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What if 50% of the existing urban road space was allocated to e-bikes and the slow modes? Seven chairs at the department for civil engineering at ETH join forces to analyze the opportunities and effects of an urban future giving priority to cycling, micromobility and public transport. Our group will contribute with the developement of spatial optimization methods for redistributing the street lanes to design the new bike lane network, considering constraints such as (multi-modal) accessibility and exposure levels of the lanes.

We are delighted to announce that our paper “How do you go where? Improving next location prediction by learning travel mode information using transformers” by MIE Lab members Ye Hong, Henry Martin, and Martin Raubal is now online at arXiv and will be presented at ACM SIGSPATIAL, November 1–4, 2022, Seattle, WA, USA conference.

In this work, we propose a transformer decoder-based neural network to predict the next location an individual will visit based on her historical locations, time, and travel modes. In particular, we design an auxiliary task to jointly predict the next travel mode, with the aim of guiding the learning process of the network. We conduct extensive experiments on two real-world GPS tracking datasets and conclude that considering additional aspects of travel behaviour significantly increases the performance of next location prediction. The overall architecture of the proposed model is shown below.

We participated at the largest Hackathon in Europe, HackZurich 2022, with a team of former and current PhD student of our group, and our app was awarded the challenge price by the company Sensirion! We developed a tool that utilises temperature sensors and building floor plans to serve as a digital energy consultant (for details, see here).

Our paper “Unlocking Social Network analysis methods for studying human mobility” received the Best Paper Award at the 25th AGILE Conference on Geographic Information Science! Nina Wiedemann presented the paper in Vilnius, Lithuania, and it was elected by the audience out of three papers pre-​selected by the conference committee. In the paper we demonstrate how network modelling methods can help to gain insights into the topology and spatial layout of mobility graphs. For more details, see the publication in the conference proceedings.

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).

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.

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.

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.

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.

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.