128 citations · 323 across the 13 of their papers we have counts for
16 papers
Unsupervised Urban Land Use Mapping with Street View Contrastive Clustering and a Geographical Prior
Lin Che, Yizi Chen, Tanhua Jin +3
Urban land use classification and mapping are critical for urban planning, resource management, and environmental monitoring. Existing remote sensing techniques often lack precisio…
A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks
Ye Hong, Yanan Xin, Simon Dirmeier +2
Deep neural networks are increasingly utilized in mobility prediction tasks, yet their intricate internal workings pose challenges for interpretability, especially in comprehending…
Vehicle-to-grid for car sharing -- A simulation study for 2030
Nina Wiedemann, Yanan Xin, Vasco Medici +3
The proliferation of car sharing services in recent years presents a promising avenue for advancing sustainable transportation. Beyond merely reducing car ownership rates, these sy…
Where you go is who you are -- A study on machine learning based semantic privacy attacks
Nina Wiedemann, Ourania Kounadi, Martin Raubal +1
Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data. Location data i…
Uncertainty Quantification for Image-based Traffic Prediction across Cities
Alexander Timans, Nina Wiedemann, Nishant Kumar +2
Despite the strong predictive performance of deep learning models for traffic prediction, their widespread deployment in real-world intelligent transportation systems has been rest…
Evaluating geospatial context information for travel mode detection
Ye Hong, Emanuel Stüdeli, Martin Raubal
Detecting travel modes from global navigation satellite system (GNSS) trajectories is essential for understanding individual travel behavior and a prerequisite for achieving sustai…
Spatially-aware station based car-sharing demand prediction
Dominik J. Mühlematter, Nina Wiedemann, Yanan Xin +1
In recent years, car-sharing services have emerged as viable alternatives to private individual mobility, promising more sustainable and resource-efficient, but still comfortable t…
Context-aware multi-head self-attentional neural network model for next location prediction
Ye Hong, Yatao Zhang, Konrad Schindler +1
Accurate activity location prediction is a crucial component of many mobility applications and is particularly required to develop personalized, sustainable transportation systems.…
How do you go where? Improving next location prediction by learning travel mode information using transformers
Ye Hong, Henry Martin, Martin Raubal
Predicting the next visited location of an individual is a key problem in human mobility analysis, as it is required for the personalization and optimization of sustainable transpo…
Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis
Yanan Xin, Natasa Tagasovska, Fernando Perez-Cruz +1
Artificial intelligence (AI) is revolutionizing many areas of our lives, leading a new era of technological advancement. Particularly, the transportation sector would benefit from…
National-scale bi-directional EV fleet control for ancillary service provision
Lorenzo Nespoli, Nina Wiedemann, Esra Suel +3
Deploying real-time control on large-scale fleets of electric vehicles (EVs) is becoming pivotal as the share of EVs over internal combustion engine vehicles increases. In this pap…
Trackintel: An open-source Python library for human mobility analysis
Henry Martin, Ye Hong, Nina Wiedemann +2
Over the past decade, scientific studies have used the growing availability of large tracking datasets to enhance our understanding of human mobility behavior. However, so far data…
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
Christian Eichenberger, Moritz Neun, Henry Martin +34
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggrega…
Traffic Forecasting on Traffic Moving Snippets
Nina Wiedemann, Martin Raubal
Advances in traffic forecasting technology can greatly impact urban mobility. In the traffic4cast competition, the task of short-term traffic prediction is tackled in unprecedented…
Applications of deep learning in traffic congestion detection, prediction and alleviation: A survey
Nishant Kumar, Martin Raubal
Detecting, predicting, and alleviating traffic congestion are targeted at improving the level of service of the transportation network. With increasing access to larger datasets of…
Improving Interaction with Virtual Globes through Spatial Thinking: Helping Users Ask "Why?"
J. Schöning, B. Hecht, M. Raubal +3
Virtual globes have progressed from little-known technology to broadly popular software in a mere few years. We investigated this phenomenon through a survey and discovered that, w…