Ayda Grisiute joins MIE Lab on May 1st as a Ph.D. student. Before joining MIE Lab, Ayda worked as a researcher at Singapore-ETH Centre for the Cities Knowledge Graph project. Ayda has a background in architecture and city planning. She received her master’s degree in Architecture from Aalto University, Finland, and her bachelor’s degree in Architecture from Vilnius Academy of Arts, Lithuania. In her previous studies, Ayda focused on algorithmic and data-driven design methodologies. Ayda is excited about urban knowledge management and representation with graphs and she is looking forward to an amazing journey at MIE Lab. We are happy to have her join the team!
On March 30th the kick-off event for the project “An Open Digital Twin Platform for Research on the Swiss Mobility System” (ODTPR-SMS) took place at the LEE building together with our research partners at the Institute for Transport Planning and Systems, the Swiss Data Science Center and the Center for Sustainable Future Mobility. In the latter, the Geoinformation-Engineering group is a key stakeholder. The project is part of the Swiss National Strategy and Action Plan for Open Research Data and funded by swissuniversities through a Swiss Open Research Data Grant and has been acquired by our lab member Jascha Grübel. ODTPR-SMS has been funded with 1.5 million CHF (matched funding) to develop an Open Digital Twin Platform to underpin development on a mobility-specific Digital Twin “CH on the move”. In the kick-off meeting, all involved parties agreed on the timeline, distributed tasks, discussed the licensing of the software and got to know each other. Everybody was thrilled to contribute to strategically important tool chain that will be developed in the coming 24 months. As part of the project, 2 postdocs will join us at the Center for Sustainable Future Mobility to develop key features of the system.
The Open Digital Twin Platform (ODTP) is bringing together cloud computing, data semantics, licensing, access control and visualisation to enable a holistic processing of data covering data acquisition, data representation, data processing, data analysis and data visualisation. Known tools for mobility research can be automated within ODTP to provide (micro-)services and apply to new scenarios with less overhead than ever before. ODTP takes care of the provisioning and deployment of software allowing researchers to focus on their scientific questions rather than how to get the technology working. To quickly assemble a new Digital Twin, ODTP makes use of a “Digital Twin Zoo” that hosts key software as containers readied to be micro-services. Throughout our project ODTPR-SMS, we will develop a first set of mobility related micro-services that include well-known tools such as MATSim, eqasim and our own trackintel. We are also making available as many data sources on Swiss mobility as possible starting with the National Data Infrastructure for Mobility (NaDIM).
Figure: Overview of ODTP for end users. ODTP facilitates access to open-source software and data through a container-based backend and hosting infrastructure.
ODTP will be accompanied by an open standard for digital twinning. We are looking for experts to evaluate our platform and if you are interested in mobility, digital twins or a combination thereof, please contact Jascha Grübel (email@example.com) for details on how to join our evaluation board which will be published at the Center of Sustainable Future Mobility’s website. We are also looking forward to add more mobility related software tools to ODTP and we are interested to discuss potential new services, again please contact Jascha Grübel for more details (firstname.lastname@example.org).
Reference: Grübel, J., C. Vivar Rios, M. Balać, Y. Xin, R. M. Franken, S. Ossey, M. Raubal, K. W. Axhausen and O. Riba-Grofnuz (2023a) “CH on the move”: Introducing the Prototype Digital Twin of The Swiss Mobility System, paper presented at the Swiss Transport Research Conference 2023.
Professor Martin Raubal and Thomas Hettinger from SBB will give the first Center for Sustainable Future Mobility (CSFM) seminar talk on spatial data and data analytics for sustainable mobility on 23 March 2023, 17:00-18:10 at ETH Zentrum CAB G 51, ETH Zurich. For more information, please check here.
Our paper “Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis” won the Best Vision Paper Award at the 30th ACM SIGSPATIAL Conference in Seattle, USA. This year’s vision paper selection has been very competitive given the large number of submissions. Among the six selected vision papers invited for presentation, our paper, presented by Dr. Yanan Xin, received the best vision paper award. In this paper, we envision opportunities for utilizing causal inference to enhance the interpretability and robustness of deep learning methods and address challenges in mobility analysis. This direction will help us build safer, more efficient, and more sustainable future transportation systems. For more information, check out our paper and a pre-recorded video presentation.
The vision paper also highlights what we aim to achieve through our project “Interpretable and Robust Machine Learning for Mobility Analysis”. Stay tuned for more exciting results coming from the project!
The resilience of a road network refers to its ability to bounce back to the desired level of serviceability after a disruption. After an accident, a more resilient transportation network can restore the flow of traffic faster than a less-resilient network. At the WCS Science of Cities Symposium, researchers from MIE-lab presented the preliminary findings from their recent research in which they used incidence clearance duration as a proxy for road network resilience. Does scale matter while modelling incidence clearance time? Should we use uniform or heterogeneous grids while modelling incidence clearance time? Questions like these and several others were addressed using real incident and congestion data from road networks in Singapore. Their findings reveal that incidence clearance duration is better modelled by splitting the road network into connected regions of similar topology. The notions of connected regions and urban topologies have a special place in urban road network planning because, on one hand, connected regions of similar topologies allow for the spread of traffic congestion, whereas on the other hand, these present an opportunity to share accident clearance resources within the connected region. The abstract proceedings can be found here. Look out for their upcoming pre-print!
MIE-Lab members attended the Zürich Mobility Data Workshop sponsored by IARAI at the University of Zürich on July 20th. The workshop brought together researchers based in Zurich that work on different mobility research topics to discuss the results and insights learned in the NeurIPS Traffic4cast competition series organized by IARAI. The workshop sparked a lively discussion on the challenges and opportunities of open mobility data. The workshop is co-organized by Yanan Xin, Cheng Fu (GIScience Center, UZH), together with Christian Eichenberger and Moritz Neun (Institute of Advanced Research in Artificial Intelligence, IARAI).
Our new project “Interpretable and Robust Machine Learning for Mobility Analysis” officially starts today! The project aims to bring together the knowledge of GIScience and Machine Learning, advancing our understanding of how interpretable and robust machine learning methods (especially deep learning methods) can be tailored to mobility analysis with the support of causal inference. This research is funded by the Hasler Foundation and is in collaboration with the Swiss Data Science Center. Please check out our project page for more information.
Dr. Esra Suel joins our team as a Senior Assistant. She is a Research Fellow (part-time) at the School of Public Health, Imperial College London. Since 2018, she also held a Senior Data Scientist position at the Swiss Data Science Center, ETH Zurich where she collaborated with MIE Lab. She obtained her PhD from the Centre for Transport Studies, Imperial College London. Esra’s current research focuses on using emerging sources of large-scale data (e.g., remote sensing, aerial imagery from drones, street-level imagery, mobile phones, and crowd-sourced data) coupled with advances in deep learning methods, along with traditional (e.g., survey-based) data to advance how we make measurements in urban settings and quantify change, ultimately aiming for advanced data-driven policy and behaviour changes for improving urban life.