Author: mieboss

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New paper in Applied Energy: V2G4Carsharing – A simulation study for 2030

What is the potential for integrating vehicle-to-grid with car sharing in the future? As part of the V2G4Carsharing project, we simulated scenarios for 2030 and quantified the benefits in terms of monetary savings and peak shaving effect. In our case study done in collaboration with the swiss car sharing provider Mobility, we found that Mobility could offer flexibilities between 12 to 50 MW, dependent on the scenario. There is a sweet spot where both car sharing and power grid operators benefit.

Our paper titled Vehicle-to-grid for car sharing – A simulation study for 2030 was now published in Applied Energy! Check out the paper here.

Get in touch if you are interested to learn more, or checkout our project code base and the car sharing simulator.

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New Paper in Transportation Research Part C: Quantifying the Dynamic Predictability of Train Delays

Our paper on “Quantifying the dynamic predictability of train delay with uncertainty-aware neural networks” has been published in Transportation Research Part C!

In light of the importance of accurate delay prediction for transport services and passengers, many predictive methods have been proposed. However, they hardly account for the involved uncertainty and there is a lack of work analysing the dynamic predictability over time. We fill this gap with an uncertainty-aware neural network and a framework for describing the predictability by the prediction horizon. The results on Swiss train delay data show 1) an exponential decay of the predictability by the horizon, 2) a significant portion of (aleatoric) data uncertainty in contrast to model uncertainty, and clear advantages of the NN compared to MC models.

For more details, check out our paper! This work was done in collaboration with the Institute for Trans­port Plan­ning and Sys­tems at ETH Zurich.

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New paper in the Journal of Big Data: Where you go is who you are

Location data is extremely sensitive, revealing where we spend how much time, what we like to do in our free time, and how we spend our money. Our new study titled “Where you go is who you are – A study on machine learning based semantic privacy attacks” sets out to answer a critical question: Just how much “semantic information” – that is, detailed and potentially sensitive insights into personal behavior – can be extracted from someone’s location data using machine learning techniques, especially when this data might be inaccurate or incomplete?

To address this question, we conducted a study using a dataset of location visits from a social network, Foursquare, where the category of the location (e.g., “dining” / “sports” / “nightlife”) is known. We simulate the following scenario: An attacker only knows the geographic coordinates and time of a location visit, and trains a machine learning model to infer the place category. Crucially, the geographic coordiates can be imprecise due to GPS inaccuracies or protection measures. This is simulated by obfuscating the true geographic coordinates with varying radius.

Our experiments reveal a significant privacy risk even with imprecise location data. Although the accuracy of the attacker decreases exponentially with the obfuscation radius, there remains a high privacy loss even if the coordinates are perturbed by up to 200m. A certain risk remains from the temporal information alone. These findings highlight the privacy risks associated with the growing databases of tracking and spatial context data, urging policy-makers towards stricter regulations.

Check out our paper and source code!

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New paper published in the Journal of Transport Geography – Car sharing demand prediction

Our paper titled “Spatially-aware station based car-sharing demand prediction” is now published open-access in the Journal of Transport Geography!

In this paper, we analyze long-term station-based car-sharing demand (i.e., the monthly number of reservations per station), and fit local and global models to the demand. Spatially-aware models and methods for interpretability improved our understanding of the effect of different features in varying locations. Our models can assist in planning new car sharing stations, an important avenue towards more sustainable transportation.

This work is part of the Vehicle-to-grid for Car Sharing project.

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New paper in Transportation Research Part D – Time flexibility of car sharing users

Our paper titled “Vehicle-to-grid and car sharing: Willingness for flexibility in reservation times in Switzerland” presents the results of a stated preference survey conducted with 777 Mobility car sharing users in Switzerland. This study was part of our Vehicle-to-grid for Car Sharing project, funded by the Swiss Federal Office of Energy, where we aim to analyze the potential of integrating V2G in car sharing services. Understanding the time flexibility of car sharing users is crucial for designing dynamic pricing strategies, for example with the goal to incentivize users to shift their reservation times and thereby to increase the flexibility for V2G.
We found the value of time to be 31CHF/h on average, where older adults, lower income groups and employed adults tend to have lower flexibility. For more details, see our paper published in Transportation Research Part D.

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New IJGIS Paper is online – using context data to improve traffic forecasting

Our new paper entitled “Incorporating multimodal context information into traffic speed forecasting through graph deep learning” is now online at IJGIS.

In this work, we propose a multimodal context-based graph convolutional neural network (MCGCN) model to fuse context data into traffic speed prediction, including spatial and temporal contexts. The proposed model comprises three modules, i.e., (a) hierarchical spatial embedding to learn spatial representations by organizing spatial contexts from different dimensions, (b) multivariate temporal modeling to learn temporal representations by capturing dependencies of multivariate temporal contexts and (c) attention-based multimodal fusion to integrate traffic speed with the spatial and temporal context representations for multi-step speed prediction. We conduct extensive experiments in Singapore. Compared to the baseline model (STGCN), our results demonstrate the importance of multimodal contexts with the mean-absolute-error improvement of 0.29 km/h, 0.45 km/h and 0.89 km/h in 30-min, 60-min and 120-min speed prediction, respectively. We also explore how different contexts affect traffic speed forecasting, providing references for stakeholders to understand the relationship between context information and transportation systems. Check out the open-access paper online!

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Paper published in the Journal of LBS

Our paper titled “Influence of tracking duration on the privacy of individual mobility graphs” was published by the Journal of Location Based Services! In this work, we use a GPS tracking dataset and analyze how the tracking duration affects the risk for users to be re-identified; i.e., by matching to previously stored tracking data. It is well known that the tracking data of a user is quite unique and can be matched to stored data easily; however, we study the risk of a representation of tracking data that is already privatised, namely so-called location graphs. Location graphs do not reveal the geocoordinates or time stamps of the places that a user visited, but just the topology of the mobility behaviour. Nevertheless, users can be re-identified with a top-1 accuracy of up to 20%, and the re-identification risk strongly depends on the tracking duration of the user, as well as the duration of the stored data (pool), as shown in the figure below. Check out our paper and code for more information!

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Welcome Ayda Grisiute to MIE Lab!

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!

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The Open Digital Twin Platform Project Kick-Off Event

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.