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The Open Digital Twin Platform Project Kick-Off Event

Category : news

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 (jgruebel@ethz.ch) 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 (jgruebel@ethz.ch).

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.


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New CEUS paper published – presenting our open-source library Trackintel

Category : Uncategorized

Over the past years, MIE lab has been developing an open-source Python library for analyzing human mobility data. Trackintel provides a standardized pipeline for loading, preprocessing, and analyzing tracking data, as shown in the graphic below.  In the paper titled “Trackintel: An open-source Python library for human mobility analysis”, we describe the functionality of the library and demonstrate it in a case study on several datasets.
The paper is available open-access.


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CSFM Seminar Talk on Spatial Data Analytics for Sustainable Mobility

Category : news

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.

 


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New TR_C paper online – “Conserved quantities in human mobility”

Category : Uncategorized

Our new paper entitled “Conserved quantities in human mobility: From locations to trips” was accepted at Transportation Research Part C: Emerging Technologies and is now available online.

We use two high-resolution user-labelled datasets from ~3800 individuals to analyse individuals’ activity–travel behaviour over the long term. We find that individuals maintain a conserved quantity in the number of essential travel mode and activity location combinations over time. A typical individual maintains 15 mode–location combinations, of which 7 are travelled with a private vehicle every 5 weeks. The dynamics of this stability reveal that the exploration speed of locations is faster than the one for travel modes, and they can both be well-modelled using a power-law fit that slows down over time.

Our findings enrich the understanding of the long-term intra-person variability in activity–travel behaviour and open new possibilities for designing mobility simulation models.

Check out the open-access paper online!


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New CEUS paper online: “Graph based mobility profiling”

Category : Uncategorized

Our new paper on “Graph based mobility profiling” was accepted at Computers, Environment and Urban Systems (CEUS) and is now available online. We propose a graph based workflow to identify groups of persons with similar mobility behavior based on person specific graphs that describe the mobility behavior. Our approach is privacy friendly, does not depend on a specific clustering algorithm, is robust against the choice of hyperparameters, does not require specific labels in the dataset, and is not limited to specific types of tracking data. We show in the paper how this can be used to evaluate the impact of new mobility offers.

The paper is open access and the source code of the project is available on our Github.


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New TGIS Paper is Online – “Street-level Traffic Flow and Context Sensing Analysis”

Category : Uncategorized

Our new paper entitled “Street-level traffic flow and context sensing analysis through semantic integration of multisource geospatial data” by Yatao Zhang and Martin Raubal is now online at TGIS.

Sensing urban spaces from multisource geospatial data is vital to understanding the transportation system in the urban context. However, the complexity of urban context and its indirect interaction with traffic flow deepen the difficulty of exploring their relationship. This study proposes a geo-semantic framework first to generate semantic representations of multi-hierarchical urban context and street-level traffic flow, and then investigate their mutual correlation and predictability using a novel semantic matching method. The results demonstrate that each street is associated with its multi-hierarchical spatial signatures of urban context and street-level temporal signatures of traffic flow. The correlation between urban context and traffic flow displays higher values after semantic matching than those in multi-hierarchies. Moreover, we found that utilizing traffic flow to predict urban context results in better accuracy than the reversed prediction. The results of signature analysis and relationship exploration can contribute to a deeper understanding of context-aware transportation research.


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Kick-off of the e-bike city lighthouse project

Category : Uncategorized

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.


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Received Best Vision Paper Award at the ACM SIGSPATIAL’22 Conference

Category : news

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!


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New SIGSPATIAL Paper Is Online – “Improving next location prediction”

Category : Uncategorized

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.


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Challenge award at HackZurich for “The Energymanager”

Category : Uncategorized

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).