strategies (based on research on motivation and persuasion and evaluated using the large-scale mobility study GoEco!) were presented. Overall, if individual mobility data is used to give people eco-feedback and alternative route options in a timely manner, it is successful in helping people think about and adopt more sustainable mobility styles.


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We Welcome Dr. Yanan Xin to the MIE Lab Team

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We welcome Dr. Yanan Xin as our newest team member. After successfully completing her PhD at the Pennsylvania State University, she joins the MIE lab in the role of the lead researcher.


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Exploring Choices between Internal Combustion Engine Cars and Electric Vehicles

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In our recent publication “Exploring Factors that Influence Individuals’ Choice Between Internal Combustion Engine Cars and Electric Vehicles“, we use a large dataset of people owning both an Internal Combustion Engine (ICE) car as well as an Electric Vehicle (EV) to determine the impacts of various predictor variables on their choices between the two car types. The gained insights may give additional information to assess common uncertainties regarding EVs: “How far can I drive with a fully charged vehicle? How quickly does the battery wear out? Does the reduced range and/or lack of a substantial number of charging stations impact my mobility?”

We find that chocies between ICE cars and EVs are regular considering an individual user, but that it is almost impossible to guess how someone will choose for a given trip if nothing about the person is previously known. This is a strong indication that most trips can easily be performed with any of the two vehicle types, and only individual preferences and circumstances determine the choices.


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MIE Lab involved in Future Resilient Systems II programme

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The second phase of the FRS programme at the Singapore-ETH Centre officially started on April 1st with an online research kick-off meeting. It was launched in the midst of a global crisis – COVID-19, highlighting the need to better understand and foster resilience. Within FRS-II there is a particular emphasis on social resilience to enhance the understanding of how socio-technical systems perform before, during and after disruptions. MIE Lab researchers will contribute within a research cluster focusing on distributed cognition (led by Martin Raubal). More specifically, we will develop a methodology and prototype for detecting weak signals in mobility data to identify potential disruptions.


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Interview with Former Lab Member Dr. David Jonietz

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Our former lab member Dr. David Jonietz recently gave an interview regarding how geospatial data and a digital map of the world can help transform mobility. Currently a research group leader at HERE Technologies, David Jonietz points out how we can step beyond simple maps to create more comprehensive digital representations of reality, which in turn can be used for traffic prediction and management, optimization of mobility systems, and more.

Read the full interview on the SCCER Mobility homepage.