Current Projects

Data-driven mobility behavior analysis for e-bike-city feasibility assessment

Project start: 01.09.2022
Project lead: Prof. Dr. Martin Raubal
Internal Researchres: Nina Wiedemann, Henry Martin, Ayda Grisiute
Funding: ETHZ D-BAUG, external page Swiss Federal Office of Energy

Evaluating the feasibility of the e-​bike-city concept, one must account for various aspects of people’s mobility behavior, such as the current use of transport modes, determining the effect of the built environment on e-​bike ridership, and ways of substituting emission intensive modes by micro mobility. Furthermore, the impact of such micro mobility on accessibility, commute times and personal exposure levels must be investigated. The aim of this subproject is to utilize data-​driven spatio-​temporal analysis methods and machine learning to investigate these aspects and determine the generalized costs of a transition towards the e-​bike-city.

It will analyze current bike and e-​bike behavior using a data-​driven approach (GPS tracking data); and evaluate impact of environmental/urban/social factors on ridership to estimate geographically generalizable models. The subproject will assess integration with other shared and slow mobility modes (e.g., shared EVs, shared e-​scooters) and focus on the identifications of trips for which mode substitution towards bike / e-​bike is feasible and calculate resulting reduction in CO2 emissions and risk exposure. Finally, it will develop a fast GIS-​based tool for the calculation of different accessibility measures for uni-​ and multi-​modal chains.

Eyes4ICU: Gaze-​Based Interaction and Location-​Based Service

Project start: 01.09.2022
Project lead: Dr. Peter Kiefer
Internal Researchres: NN
Funding: external page State Secretariat for Education, Research and Innovation


The external page geoGAZElab is participating in the European project “Eyes for Interaction, Communication, and Understanding (Eyes4ICU)”.
external page Eyes4ICU is a Doctoral Network in the scope of the EU Marie Skłodowska-​Curie Actions. We are participating as an Associated Partner, receiving our funding from the Swiss State Secretariat for Education, Research and Innovation.

Eyes4ICU explores novel forms of gaze-​based interaction that rely on current psychological theories and findings, computational modelling, as well as expertise in highly promising application domains. In this context, the geoGAZElab is investigating gaze-​based interaction with location-​based services, focusing particularly on Gaze-​supported Trip Recommendation and Gaze-​supported Travel Experience Logging.

Interpretable and Robust Machine Learning for Mobility Analysis

Project start: 01.11.2021
Project lead: Prof. Dr. Martin Raubal
Intenal researchers: Dr. Yanan Xin, Ye Hong
Funding: external page Hasler Stiftung

Artificial intelligence (AI) is revolutionizing many areas of our lives, leading a new era of technological advancement. Particularly, the transportation sector would benefit from the progress in AI and advance the development of intelligent transportation systems. Building intelligent transportation systems requires an intricate combination of artificial intelligence and mobility analysis. The past few years have seen rapid development in transportation applications using advanced deep neural networks. However, such deep neural networks are often difficult to interpret and lack robustness, which slows the deployment of these AI-powered algorithms in practice. To improve their usability in deployment, an increasing research effort has been devoted to developing interpretable and robust machine learning methods, among which the causal inference approach recently gained traction as it can provide interpretable and actionable information. However, most methods are developed for image or sequential data which cannot satisfy the specific requirements of mobility data analysis. These unique requirements have been intensively studied in the Geographic Information Science (GIScience) field but have not yet been well utilized in developing machine learning models. The goal of our project is to bring together the knowledge of GIScience and Machine Learning, advancing our understanding of how interpretable and robust machine learning methods can be tailored to mobility analysis with the support of causal inference. The outcome of this research will deepen our understanding of how to integrate AI technologies and GIScience for mobility analysis, making AI in the transportation sector more interpretable and reliable. Ultimately, we aim to facilitate the deployment of AI in intelligent transportation systems and build a safer, more efficient, and more sustainable transportation system in the future.

The project is conducted in collaboration with the external page Swiss Data Science Center.  

3D Sketch Maps

Project start: 01.11.2021
Project lead: Prof. Dr. Martin Raubal
Intenal researchers: t.b.a.
Funding: Swiss National Science Foundation (SNF)

The 3D Sketch Maps project, funded by the Swiss National Science Foundation in the scope of the Sinergia funding program, investigates 3D sketch maps from a theoretical, empirical, cognitive, as well as tool-related perspective, with a particular focus on Extended Reality (XR) technologies. Sketch mapping is an established research method in fields that study human spatial decision-making and information processing, such as navigation and wayfinding. Although space is naturally three-dimensional (3D), contemporary research has focused on assessing individuals’ spatial knowledge with two-dimensional (2D) sketches. For many domains though, such as aviation or the cognition of complex multilevel buildings, it is essential to study people’s 3D understanding of space, which is not possible with the current 2D methods.

The 4-year project will be carried out jointly by the Chair of Geoinformation Engineering, the Chair of Cognitive Science at ETH Zurich (Prof. Dr. Christoph Hölscher), and the Spatial Intelligence Lab at University of Münster (external page Prof. Dr. Angela Schwering).

V2G4CarSharing - Vehicle to grid for Car Sharing

Project start: 01.10.2021
Project lead: Prof. Dr. Martin Raubal
Internal researchers: Dr. Yanan Xin, Nina Wiedemann
Funding: SFOE (Swiss Federal Office of Energy)

Car-sharing and Vehicle-to-Grid (V2G) are promising innovations to decarbonize the transport sector. Integrating car-sharing and V2G offers new opportunities to accelerate the market penetration of individual innovation due to their complementary roles in increasing the asset use of electric vehicles (EVs). However, there are still many unanswered questions in integrating car-sharing and V2G, such as how to optimize the charging/discharging schedules of shared EVs given the flexibility of bookings? How does the future penetration rate of shared EVs influence the feasibility and benefits of coupling V2G? How can a dynamic pricing strategy help with the integration? We address these questions by developing and evaluating optimal strategies to integrate car-sharing and V2G using three different case studies. This project will enrich our understanding of the feasibility and benefits of combining car-sharing and V2G, and provide guidance to address these challenges in practice.

The project is carried out together with the partners external page HivePower and external page Mobility