Martin Raubal

Professor

Swiss Federal Institute of Technology in Zurich

Switzerland

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About

Professor Martin Raubal is a faculty member at the Swiss Federal Institute of Technology in Zurich, Switzerland. His research spans various areas including deep learning for traffic forecasting, augmented reality in aviation, and urban planning. Recent publications reflect his interdisciplinary focus, exploring topics such as wayfinding processes, eye-tracking patterns, and the impact of urban infrastructure on electric vehicle charging. Professor Raubal's work contributes significantly to understanding and improving urban resilience and transportation systems.

Articles (23)

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Collaborators (5)

Sidi Wu

ETH Zurich

SWITZERLAND

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Esra Suel

Lecturer in City Modelling

University College London

UNITED KINGDOM

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Ioannis Giannopoulos

TU Wien

AUSTRIA

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Song Gao

Associate Professor

University of Wisconsin-Madison

UNITED STATES

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Victor Schinazi

Bond University

AUSTRALIA

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Martin Raubal

Research Interests

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About

Articles (23)

Counterfactual explanations for deep learning-based traffic forecasting

Context-Sensitive Augmented Reality Assistance in the Cockpit

Quantifying the impacts of non-recurrent congestion on workplace EV charging infrastructures

Decoding wayfinding: analyzing wayfinding processes in the outdoor environment

Leveraging Temporal Gaze Patterns for Intelligent Segmentation in Mobile Eye-Tracking

Context-Aware Knowledge Graph Framework for Traffic Speed Forecasting Using Graph Neural Network

2D versus 3D aviation weather visualisations

Bike network planning in limited urban space

Leveraging context-adjusted nighttime light data for socioeconomic explanations of global urban resilience

VResin: Externalizing spatial memory into 3D sketch maps

Vehicle-to-grid for car sharing - A simulation study for 2030

The influence of uncertainty visualization on cognitive load in a safety- and time-critical decision-making task

Estimating Perceived Mental Workload From Eye-Tracking Data Based on Benign Anisocoria

Incorporating multimodal context information into traffic speed forecasting through graph deep learning

Context-aware multi-head self-attentional neural network model for next location prediction

FlyBrate: Evaluating Vibrotactile Cues for Simulated Flight

A semantic web approach to land use regulations in urban planning: The OntoZoning ontology of zones, land uses and programmes for Singapore

The effect of flight phase on electrodermal activity and gaze behavior: A simulator study

Unobtrusive interaction: a systematic literature review and expert survey

Conserved quantities in human mobility: From locations to trips

Street‐level traffic flow and context sensing analysis through semantic integration of multisource geospatial data

Using rooftop photovoltaic generation to cover individual electric vehicle demand—A detailed case study

Applications of deep learning in congestion detection, prediction and alleviation: A survey

Collaborators (5)

Sidi Wu

Esra Suel

Ioannis Giannopoulos

Song Gao

Victor Schinazi

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